1. Introduction

IBM Planning Analytics latest enhancements IBM

Mastering MDX in IBM Planning Analytics Workspace

15 July 2026, 10:46:44 pm AEST

9 min

Part 1: Essential Functions Every TM1 Developer Should Kno

MultiDimensional Expressions (MDX) is one of the most powerful features available in IBM Planning Analytics Workspace (PAW). While many TM1 developers rely on static subsets, mastering MDX opens the door to dynamic reports, intelligent dashboards, and significantly reduced maintenance

In this first article of the series, we'll explore three fundamental MDX functions that every TM1 developer should understand:

TopCount()
Filter()
Descendants()

These functions form the building blocks of many real-world PAW applications.

Why Learn MDX?

Static subsets require manual maintenance and often become outdated. MDX allows subsets to adapt automatically to changing data and hierarchies.

With MDX, you can:

Create dynamic reports.
Build interactive dashboards.
Automatically rank and filter data.
Simplify maintenance.
Deliver more flexible analytics to business users.

Let's look at three commonly used functions.

1. TopCount() – Find Your Top Performers

The TopCount() function returns the top members from a set based on a specified measure.

Syntax

Example: Top 10 Products by Revenue

What This Does

Evaluates all products.
Sorts them based on Revenue.
Returns the top ten products.

Typical Use Cases

Top 10 customers by sales.
Highest-cost departments.
Top-performing regions.
Best-selling products.

Dashboard Example

A PAW dashboard displaying:

Top 10 Products by Revenue

Since the subset is dynamic, rankings update automatically whenever the data changes.

2. Filter() – Show Only What Matters

The Filter() function allows you to return members that satisfy a specific condition.

Syntax

Example: Customers with Revenue Above $1 Million

What This Does

Evaluates every customer.
Keeps only customers whose revenue exceeds $1 million.
Excludes all others

Common Business Applications

Budget Variance Reporting

Show only cost centers exceeding budget:

Active Employees

Display only employees with a headcount value greater than zero:

Product Profitability

Show products with positive margins:

Why Use Filter?

Instead of maintaining manual subsets every month, your reports automatically update based on business rules.

3. Descendants() – Navigate Hierarchies Dynamically

TM1 dimensions are hierarchical by nature. The Descendants() function retrieves all child members below a specified parent.

Syntax

Example

Suppose the Region hierarchy looks like this:

Using:

returns:

Common Use Cases

Regional Reporting

Display all locations under North America.

Cost Centre Rollups

Retrieve all departments under Finance.

Organizational Structures

Expand reporting relationships automatically.

Product Hierarchies

Display all SKUs belonging to a product family.

Benefits

When new members are added to the hierarchy, they are automatically included without requiring changes to the subset.

Combining Functions

MDX functions become especially powerful when combined.

Example: Top 10 Profitable Products

What Happens?

Step 1:

Filter removes products with negative profit.

Step 2:

TopCount selects the ten most profitable products.

This type of expression is commonly used in executive dashboards.

Performance Considerations

Prefer Dynamic subsets

Dynamic subsets reduce manual maintenance and improve flexibility.

Use Filter Carefully

Filtering very large dimensions can impact performance. Consider limiting the initial set whenever possible.

TopCount Is More Efficient Than Order + Head

Instead of:

Use:

TopCount is cleaner and generally performs better.

Test Expressions in the PAW Set Editor

The Set Editor provides a quick way to validate and troubleshoot MDX expressions before deploying them into books and dashboards.

Conclusion

MDX is one of the most valuable skills a TM1 developer can master. Functions such as TopCount(), Filter(), and Descendants() enable dynamic reporting and significantly reduce the effort required to maintain subsets and dashboards.

By incorporating these functions into your Planning Analytics applications, you can deliver smarter, more responsive solutions to your users.

In Part 2, we'll explore time intelligence functions, including:

ParallelPeriod()

PeriodsToDate()

YTD calculations

Rolling periods

Year-over-Year analysis

These functions are essential for financial reporting and variance analysis.

Are you using MDX extensively in your Planning Analytics applications? Which function do you find most useful? Share your thoughts in the comments.

Business Analytics Analytics IBM

The CFO's Approach to Agentic AI in Finance

6 July 2026, 9:45:01 am AEST

9 min

From experimentation to enterprise value — how finance leaders can adopt autonomous AI without losing control.

For the last two years, most finance teams have used AI to draft, summarise, and answer. Agentic AI changes the question entirely. Instead of asking "What can AI tell me?", the CFO now has to ask "What should AI be allowed to do?"

That single shift — from AI as an assistant to AI as an actor — is the most consequential change to land on the finance function in a decade. And it lands squarely on the CFO's desk, because the CFO owns both sides of the equation: the upside of speed and capacity, and the guardrails of control, accuracy, and audit.

Here is a practical way to think about getting in.

What "agentic" actually means for finance

Generative AI produces content. You ask, it responds, you decide what to do next. Agentic AI is different: an agent can take a goal, break it into steps, pull data from your systems, take actions, and complete a multi-step workflow with limited supervision.

In finance terms, that is the difference between an AI that drafts a variance commentary when you ask, and an agent that notices the variance during close, gathers the supporting detail, drafts the explanation, routes it for review, and flags the two items that need a human decision.

The technology is genuinely capable now. The risk is that finance leaders either dismiss it as hype or rush in without the controls the function demands. The right path sits between those two.

The mindset shift the CFO has to make

For years, finance evaluated automation through a cost-and-efficiency lens: how many hours can we save? Agentic AI deserves a wider frame. The real prize is capacity and capability — releasing skilled people from low-judgment work so they can spend time on analysis, scenario planning, and partnering with the business.

But capability comes with a new responsibility. When an agent acts inside your ERP, your planning system, or your procurement workflow, it inherits the same scrutiny any process would face: Is it controlled? Is it auditable? Can we explain what it did and why? The CFO is no longer just the sponsor of an AI project. They are the architect of trust around it.

A practical approach: how to get in without getting burned

The CFOs who are succeeding aren't the ones who moved fastest. They are the ones who moved deliberately. A workable sequence looks like this:

1. Start with the value, not the technology. Pick processes that are high-volume, rules-bound, and high-friction — month-end close tasks, intercompany reconciliations, invoice matching, procure-to-pay routing, first-draft FP&A commentary. These give agents room to deliver measurable wins and a clear baseline to measure against.

2. Get the data house in order first. An agent is only as good as the data it reads and the system of record it writes to. Fragmented spreadsheets and undocumented logic don't become trustworthy just because an AI now reads them. A clean, governed financial data layer is the precondition, not an afterthought.

3. Design governance from day one. Decide upfront where a human must stay in the loop, what an agent is allowed to action autonomously, and what always requires sign-off. Build in audit trails, segregation of duties, and explainability before the first agent goes live — not after the first surprise.

4. Pilot narrow, measure hard. Run a contained pilot with explicit metrics: cycle time, error rate, exceptions handled, hours released. Resist the temptation to declare victory on anecdote. The numbers are what move agentic AI from an experiment to a board-level capability.

5. Build the operating model. Someone has to own the agents — monitor their performance, review their decisions, retrain them, and retire them when they drift. Treat them as part of the team's operating model, with clear ownership, not as a one-off tool that runs unattended.

6. Scale with controls, not without them. Once a use case is proven and governed, extend it. Each new agent should inherit the same controls, the same monitoring, and the same accountability as the first.

Where the early wins are

If you are looking for the first places agentic AI earns its keep in finance, the most reliable candidates are:

Record-to-report: reconciliations, journal preparation, close-task orchestration, and first-draft commentary.
FP&A: variance detection and explanation, data gathering for forecasts, and surfacing the anomalies that deserve a human's attention.
Procure-to-pay: purchase-requisition-to-purchase-order routing, vendor query handling, and invoice matching.
Order-to-cash: collections follow-ups, dispute triage, and cash application.
Compliance and controls: continuous monitoring, exception flagging, and audit-evidence assembly.

These are deliberately unglamorous. That is the point. Proving value on well-bounded, high-volume work builds the credibility — and the control patterns — you need before you let agents anywhere near judgment-heavy territory.

The risks the CFO must own

A balanced approach means naming the risks plainly:

Accuracy and hallucination. An agent that confidently produces a wrong number is worse than no agent. Validation and human review on material outputs are non-negotiable.
Control failures. Autonomy without segregation of duties is a control gap waiting to be found by an auditor.
Over-automation. Not every process should be handed over. Some decisions exist precisely because they require human judgment.
Data privacy and security. Agents touch sensitive financial data. Where it goes, who can see it, and how it is retained all need answers.
Change management. The technology is rarely the hard part. Helping experienced finance professionals trust, supervise, and work alongside agents is where most programmes succeed or stall.

The CFO as the architect of trust

Agentic AI will reshape how finance work gets done — that much is no longer in question. What is still being decided, in every organisation, is whether it gets done well. That outcome depends less on the model and more on the leadership around it.

The CFOs who get this right won't be the loudest adopters. They will be the ones who treated agentic AI the way they treat everything else in finance: with ambition for the value, and discipline about the control. Start with a real problem, govern it properly, measure it honestly, and scale what works.

Agentic AI doesn't reduce the CFO's role. It elevates it — from steward of the numbers to architect of the trusted, intelligent finance function that the rest of the business is about to depend on.

Octane Software Solutions partners with finance leaders to design and deliver governed, value-led AI and EPM transformations. If you're shaping your own approach to agentic AI in finance, we'd be glad to compare notes.

AI IBM

The Cost of Siloed Planning: Why Integrated Planning Matters

12 June 2026, 1:17:38 pm AEST

7 min

The Cost of Siloed Planning: Why Integrated Planning Matters

The case for integrated planning — and why AI makes the absence of it more dangerous, not less.

There's a story I use when I'm talking to CFOs about integrated planning. It's not a hypothetical. It's a company called Cochlear — a publicly listed medical device business. In a single trading day, the company lost approximately 40% of its market value recently.

siloed_data_market_crash_artwork

The reason wasn't fraud. It wasn't a product failure. It was a disconnect. Sales were declining. Inventory was building. The two facts were sitting in different systems, owned by different teams, measured against different KPIs. Nobody connected the dots in time. The market did.

When I ask CFO audiences how many of them are operating with the same structural vulnerability, the room gets uncomfortable. Because the honest answer, for most mid-to-large organisations, is: yes.

The Silent Risk of Siloed Planning

Most finance functions know they have a data fragmentation problem. What they underestimate is the risk that fragmentation creates — particularly now that AI is being layered on top of it.

Here's the dynamic I see repeatedly: an organisation invests in AI tools to improve forecasting, automate reporting, or generate commentary. The tools are good. But the data feeding them is fragmented. Sales live in one system. Inventory in another. Headcount in a spreadsheet. Finance in the ERP. There's no single connected model that shows the cause-and-effect relationship between these inputs.

When AI is applied to that fragmented foundation, it doesn't fix the fragmentation. It produces confident, well-articulated outputs that are built on incomplete information. That's arguably more dangerous than a spreadsheet, because it has the appearance of rigour.

If you don't have integrated planning — if you don't understand the cause and effect between your departments — you're not ready to layer AI on top of it. That's your baseline.

What Integrated AI Planning Actually Looks Like

I want to give you a concrete picture, because 'integrated planning' is a term that gets used loosely. Let me describe what we built for one of the investment banks in our client portfolio — a globally operating institution with more than 800 legal entities and over 1,000 active users of the planning application.

When we started the engagement, their budget cycle took two months. By the time it was complete, it was already out of date. Rolling forecasts — the aspiration — were impossible at that cadence.

After implementing a properly integrated planning environment, with AI infused across the workflow, they can now complete a rolling forecast in four to six days. More importantly, the system responds to real-world events. When macro conditions shift — supply chain disruption, geopolitical instability, commodity price movement — the model recalibrates. Frontline decision-makers get live insight, not a stale plan from last quarter.

75%+

Reduction in forecast cycle time — from 2 months to 4-6 days
800+ global entities, 1,000+ users

The Four Things AI Planning Actually Delivers

When integrated planning is working properly, with AI layered appropriately, it delivers four things that individually sound incremental but together are genuinely transformational:

One version of the truth. Sales, inventory, cash flow, and headcount in a single connected model, updated in near real time. This alone eliminates the majority of the reconciliation work that consumes analyst capacity.
A forecast that responds. Market events auto-trigger scenario recalculation. You're not waiting for the next planning cycle to understand the impact of a supply chain disruption. You know within hours.
AI-generated commentary. Variance explanations and board narratives written by AI, reviewed and approved by your team. The AI has access to the transaction history, the drivers, the period comparisons — and it generates commentary that is, frankly, more consistent and complete than what most analysts produce under time pressure.
Early warning signals. Anomaly detection that surfaces the Bioventus-style blind spots before they become headlines. The inventory building while sales decline scenario — that gets flagged, automatically, before the market figures it out.

Why the Spreadsheet Will Not Save You

I want to be clear about something: I'm not dismissing the sophistication of finance teams who have built complex Excel environments. Many of them are genuinely impressive pieces of engineering. But they have a structural limitation that AI cannot fix: they are disconnected by design.

A spreadsheet that sits in finance doesn't know what's happening in supply chain. An Excel model in the sales team doesn't feed into the headcount plan. The connections are manual, maintained by people, and dependent on someone remembering to update something. When that person is on leave, or when they leave the business, the connections break.

Integrated planning replaces those manual connections with a live data model. AI then operates on top of that model — not on top of a series of disconnected files.

Where to Start

If you're reading this and recognising your own organisation in the description above, the path forward is cleaner than it might seem. In our experience, the most effective starting point is not a full transformation program — it's a targeted proof of concept that connects two or three currently disconnected planning domains and demonstrates what a live, integrated model looks like.

Once the board and leadership team can see a rolling forecast that updates automatically when assumptions change, the conversation about broader transformation becomes significantly easier.

The Cochlear story is an extreme example. But the structural vulnerability it illustrates is common. And with AI amplifying the outputs of whatever data foundation you're sitting on, the cost of not addressing it is rising.

In the next blog in this series, I'll address something most CFOs are completely unaware of: the cost of AI tokens — and why it's about to become a CFO-level line item.

Amendra Pratap is the Founder and Managing Director of Octane Solutions, an IBM Gold Partner specialising in AI-powered finance transformation across Australia, New Zealand, and the Pacific.

The Enterprise Knowledge Crisis, And Why Agentic AI Is the Only Answer That Scales

4 June 2026, 2:00:01 pm AEST

15 min

The Problem Every C-Suite Recognises but Few Have Solved

Your organisation is not short on information.

After a decade of digital transformation, most enterprises have built expansive repositories: policies, contracts, procedures, compliance frameworks, knowledge bases, operational runbooks, HR documentation, supplier records, regulatory filings. These assets represent years of institutional knowledge and millions of dollars of accumulated investment.

And yet, when an employee needs an answer, a real, accurate, contextual answer, they still open a search bar, scroll through folders, send an email, or wait for a reply.

The information exists. The problem is access, context, and trust.

McKinsey research estimates that knowledge workers spend an average of 1.8 hours every day searching for and gathering information. Across an enterprise of 10,000 employees, that is the equivalent of over 3,000 people doing nothing but searching, every single day.

This is not a content management failure. It is a knowledge operationalisation failure. And it is costing organisations far more than they realise.

The questions being asked every day inside your organisation reveal the gap plainly:

What policy applies to this situation?
Who approves this request, and at what threshold?
What compliance obligations apply to this transaction?
Which supplier holds the preferred contract?
What is the current version of this document?
Has this issue been resolved elsewhere in the business?

The answers exist, buried in documents that were designed to be stored, not used.

"Most enterprise knowledge is archived. Very little of it is operationalised. This is the gap that Agentic AI is uniquely built to close."

From Content Management to Knowledge Intelligence: A Strategic Shift

Traditional enterprise platforms were designed to store information. Agentic AI is designed to understand it.

This distinction matters enormously at the C-suite level. The shift is not incremental, it is architectural.

Instead of asking employees to navigate folder structures, keyword searches, and disconnected repositories, an intelligent AI agent allows them to ask a question in plain language and receive an accurate, contextual, source-cited answer in seconds. The experience moves from:

"Search and hope you find the right document."

to

"Ask and receive a trusted, actionable answer."

This is not another chat interface or search upgrade. It is a fundamental change in how enterprise knowledge becomes a business asset, one that informs decisions, reduces risk, accelerates operations, and scales without adding headcount.

At Octane, we have helped organisations make exactly this transition. We build intelligent AI agents that transform enterprise content into a conversational, governed, and actionable knowledge layer, one that answers questions, interprets policies, generates insights, and orchestrates business processes at scale.

What Separates Agentic AI from Everything That Came Before

Most organisations have experimented with Generative AI. Many have built chatbots, piloted copilots, or deployed search enhancements. Most of these initiatives delivered limited value and stalled at the proof-of-concept stage.

The reason is simple: they were designed to generate content, not to reason about it.

Agentic AI operates at a fundamentally different level. The shift from generative tools to enterprise-grade agents requires architectural foresight, business alignment, and strict governance, not just better models.

An enterprise AI agent does not simply retrieve a document. It:

Understands the intent behind a question
Searches across the enterprise knowledge corpus
Interprets policies, procedures, and obligations in context
Evaluates the user's role, location, and permissions
Applies relevant business rules and thresholds
Generates a clear, precise recommendation
Cites authoritative sources for every claim
Executes actions or triggers downstream workflows
Escalates to human reviewers when confidence is insufficient
Learns and improves from each interaction

The result is not another search tool. It is a trusted digital colleague, one available to every employee, across every department, at every hour.

The Architecture Behind Enterprise-Grade Accuracy

Here is where strategy meets engineering, and where most AI initiatives fail.

Building a basic AI assistant is straightforward. Building one that consistently delivers accurate, auditable, and reliable answers at production scale is substantially harder.

Any technical research will show that, the gap between a working prototype and a trusted enterprise platform spans multiple engineering disciplines: document preparation, intelligent chunking, hybrid retrieval strategies, domain alignment, continuous evaluation, and governance controls.

Most enterprise knowledge was never created with machine readability in mind. PDFs contain tables that break during extraction. Scanned documents carry OCR errors. Policies are written in deliberately broad language. PowerPoint decks rely on visual flow rather than explanatory text. Multiple conflicting versions of the same document often coexist.

A simple AI pipeline ingests all of this without discrimination. A production-grade Agentic AI system cannot afford to.

The difference between a 60% accuracy system and a 95% accuracy system, the threshold at which enterprise trust is established, lies in five areas:

Data Preparation: Normalised formatting, semantic chunking, rich metadata tagging (ownership, jurisdiction, effective date), and active deduplication of conflicting content.

Intelligent Retrieval: Hybrid search combining vector similarity with keyword matching. Dynamic chunk sizing. Re-ranking models calibrated to your domain. Query rewriting and expansion. Retrieval defines the boundaries of truth; improving it delivers larger gains than upgrading the model itself.

Domain Alignment: General-purpose language models are fluent but not domain experts. High-accuracy enterprise systems include domain-tuned models, precisely engineered system prompts, and explicit constraints that ground answers in verified organisational sources.

Continuous Evaluation: Accuracy is not achieved once. It requires ongoing measurement against real user queries, expert-validated ground truth, multi-dimensional response grading, and regression testing after every content or configuration change.

Governance by Design: In enterprise and regulated environments, accuracy alone is insufficient. Every response must be explainable, traceable, auditable, and governed. This is the bar that separates pilots from platforms.

The Octane Delivery Model: Powered by IBM watsonx

Octane delivers Agentic AI solutions built on IBM's enterprise-grade AI platform, watsonx Orchestrate, giving your organisation access to one of the most capable and governed AI ecosystems available today.

At the centre of this ecosystem is IBM's Agent Catalogue: a central hub that makes it easy to discover, deploy, and orchestrate specialised AI agents built by IBM and its global partner network. These agents are framework-agnostic, meaning they can be built with any modern development stack and integrate seamlessly with the watsonx Orchestrate environment.

Through IBM Agent Connect, Octane's specialised agents integrate directly into your watsonx environment, delivering pre-built capabilities focused on specific business domains, with the productivity enhancements and process simplifications your teams need from day one.

This architecture means your Agentic AI investment is not a bespoke, isolated prototype. It is built on an open, scalable platform backed by IBM's global enterprise infrastructure, security frameworks, and partner ecosystem, reducing time to value while maximising long-term extensibility.

Where Agentic AI Delivers Measurable Business Impact

Agentic AI is not a horisontal technology looking for a problem. It is a targeted capability that delivers measurable outcomes in specific, high-value enterprise domains.

Human Resources & Employee Experience

Employees spend significant time searching for HR information or waiting days for responses to routine questions. An intelligent HR agent answers instantly and consistently:

"What parental leave am I entitled to in my region?" "Can I carry over unused annual leave this year?" "What is the reimbursement limit for international travel?"

The agent interprets policies, applies regional requirements, and delivers cited responses, reducing HR support workload, accelerating onboarding, and improving employee experience at scale.

Governance, Risk & Compliance

Compliance creates value only when employees understand and follow it, a condition that is remarkably difficult to sustain across large organisations.

"Can I share this customer record with a third-party vendor?" "What approvals are required before signing this agreement?" "Which privacy obligations apply to this process?"

The agent reviews applicable policies, interprets requirements in context, and provides evidence-backed guidance, reducing violations, accelerating decisions, and creating a defensible audit trail.

Finance Operations

Finance teams operate across thousands of transactions, controls, and approval frameworks. Manual policy lookup is a bottleneck that compounds across every department.

"What approval is required for capital expenditure over $50,000?" "Which expense category should this purchase be assigned to?" "What is our current policy on FX hedging for subsidiary transactions?"

Immediate, accurate answers reduce approval delays, improve policy compliance, and strengthen financial governance.

Procurement & Vendor Intelligence

Procurement teams manage complex supplier ecosystems across contracts, SLAs, ESG obligations, and preferred vendor registers.

"Who is our preferred cloud infrastructure vendor in APAC?" "What service levels apply to this support contract?" "Which of our current suppliers are certified against our ESG requirements?"

The agent retrieves answers directly from contracts, supplier records, and procurement policies, accelerating cycles and improving compliance.

Customer Service & Contact Centre

Service agents lose significant time searching internal systems for product information, escalation paths, and policy guidance. Agentic AI delivers that knowledge in real time, reducing handling times, improving first-contact resolution, and lowering operational costs without increasing headcount.

Healthcare & Clinical Operations

Healthcare professionals require immediate access to clinical protocols, pre-operative requirements, and compliance obligations. Agentic AI delivers approved guidance instantly, improving patient safety and reducing administrative burden.

The Business Case: A Quantified Return

Organisations implementing enterprise Agentic AI knowledge platforms consistently achieve:

Business Outcome	Typical Impact
Employee search time reduction	4.5+ hours saved per employee per week
Support ticket deflection	20–40%
Support team productivity	14–15% improvement
Issue resolution speed	30–50% faster
Overall employee productivity	5%+ improvement
Policy compliance consistency	Measurable governance uplift
Decision speed	Significant reduction in knowledge latency

For a mid-size enterprise of 5,000 employees, 4.5 hours saved per employee per week represents approximately 22,500 hours of recovered productive capacity, every week.

The question is not whether the ROI justifies the investment. The question is how quickly you can move from assessment to deployment.

The Octane Approach: From Strategy to Scale

Most AI consulting firms help you define a strategy. Octane helps you execute one.

We have won multiple global awards for transforming businesses through Agentic AI, not through theoretical frameworks, but through delivered systems that operate in production environments across regulated industries including Financial Services, Healthcare, Government, Telecommunications, and Critical Infrastructure.

Our engagement model is structured to move organisations from curiosity to capability at pace.

AI Strategy & Opportunity Assessment

We identify where Agentic AI creates the highest measurable value in your specific organisation, grounded in your data, your processes, and your competitive position. Not generic use cases. Your use cases.

Agent Design & Architecture

We design agents that are grounded in your business processes, governance requirements, and operational realities. Every agent is built with explainability, auditability, and scalability as first-class requirements, not afterthoughts.

Enterprise Integration

We connect agents to your existing business systems: ERP, CRM, HRIS, document management, workflow platforms, and operational data sources. Agents that cannot operate within enterprise systems cannot scale.

Enablement & Knowledge Transfer

We do not gatekeep. Our enablement programs are designed to transfer knowledge, skills, and confidence to your team, so that your organisation does not just use AI, but owns it. From technical deep dives to executive briefings, every session is tailored to your team's goals and capability level.

Governance, Security & Trust

Every solution Octane delivers is secure, auditable, explainable, and enterprise-ready. We build governance by design, not compliance as an afterthought. This is non-negotiable in regulated environments and increasingly expected in every sector.

Scale & Optimisation

We expand from a proven first use case into an enterprise-wide Agentic AI operating model, one that evolves as your business evolves.

What Your Organisation Could Build

Whether the priority is a single high-impact agent or an enterprise-wide knowledge platform, Octane has the capability, methodology, and platform partnerships to deliver it:

Ask HR, Employee policy and entitlement advisor
Ask Finance, Approvals, expense, and financial policy assistant
Corporate Policy Advisor, Governance and compliance intelligence layer
Procurement Assistant, Supplier, contract, and vendor intelligence
Compliance Advisor, Regulatory obligation and risk guidance
Legal Knowledge Agent, Contract interpretation and escalation
Service Desk Assistant, Internal IT and operations support
Enterprise Ask-Me-Anything Platform, Unified knowledge access across the organisation

The goal across every one of these is the same:

Transform information into action. Transform content into decisions. Transform knowledge into competitive advantage.

Three Ways to Engage Octane

We meet organisations wherever they are in their Agentic AI journey.

Discovery Workshop: A structured half-day or full-day session to map your highest-value AI opportunities, assess your knowledge estate, and define a prioritised roadmap. Designed for leadership teams ready to move from awareness to action.

Enterprise Readiness Assessment: A comprehensive evaluation of your data, governance, technology, and organisational readiness for Agentic AI at scale. Delivers a detailed implementation blueprint with defined business cases and ROI projections.

Agentic AI Implementation: End-to-end delivery of your first production-grade AI agent, from design and integration through deployment, evaluation, and enablement. Built on IBM watsonx Orchestrate. Designed to scale.

The Organisations That Move First Will Move Furthest

The competitive dynamics of Agentic AI are not symmetrical.

Organisations that deploy trusted, production-grade knowledge agents over the next 12 to 18 months will accrue structural advantages: faster decision-making, lower operational costs, stronger governance, and institutional knowledge of what works at scale in their industry.

Organisations that wait will face a widening gap, not just in technology, but in organisational capability and institutional confidence.

The question is no longer whether your organisation has the information it needs.

The question is whether your people can access it, trust it, and act on it, at the speed your business demands.

Octane builds the bridge.

Ready to Begin?

Discover how Octane can transform your enterprise knowledge, policies, procedures, and operational content into a governed, intelligent, AI-powered platform that delivers measurable value across the entire organisation.

Book a Discovery Workshop → Request an Enterprise Readiness Assessment → AI Implementation Roadmap by an Agentic AI Specialist

Octane is an award-winning Agentic AI consulting firm specialising in strategy, agent design, enterprise integration, and organisational enablement. Our solutions are built on IBM watsonx Orchestrate, one of the world's most capable and governed enterprise AI platforms.

AI IBM Planning Analytics latest enhancements IBM WatsonX Orchestrate agentic ai

Your Finance Team Is Still Running Planning Cycles on Email Threads. Here's What to Do Instead.

3 June 2026, 9:45:01 am AEST

11 min

A complete practitioner's guide to IBM Planning Analytics Workflow — how it works, how to build one from scratch, and what's coming next that will change the game entirely.

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WHAT YOU WILL TAKE AWAY

✓ Why IBM Planning Analytics Workflow exists — and what problem it solves for FP&A teams

✓ The difference between Plans and Applications — and when to use each

✓ A step-by-step playbook for building a production-grade custom workflow

✓ Every confirmed update and upcoming feature on the 2026 roadmap

Why Planning Cycles Break Without Workflow

Every quarter, finance teams across the world do the same thing: they send spreadsheets over email, chase submissions on Slack, lose track of who approved what, and scramble to consolidate versions that were never meant to live together.

It is not a people problem. It is an infrastructure problem. And IBM Planning Analytics was designed specifically to fix it.

67%

of FP&A time spent gathering data vs. actual analysis

3×

faster planning cycles with structured workflows

100%

audit trail on every submission and approval

IBM Planning Analytics — built on the high-performance TM1 in-memory engine — gives you a single governed platform where planning, workflows, approvals, and AI-powered forecasting all live together. The result: shorter cycles, cleaner data, and a finance team that can finally spend time on strategy.

"The best planning systems don't just store numbers — they orchestrate the people, processes, and decisions around them."

Plans vs. Applications — Know the Difference Before You Build

IBM gives you two containers inside Planning Analytics Workspace. Most teams conflate them. Understanding the distinction is the first unlock.

APPLICATIONS are organized landing pages. They group books, reports, and input forms into a structured navigation experience — great for adoption, not designed to enforce a process sequence.

PLANS are process containers. They enforce sequence, track contributors, manage approvals, and give administrators a real-time status dashboard — everything you need to run a governed planning cycle.

A Plan can hold reports, data entry forms, checklists, and announcements — all grouped into tasks with due dates, assigned contributors, and optional approval gates. Task dependencies ensure that a downstream step stays locked until the prerequisite is complete. Contributors can still work on non-dependent tasks in any order, giving them flexibility without sacrificing governance.

How to Build a Custom Workflow — Start to Finish

Here is the exact sequence a practitioner follows to build a production-grade custom workflow in IBM Planning Analytics Workspace:

01	Create the Plan from the Applications & Plans page Click New → Plan. Set a name, description, and cycle dates. Plans are accessible to PAW administrators, modelers, and analysts — no special IT access required to get started.
02	Build your task structure in Edit mode Add Tasks and Announcements. Each task holds assets — books, input forms, reports — and is assigned to specific contributors and approvers with individual due dates. Think of each task as one step in your planning cycle.
03	Configure task dependencies via the Workflow tab Set upstream dependencies so that a task only unlocks when the preceding one is submitted. This eliminates the classic problem of a manager approving data that hasn't been entered yet.
04	Wire in TurboIntegrator automation via Action Buttons Attach TI processes to Action Buttons. Configure them to trigger automatically on process completion, navigate users to the next task, or fire parallel processes simultaneously — all without a single manual handoff.
05	Assign contributors and activate notifications Add contributors by user or group. IBM handles email notification routing automatically. Every contributor sees their tasks, due dates, and status — no onboarding beyond a login.
06	Monitor from the Home dashboard The Overview tab gives administrators a live view of every task status across the entire cycle. The Data Accountability dimension shows exactly who owns which data — and when they gave it up.

Pro tip: Action Buttons in PAW now support dynamic multi-step workflows — configure a process to run and immediately navigate the user to the next task upon completion, eliminating manual handoffs entirely.

TurboIntegrator — The Automation Engine Inside Your Workflow

Action Buttons and Plans cover 80% of workflow needs. TurboIntegrator (TI) covers the remaining 20% — the complex, high-stakes automation that separates a good implementation from a great one.

Three trigger points every practitioner should master:

On action button click: Run multiple TI processes in parallel using different parameter sets. This alone can cut processing time by half on large datasets.
On user activity in Plans: Trigger a TI process automatically when a contributor interacts with a plan task — no more waiting for a manual sign-off before downstream steps can begin.
On ownership release (v3.1.5 — NEW): A TI process now fires automatically when a contributor releases ownership of a Data Accountability member. Use this to auto-lock submitted data, notify approvers, or kick off consolidation.

The process debugging experience has also matured significantly. You now get a diff view to review script changes before saving, a split-panel editor for comparing two sections of the same TI script side by side, and a dedicated log tab that keeps debugging isolated from the rest of your workspace.

"Workflow automation that previously required external tools or manual triggers can now be built entirely inside Planning Analytics — in an afternoon."

The Release Roadmap — Every Update That Matters

IBM has been shipping Planning Analytics updates at pace. Here is every workflow-relevant change, clearly tagged by status:


LIVE NOW	AI assistant embedded inside Plans (PAW 3.1.5) Contributors can now query the AI assistant without leaving their planning workflow — asking for guidance, pulling insights, or executing common tasks via natural language. No documentation hunt required.
LIVE NOW	Release-ownership workflow trigger (PAW 3.1.5) Auto-trigger a TI process the moment a planner releases ownership in the Data Accountability dimension — enabling automated lock, notify, and consolidate sequences that previously required external orchestration tools.
FEB 2025	Explain Cell & Process Explain AI The AI breaks down any cell value in plain English — whether it's a consolidation, a rule calculation, or a time-based roll-up. Bridges the gap between model logic and business users in seconds
FEB 2025	Parallel TI process execution Run multiple TI processes simultaneously from a single action button with different parameter sets — a practical win for teams managing complex, multi-entity planning cycles.
COMING SOON	watsonx Orchestrate integration for HR, Sales & Procurement IBM has confirmed that Planning Analytics will integrate with watsonx Orchestrate — extending agentic AI workflow automation to HR, Sales, and Procurement functions beyond Finance.
AI ROADMAP	Natural-language workflow triggers via intelligent agents Agentic AI will allow users to trigger planning tasks, run forecasts, and push data updates through plain-language commands — removing the last manual friction points from the planning cycle.
ROADMAP	User-level AI access controls Granular per-user AI assistant permissions are coming — giving administrators precise control over who can use AI features inside workflows, addressing governance concerns in regulated industries

IBM Planning Analytics has quietly become one of the most complete planning orchestration platforms on the market. The combination of governed workflow structures, TurboIntegrator automation, and now embedded AI assistance inside live planning cycles is a meaningful shift — not an incremental feature drop.

For FP&A leaders still managing planning cycles through email and spreadsheets, the gap between where you are and where you could be has never been wider. Or easier to close.

Before you go — one question for the community

What is the single biggest bottleneck in your current planning cycle?

"Drop your answer in the comments — I read every one."

#IBMPlanningAnalytics #FPandA #FinanceTransformation #PAW #TurboIntegrator #WorkflowAutomation #IBMwatsonx #TM1 #DigitalFinance

AI financial transformation IBM Planning Analytics latest enhancements

Enterprise AI for Australian Finance Teams: What Actually Works in 2026

2 June 2026, 1:11:14 pm AEST

14 min

If you are a CFO or finance director at an Australian mid-market company, you have spent the last 18 months watching every vendor on the planet tell you that AI will transform your function. You have sat through the demos. You have seen the slide decks. You have heard the promises.

And you have probably noticed that most of it does not address the two questions that keep you up at night:

Where does my data go?
Can I trust this thing to make decisions on my behalf: legally?

This guide is written from the perspective of a firm that has been doing enterprise finance technology in Australia for over a decade. We are based in Sydney. We work with Australian CFOs. We deal with Australian regulatory requirements every day. And we are going to tell you what we are actually seeing on the ground, not what the vendor keynotes are saying.

The Australian Context Is Different

Global AI conversation is dominated by US-centric advice. "Move fast." "Adopt GPT-4." "Agent everything." That is fine if you are a Series B startup in San Francisco with no regulatory exposure.

It is not fine if you are a finance director at an ASX-listed company, a mid-market manufacturer in Melbourne, or an accounting practice in Sydney managing client funds under strict professional obligations.

Three things make the Australian landscape genuinely different:

1. Data Sovereignty Is Not Optional

Over 80% of Australian enterprises now factor country-of-origin into vendor selection. For finance teams handling customer financial data, employee records, or health-adjacent information, the question is not whether to host data onshore: it is which provider can guarantee it.

This immediately disqualifies a significant number of AI tools and platforms that route data through US-based inference endpoints. If your "enterprise AI assistant" sends a prompt containing client financial data to an API server in Virginia, you have a data sovereignty problem. Whether or not, the vendor's marketing page says "enterprise-grade security."

The Australian Government's National Interest Framework sets expectations (not legally binding mandates, but strong signals) that digital infrastructure should prioritise sovereign, onshore data hosting. For finance, this is not abstract policy; it is the difference between a board that approves your AI initiative and one that shelves it.

2. The Privacy Act Changes Are Coming

On 10 December 2026, amendments to the Privacy Act take effect that require APP entities to disclose (in their privacy policies) whether they use automated decision-making technology that could significantly affect individuals.

If your finance function uses AI to:

Score credit applications
Flag fraud
Automate insurance assessments
Generate personalised financial recommendations
Draft communications that influence client outcomes

...you need to be able to explain what the system does, what data it uses, and whether a human is meaningfully involved in the decision.

This is not a theoretical future requirement. It is 8 months away. Finance teams that are deploying AI systems in 2026 without building in explainability, audit trails, and human-in-the-loop governance are creating compliance debt they will need to unwind before December.

3. The Talent Gap Is Acute

Australian enterprises do not have enough people who can bridge finance expertise and AI engineering. The "AI translator" (someone who understands both a consolidation hierarchy and a vector database) barely exists in the local market.

This is why most Australian mid-market companies cannot build AI capabilities in-house. They need a partner who understands both the technology and the financial domain. And "partner" does not mean a Big 4 firm handing you a strategy PDF and a $300K invoice. It means someone who can actually build the pipeline, test it against your data, and support it in production.

What Australian Finance Teams Are Actually Deploying

Based on what we are seeing across our client base and the broader Australian market, the deployments that are working fall into three categories:

1. Internal AI Assistants (RAG Architecture)

This is the most common use case and the one generating the most search demand from Australian CFOs right now.

The problem it solves: Your team spends hours every week searching through Xero guides, past email advice, internal policy documents, compliance manuals, and historical board packs. They know the answer exists somewhere; they just cannot find it fast enough.

What it looks like in practice: A secure internal assistant (accessible through a web interface or integrated into Teams) that can answer questions using only your organisation's internal data. "What was the depreciation policy we applied to the Melbourne warehouse in FY24?" or "What did the auditor flag in the Q2 management letter?

How it works (RAG pipeline):

[PROMPT FOR NANO BANANA PRO: 3D isometric infographic of a data pipeline. Left: a pile of documents and folders labeled "Internal Data." Center: a glowing processing unit labeled "Onshore Vector DB." Right: a clean user chat interface on a tablet. Arrows indicating flow from left to right. Soft studio lighting, high-tech aesthetic, color palette of professional blues, whites, and teals. Clean white background.]

Your internal data (policies, emails, reports, guides) is chunked into small passages and converted into mathematical representations (embeddings).
These embeddings are stored in a vector database: hosted onshore, within your infrastructure perimeter.
When a user asks a question, the system retrieves the most relevant passages from the vector database.
Those passages are assembled into a context window and sent to a language model for synthesis.
The model generates an answer grounded in your actual data, not the open internet.

Critical design decision: Where does the LLM inference happen? If you are using a cloud-hosted model (OpenAI, Anthropic, Google), your prompt (which now contains retrieved chunks of your internal data) leaves your infrastructure. For many Australian finance teams, this is a deal-breaker.

The alternative is running inference locally or through a sovereign cloud provider. IBM watsonx, for instance, can be deployed on Australian infrastructure, keeping the entire pipeline (embeddings, retrieval, and inference) within your data sovereignty boundary.

2. Automated Financial Workflows (Agentic AI)

This goes beyond Q&A. Agentic AI systems can execute multi-step workflows autonomously, but with human approval gates at critical decision points.

Practical examples we have built or seen deployed in Australian finance teams:

Automated reconciliation: An agent pulls bank feeds from Xero or SAP, matches them against internal ledger entries, flags discrepancies, and prepares a reconciliation summary for a human to review and approve.
Board pack drafting: An agent pulls financial results from your planning system (TM1), retrieves the prior period commentary template, generates a first draft of variance commentary, and routes it to the CFO for review.
Invoice processing: An agent ingests supplier invoices via email, extracts key fields (ABN, amount, due date, GL code), validates against PO records, and queues approved items for payment.

The key phrase is "human-in-the-loop." No Australian CFO we have worked with wants fully autonomous AI making ledger-impacting decisions. What they want is AI that does the first 80% of the work (the retrieval, the matching, the drafting) and then surfaces a clean decision for a human to approve.

3. AI-Enhanced Planning and Forecasting

This is where our deepest expertise sits. IBM Planning Analytics (TM1) now integrates with watsonx AI capabilities, including natural language query, automated anomaly detection, and predictive forecasting.

What this means in practice:

A finance analyst can ask the Planning Analytics Assistant: "Why did APAC revenue drop 12% in March?" and get a natural language explanation grounded in the actual cube data.
The system can flag outliers in forecast submissions before the FP&A team manually reviews 200 cost centre budgets.
Predictive models can extend historical trends into forward forecasts, giving the CFO a machine-generated baseline to challenge or refine.

The Architecture That Works for Australia

Based on our implementation experience, the architecture pattern that works for Australian finance teams keeps every component inside your data boundary.

[PROMPT FOR NANO BANANA PRO: 3D isometric conceptual diagram showing a secure, locked digital boundary box. Inside the box: stacked 3D layers representing "TM1 Engine," "Vector DB," and "Sovereign LLM Inference." A light blue, glass-like shield surrounds the entire stack, labeled "AU Sovereign Boundary." Professional, clean, and secure corporate tech style.]

Every component stays inside your data boundary. Embeddings are generated locally. The vector database is hosted onshore. LLM inference runs on sovereign infrastructure. Planning data stays in TM1. And the entire system maintains an audit trail for Privacy Act compliance.

What to Ask Your AI Vendor

If you are evaluating an AI consulting firm or platform for your Australian finance team, ask these questions:

Where does inference happen? If the vendor cannot tell you the physical location of the GPU running your prompts, that is a problem.
Can you guarantee data residency? Not "our servers are secure"—specifically, does the data stay in Australia throughout the entire pipeline?
How do you handle the December 2026 Privacy Act requirements? If they look confused, walk away.
Do you build the explainability layer, or do we? Audit trails, decision logs, and human-in-the-loop governance should be part of the architecture, not an afterthought.
What happens when the model hallucinates? Every LLM will occasionally generate incorrect information. What guardrails exist?
Have you built this for a finance team before? The difference between a general AI consulting firm and one with finance domain expertise is the difference between a prototype and a system that survives the first month-end close.

A Realistic Timeline

For an Australian mid-market finance team deploying their first AI capability:

Phase	Duration	What Happens
Discovery	2–3 weeks	Audit data sources, define use case, confirm sovereignty requirements
Architecture	2–4 weeks	Design pipeline, select LLM hosting, define security and approval layers
Build	4–8 weeks	Implement RAG pipeline, integrate with source systems, build UI
Test	2–3 weeks	Validate accuracy, run against real financial data, stress-test edge cases
Production	1–2 weeks	Deploy, train users, establish monitoring
Total	3–5 months	From kickoff to a production-grade, compliant internal AI assistant

How Octane Fits

Octane Software Solutions is an IBM Finance & AI Partner headquartered in Sydney, with offices in Canberra, Mumbai, Bangalore, Hyderabad, Gurgaon, and Suva. We have completed 100+ enterprise projects with 90,000+ hours of implementation experience.

We work specifically at the intersection of enterprise finance and AI:

IBM Planning Analytics (TM1): Implementation, managed support, and AI integration via watsonx.
Agentic AI for finance: Secure, sovereign workflow automation built for Australian regulatory requirements.
RAG pipeline architecture: Internal AI assistants grounded in your data, hosted on your infrastructure.
Octane Blue Managed Support: 24/7 SLA-backed TM1 DevOps from USD $3,070/month.

We are not a general AI consulting firm. We are a finance technology firm that builds AI. There is a difference.

Visit us!

TM1 vs Power BI: When to Use Each

29 April 2026, 9:30:00 am AEST

17 min

If you are comparing TM1 and Power BI, you are asking the wrong question.

These are not competing products. They solve fundamentally different problems. TM1 is a planning engine, and Power BI is a visualisation platform. Comparing them is like comparing an engine to a dashboard: one produces the power, and the other displays the information.

The real question is: when do you use each, and how do you connect them?

The Core Difference in One Sentence

TM1 writes data. Power BI reads data.

That is the entire architectural divide in eight words.

IBM Planning Analytics (TM1) is a database where finance teams build budgets, run forecasts, and model scenarios. Users actively put data into TM1 (assumptions, targets, adjustments), and the engine calculates everything downstream in real time.

Microsoft Power BI is the opposite motion. Analysts pull data out of systems and present it through dashboards and reports. Users consume data in Power BI; they explore it, but they do not typically change it.

Once you understand this distinction, everything else falls into place.

Feature Comparison

Capability	IBM TM1 (Planning Analytics)	Microsoft Power BI
Primary Function	Planning, budgeting, forecasting, modelling	Reporting, visualisation, dashboarding
Data Direction	Read + Write (bidirectional)	Read-only (native)
Engine Type	In-memory OLAP database	Analytical / visualisation engine
Data Modelling	Multidimensional cubes, rules, TI scripting	Tabular models, DAX, Power Query
Excel Integration	Native bidirectional (PAfE)	Export/embed only
User Input	Core feature (data entry, allocations, approvals)	Not native (requires third-party add-ons)
Scenario Modelling	Sandboxes, versions, real-time what-if	Limited without writeback tools
Licensing	Enterprise subscription	Per-user subscription (Pro/Premium)
Ease of Use	Moderate (requires TM1 expertise)	High (self-service, drag-and-drop)
Best For	Finance teams managing complex plans	Organisation-wide data consumption

When to Use TM1

TM1 is the right choice when your team needs to create, manage, and calculate financial data, not just look at it.

Budgeting and Forecasting

This is TM1's home turf. Multi-entity budgets, rolling forecasts, and driver-based planning models are its strengths. Finance users input their assumptions, and TM1 calculates the downstream impact across every cost centre, region, and business unit in real time.

We have seen teams try to build budgeting workflows inside Power BI using SharePoint lists and Power Automate. It works for about three months until someone needs a version comparison, a conditional allocation, or an approval workflow that does not break when someone edits the wrong row. That is when the call comes in.

Power BI has no native mechanism for users to input budget data, define calculation rules, or run scenario models. It was never designed to.

Financial Consolidation

Multi-entity consolidation with intercompany eliminations, currency translation, and minority interest adjustments is core TM1 territory. If your organisation operates across legal entities (subsidiaries, joint ventures, regional holding companies), TM1 handles the consolidation hierarchy natively. Data aggregates from leaf-level entities upward through the legal structure automatically.

Cost Allocation

Distributing shared costs (IT overhead, corporate services, facility expenses) across departments based on calculated drivers is the kind of multi-step, cascading logic that TM1's rule engine was built for. Try replicating that in DAX and you will understand why TM1 developers exist.

What-If Analysis

TM1's sandbox feature lets analysts create personal "what-if" scenarios without touching the base data. Best case, worst case, and management case all run simultaneously with instant comparison. It is the feature that finance teams never know they need until they have it, and then they cannot live without it.

When to Use Power BI

Power BI is the right choice when your organisation needs to see, explore, and share data across every department, not just finance.

Executive Dashboards

This is where Power BI genuinely excels and TM1 does not pretend to compete. Power BI transforms raw data from ERP systems, CRM platforms, databases, and spreadsheets into interactive visual dashboards that look professional and update in real time. The drag-and-drop interface means non-technical users can build their own views. Furthermore, the Microsoft 365 integration (embedding dashboards in Teams, SharePoint, and email) is seamless in a way that no other BI tool has managed to match.

If your CEO needs a single screen showing revenue, pipeline, headcount, and customer satisfaction, Power BI is the tool.

Self-Service Reporting

Business users across every department (sales, marketing, operations, HR) can build their own reports in Power BI without waiting for an analyst to queue it up. The DAX formula language is surprisingly deep, and Power Query handles data transformation well enough that most business analysts can be self-sufficient within a few weeks.

TM1 can produce reports, but it is designed for the finance team. Power BI is designed for everyone else.

Organisation-Wide Distribution

Power BI Pro starts at approximately $10/user/month. That makes it commercially viable to give hundreds or thousands of people access to live dashboards. TM1's licensing model is built for the smaller, specialised teams who actively build and manage planning models, typically 10 to 50 users. If you need 500 people looking at data, Power BI wins on cost alone.

Cross-Source Analysis

Power BI natively connects to over 150 data sources. Pulling data from SAP, Salesforce, Oracle, SQL databases, and flat files into a single visual layer is one of its strongest capabilities. For organisations with fragmented data landscapes (which is most organisations), this alone justifies the investment.

The Real Answer: Use Both

In most mature enterprise environments, TM1 and Power BI are not competing. They are running in sequence:

Source Systems → TM1 (Planning + Modelling) → Power BI (Visualisation + Distribution)

TM1 handles the heavy lifting: complex financial calculations, budget collection, forecast modelling, and scenario analysis. It is the engine room where 15 finance professionals build and maintain the models that drive the business.

Power BI handles the presentation: transforming TM1's output into polished dashboards for the 200 executives, managers, and analysts who need to see the results but never need to touch the model.

This architecture gives you:

The modelling depth and writeback capability of TM1
The visual accessibility and distribution reach of Power BI
A single governed source of truth for all planning data

The question becomes: how do you actually connect them?

The Integration Challenge

Connecting TM1 to Power BI has historically been harder than it should be.

TM1 stores data in multidimensional cubes, while Power BI expects flat, tabular data. The traditional approach (exporting CSVs from TM1, moving files to a staging folder, importing them into Power BI) is manual, fragile, and destroys the real-time value of both platforms. Version mismatches, stale data, and reconciliation headaches are common. We have seen finance teams burn entire afternoons on this every reporting cycle.

Common Integration Approaches

Method	Pros	Cons
CSV/Excel Export	Simple, no setup	Manual, error-prone, no real-time
TM1py (Python API)	Flexible, customisable	Requires developer skills, ongoing maintenance
ODBC/SQL Staging	Standardised, widely understood	Adds complexity, latency, and another database to manage
DataFusion Connector	Real-time, low-code, no staging DB	Purpose-built for TM1

Why We Built DataFusion

We built the DataFusion connector because we kept seeing the same pattern across client engagements: a finance team with a perfectly functioning TM1 model, and a leadership team that could not see any of it because the Power BI connection was held together with CSV exports and a prayer.

DataFusion connects directly to TM1 cubes via the REST API, extracts multidimensional data, and serves it to Power BI in real time. There is no intermediate database, no custom ETL scripts, and no TM1 development skills required.

How it works:

DataFusion connects to your TM1 server via the REST API.
You select the cube and the data you need through a visual interface.
DataFusion extracts and flattens the data for Power BI consumption.
Power BI connects to DataFusion as a standard data source.
Dashboards update automatically as TM1 data changes.

The result: your finance team works in TM1, and your executives see the results in Power BI. No one exports a CSV ever again.

Decision Framework

Your Priority	Use This
Building budgets and forecasts	TM1
Visualising financial results	Power BI
Users inputting data	TM1
Organisation-wide reporting	Power BI
Complex allocation models	TM1
Combining data from 10+ sources	Power BI
Scenario modelling and what-if	TM1
Self-service analytics	Power BI
Both planning AND visualisation	TM1 + Power BI + DataFusion

See It Working

If your organisation runs TM1 and Power BI (or is considering both), we can show you how DataFusion bridges them in real time. No staging databases, no CSV exports, and no custom code.

Start your 60-day free trial now!

Octane Software Solutions is an IBM Finance & AI Partner with 90,000+ hours of TM1 experience across 100+ enterprise projects.

DataFusion Dynamic Arrays IBM

The Ultimate IBM Planning Analytics (TM1) Training Roadmap for Beginners

22 April 2026, 8:45:01 am AEST

6 min

IBM Planning Analytics (TM1) is a powerful enterprise planning and analytics platform used for budgeting, forecasting, and reporting. This premium roadmap is designed to take you from beginner to job-ready TM1 developer with practical skills, real-world use cases, and interview preparation.

roadmap (1)

Phase 1: Fundamentals (Week 1–2)

Understand OLAP concepts (Dimensions, Cubes, Measures)
Explore TM1 Architecture (Server, PAW, TM1 Web)
Install and navigate Planning Analytics Workspace (PAW)
Understand basic cube and dimension structures

Phase 2: Core TM1 Development (Week 3–5)

Create Dimensions, Hierarchies, and Attributes
Build Cubes and load sample data
Understand Rules (SKIPCHECK, FEEDERS)
Learn calculation logic and aggregation

Phase 3: TurboIntegrator (TI) Processes (Week 6–7)

Learn TI structure: Prolog, Metadata, Data, Epilog
Load data from CSV/DB into cubes
Use ASCIIOutput for debugging
Handle errors and optimize performance

Phase 4: TM1 MDX (Week 8–9)

Create dynamic subsets using MDX, SubsetCreateByMDX, SubsetMDXSet
Use TM1 functions: TM1SUBSETALL, TM1FILTERBYLEVEL
Understand DISTINCT vs TM1DISTINCT, Filtering data by value
Work with alternate hierarchies

Phase 5: Advanced Topics (Week 10–12)

Security: Groups, Users, Cube Security
Chores and Scheduling
Performance tuning and feeders optimization
Sandboxes and versioning

Real-Time Project (Must Do)

Build a Sales Planning Model:

Dimensions: Time, Product, Region
Create Cube: Sales Planning
Load data using TI
Apply rules for forecasting
Build reports in PAW

Best Practices

Always use feeders efficiently
Keep dimensions optimized and clean
Use TopCount, Filtering data by value in subsets
Debug using logs and ASCIIOutput
Use meaningful naming conventions

Interview Preparation Tips

Be ready to explain Cube, Dimension, Rule, and TI process
Know difference between feeders and SKIPCHECK
Practice MDX queries
Prepare real project explanation

Useful Resources

Official IBM Documentation: https://www.ibm.com/docs/en/planning-analytics

IBM Product Page: https://www.ibm.com/products/planning-analytics

Octane Page: https://blog.octanesolutions.com.au

By following this roadmap and practicing consistently, you can become a TM1 developer within 3 months. Focus on hands-on implementation and real-world projects.

Analytics IBM Planning Analytics latest enhancements

Mastering TM1DISTINCT: The Smart Way to Clean Up Your MDX Subsets

15 April 2026, 10:30:01 am AEST

8 min

IBM TM1 models can get messy fast, especially when you use alternate hierarchies and elements show up in multiple places. That's where the TM1DISTINCT MDX function quietly saves the day. Instead of blindly stripping out anything that "looks" duplicated, it understands TM1's hierarchies and only removes true duplicates – the exact same member in the exact same context

The Problem: Why Regular DISTINCT Isn't Enough

Imagine you're building a dynamic subset of Products. You pull all products under All Products, then union it with a special "Focus Products" consolidation. Now some products appear twice in the raw result. With the classic DISTINCT, TM1 might collapse these duplicates in a way that hides the structure you actually care about.

This is especially problematic when working with:

Alternate hierarchies that place elements in multiple logical positions
Union operations that naturally generate overlapping sets
Complex dimension structures where the same leaf element has different parents
Dynamic subsets that combine multiple source sets.

TM1DISTINCT is smarter: it keeps the element where it appears in different meaningful places, and only cleans up genuine duplication caused by unions or repeated logic.

Understanding TM1DISTINCT vs DISTINCT

The key difference lies in context awareness:

DISTINCT: Removes any duplicate entries that match another entry based on the member name. This can accidentally collapse elements that appear legitimately in different branches of the hierarchy.
TM1DISTINCT: Removes duplicates only when they are truly identical – same element, same hierarchy path, same context. It respects the multi-hierarchical nature of TM1.

While the existing DISTINCT function removes duplicate elements from a set, the new TM1DISTINCT function removes duplicate members only if they are truly identical, including their parent context. This distinction is important because a single element can appear as multiple members in a hierarchy if the element has different parents.

This distinction becomes critical when your dimension design intentionally places elements in multiple locations for different analytical views.

Practical Examples

Example 1: Basic Leaf-Level Filtering

TM1DISTINCT(
TM1FILTERBYLEVEL(
{Descendants([Product].[All Products])},
0
)
)
Here, you get a clean leaf-level list of products, free of accidental duplication, but still faithful to how the hierarchy is built. The function returns all leaf-level descendants while removing any technical duplicates that might arise from the query logic.

Example 2: Combining Multiple Sets (Union Scenario)

TM1DISTINCT(
{ TM1SubsetAll([Customer]) + [Customer].[Key Accounts] }
)

You end up with each real customer only once, even though "Key Accounts" is already part of the full customer list. This is where TM1DISTINCT truly shines – it preserves your intentional hierarchy structure while cleaning up the noise.

Example 3: Alternate Hierarchy Preservation

TM1DISTINCT(
TM1FILTERBYLEVEL(
{Descendants([Cost Center].[Total Company])},
0
)
)

Leaf-level cost centers under Total Company are returned, and any technical duplicates from unions or repeated selection logic are cleaned up safely. The alternate hierarchy placements remain intact.

Real-World Impact

Consider a retail company with a Product dimension that has both:

A Standard Hierarchy: All Products → Category → Subcategory → SKU
An Alternate Hierarchy: All Products → Channel → Brand → SKU

The same SKU (say, "Blue Shirt Medium") legitimately appears under both "Subcategory" and "Brand." Using DISTINCT here might collapse one of these occurrences, breaking reporting by channel. Using TM1DISTINCT keeps both occurrences because they represent different analytical contexts.

Best Practices

Use TM1DISTINCT when building dynamic subsets that combine multiple sets or work with alternate hierarchies
Avoid it only for simple, single-hierarchy subsets where standard DISTINCT would work fine
Combine with TM1FILTERBYLEVEL to ensure clean, context-aware filtering
Test with your actual dimension structure to verify the results match business expectations.

Conclusion

TM1DISTINCT represents a maturation of MDX handling in Planning Analytics, acknowledging that TM1's rich hierarchy support requires intelligent de-duplication. By using it in your dynamic subsets, you ensure clean data without sacrificing the intentional structure your dimensions are built upon. Your business users – and your data model – will thank you for it.

References

IBM Planning Analytics Documentation. (2024). TM1DISTINCT( <set> ). IBM. https://www.ibm.com/docs/en/planning-analytics/3.1.0?topic=tsmf-tm1distinct-set
IBM Planning Analytics. (2024). TM1 specific MDX functions. IBM. https://www.ibm.com/docs/en/planning-analytics/2.0.0?topic=mfs-tm1-specific-mdx-functions

Finance Transformation Blog Series: Why Month-End Close Is Still Broken and Why More Headcount Won't Fix It

1 April 2026, 8:29:59 am AEDT

8 min

The close has a structural problem. Here's what it actually is and why the solution isn't what most finance leaders think.

6–10

Business days lost to close every month

67%

Of finance leaders cite close as their #1 stress source

30+

hours/month spent on manual reconciliations

Let's be honest about something. You've probably tried to fix the close before.

Maybe you hired more analysts. Maybe you invested in an RPA tool that automated a handful of repetitive tasks. Maybe you tightened up your close calendar, sent the chasers earlier, and ran a post-mortem after every painful period end.

And yet. Here you are. Still losing a week — or more — every single month. Still watching your best people disappear into reconciliation hell while the business waits for numbers that should have been ready two days ago.

Here's the uncomfortable truth: it's not a people problem. And it's not an effort problem. It's a structural problem. And structural problems don't respond to more headcount or harder work.

The Real Reason the Close Takes So Long

Month-end close isn't slow because your team is slow. It's slow because of what the process actually requires: reconciling data across multiple systems that were never designed to talk to each other, applying human judgment to hundreds of individual transactions, chasing approvals across teams with competing priorities, and writing commentary from scratch on numbers that have only just become available.

Every one of those steps is a handoff. And every handoff is a place where delays compound, errors multiply, and your best finance people end up doing work that is, frankly, beneath them.

Think about what that actually means across a year. If your close takes 8 days, your finance team spends roughly 96 days — nearly a third of the working year — in close mode. Not in business partnering mode. Not in strategic planning mode. In close mode.

The opportunity cost of that is enormous. And it compounds every quarter, every year, every time a good analyst decides they'd rather work somewhere their skills are actually used.

Why RPA Didn't Solve It

A decade ago, Robotic Process Automation was sold to finance teams as the answer. Automate the repetitive stuff. Free up your people for higher-value work. It was a reasonable promise.

And RPA delivered — on a narrow slice of the problem. Rule-based tasks, fixed data formats, predictable exceptions: RPA handles those well. The problem is that the code isn't mostly rule-based, fixed-format, or predictable.

What RPA cannot do:

Distinguish a genuine anomaly from a normal pattern shift caused by a product launch or seasonal swing
Draft a correcting journal entry, present it for human approval, and post it to the GL — in one unbroken flow
Generate a plain-English narrative explaining why P&L moved the way it did this period
Handle intercompany disputes where both sides have slightly different numbers, and someone needs to reason through which is right
Learn from previous close cycles and improve its own judgments over time

RPA can move data between systems. It can't think about data. And the close fundamentally requires thinking.

This is why finance teams that invested heavily in RPA often find themselves in the same place as before: the repetitive tasks are faster, but the hard parts-the judgment calls, the exceptions, the explanations — still land on your most experienced people at the worst possible moment.

The Compounding Cost of the Status Quo

It's easy to treat the slow close as a known cost of doing business. It's less easy to quantify what it's actually costing you.

Direct costs:

Analyst overtime during close windows
Error correction — restatements, audit findings, and the rework they generate
Delayed management reporting, which means delayed decisions

Indirect costs, the ones that rarely make it onto a CFO's dashboard:

Analyst attrition: talented finance professionals don't join a company to reconcile spreadsheets for a third of their working year
Opportunity cost: every hour spent in the close is an hour not spent on forecasting, scenario modelling, or business partnering
Organisational credibility: a finance function that takes 10 days to close is perceived — fairly or not — as a constraint on the business, not a strategic asset

Manual journal entry errors remain a leading cause of restatements and audit findings. The close isn't just slow — it's risky.

What the Close Actually Needs

The close doesn't need more people doing the same things faster. It needs a fundamentally different approach to how the work gets done.

Specifically, it needs automation that can reason — that can look at a set of unallocated transactions and not just flag them, but identify the right cost centres, draft the correcting entries, surface them for human approval, and post them once approved. It needs automation that can handle exceptions, not just avoid them.

That capability now exists. It's called agentic AI, and it represents something genuinely different from the copilots, chatbots, and RPA tools that finance teams have been using up to now.

In the next post in this series, we'll get into exactly what agentic AI is, how it works, and why the month-end close is the perfect first place to apply it.

But if you'd rather see it working right now than read about it — that's understandable too.

Watch a full month-end close run by AI agents, in real time, with live approvals.

Email us at media@octanesolutions.com.au

shared resources automations TM1 support

Modernise Your TM1. Without Breaking What Already Works

27 March 2026, 9:00:00 am AEDT

13 min

Here’s a scenario I see playing out across organisations right now. Finance teams have invested significantly in TM1 — IBM Planning Analytics — but somewhere along the way, the platform stopped feeling like a competitive advantage and started feeling like a liability.

Excel is creeping back in. Enhancements are taking months. The system goes down at the worst possible moment. And in many cases, the entire environment is held together by one or two people who are a single resignation away from becoming a crisis.

I’ve spent the last twenty years working with TM1 environments across every size and industry, and this pattern is remarkably consistent. The tool is rarely the problem. The way organisations run and support the tool — that’s where things break down.

In our recent webinar, I walked through the three pillars that we believe will modernise any TM1 environment — without starting over, without a costly eighteen-month project, and without breaking the things that already work.

Pillar 1: Infrastructure — Are You Running on the Right Foundation?

The first thing we audit with any new client is their infrastructure. And almost without exception, on-premise sites are running on versions that are three to five years out of date. That’s not a criticism — it’s the reality of how upgrade cycles work when there are competing priorities and limited IT resources.

The consequences are real: technical debt, security exposure, and a platform that’s increasingly hard to build upon. IBM ended support for versions 10.2 and below in April 2024. If you’re still on one of those versions, this conversation is urgent.

Today, there are three infrastructure options:

On-Premise: Full control, but the patching, security, and upgrades become your responsibility. In our experience, we have yet to encounter a single on-premise site running the latest version. Not one.
IBM Managed Hosting (IBM Cloud): IBM runs the platform for you. Upgrades are managed. The infrastructure burden shifts away from your team. This is a strong option if you want to focus budget on functionality rather than keeping the lights on.
AWS (PA on SaaS): The newest option, and in our view, the most compelling for organisations starting fresh or migrating today. Version 12, fully cloud-native, elastic RAM scaling in 16GB increments, and the lowest infrastructure overhead we’ve seen. Available in Australia and multiple other markets.

The economics matter here. On-premise can appear cheaper until you account for the IT time, upgrade projects, and security management it demands. Cloud pricing continues to decline. And migration itself is more straightforward than most teams expect — we have a dedicated team and a tested checklist that handles the move, including legacy Excel macros and TI processes that need extra care.

One important point: moving to the cloud is not modernisation on its own. If you move poor architecture onto better infrastructure, you still have poor architecture. Infrastructure is the foundation. Architecture is the next conversation.

Pillar 2: Architecture — Is Your Model Built to Last?

Legacy TM1 models accumulate debt in predictable ways. Different consultants come in over the years, each doing good work for the specific problem in front of them. But when you look at the model as a whole, the design may no longer reflect where the business is, or where it needs to go.

Some common symptoms we encounter:

Rules logic so layered over the years that no one can fully trace a number back to its source
Hard-coded dimension logic that makes even routine changes risky
Bloated, unused dimensions sitting in models and consuming memory
Critical processing knowledge living only in the head of one person
Metadata-driven design. Month rollovers, version changes, and business rule updates happen by changing a config cube — not by touching code.
Purpose-built, modular cubes. A separate staging layer, a core calculation layer, an input layer, and a reporting layer. It looks like over-engineering until you need to scale or change something.
Decoupled reporting. Reports connect to the relevant cubes and update automatically. Changes to the underlying model don’t cascade into manual reporting fixes.
Designed for change. The model is built for what the business will need, not just what was required at the time of build.

Modern TM1 architecture is built around a few core principles:

In some cases, we encounter, the accumulated technical debt is significant enough that a full rebuild is actually cheaper and faster than attempting to refactor what exists. We’ve seen organisations consider switching platforms entirely — not because TM1 couldn’t do what they needed, but because internal friction around a legacy model made change impossible. That’s a solvable problem, and it starts with an honest architecture assessment.

Pillar 3: Support — Moving from Reactive to Managed

This is the pillar that makes the most immediate difference, and the one most organisations have never properly addressed.

Traditional TM1 support looks like this: something breaks, someone raises a ticket, a consultant is engaged, investigation starts from scratch. The same issues recur every month. Knowledge lives in one person’s head. There are no SLAs, no health checks, no road map.

The data on this is stark. Around 60% of TM1 support spend goes into reactive work — fixing things that have already broken. Studies consistently show it costs three to five times more to fix production issues than to prevent them. And approximately 40% of finance teams report that Excel shadow models are running alongside TM1, filling the gaps where the platform is too slow or too broken to serve the business.

Modern managed support changes this. The key features:

Proactive health monitoring: Continuous environment monitoring rather than waiting for users to report problems
Finance calendar alignment: Support resources and capacity planned around budget cycles and month-end close, not allocated reactively
Defined SLAs: Users know what Priority 1 means, when the clock starts, and what happens next
Recurring issue elimination: Every resolved ticket is reviewed. Is this pattern recurring? If so, it goes into the backlog for a permanent fix
Knowledge transfer: No single-point-of-failure dependency. Multiple team members hold model knowledge, documented in a living knowledge base

The outcome, based on our work with clients across this model: a reduction of approximately 35% in total cost of ownership within the first several months and through optimised licensing, reduced reactive spend, and the compounding benefit of preventing issues rather than repeatedly fixing them.

The Practical Road Map: What to Expect and When

Modernisation does not need to be an eighteen-month programme. Here is what a practical timeline looks like:

Weeks 1–4: Assess and Plan

Audit the current architecture. Review support history — specifically, what tickets have recurred in the last six months and why. Assess infrastructure options and costs, including licensing implications. Align IT, security, and finance stakeholders on the direction.

Months 2–4: Execute

If migrating to the cloud, schedule and complete the migration. Begin architecture improvements, prioritised by business impact. Onboard managed support. Early benefits typically become visible around month four.

Ongoing: Optimise

Quarterly architecture reviews. Continuous performance tuning. Regular training as new features are released (IBM ships updates roughly every six weeks on the cloud). The knowledge base and road map stay live. Once this rhythm is established, improvements compound.

What Organisations That Have Done This Are Seeing

These are outcomes from actual client engagements, not projections:

Faster budgeting and month-end cycles: When the platform is stable and changes can be made quickly, the cycle compresses
Significant reduction in data errors: Proper architecture catches exceptions before they reach users
Zero outages during month-end: This is a bold statement, but it’s what the data shows when the foundational work is done properly
Finance teams spending more time on analysis than administration: The tool starts doing what it was bought to do

There is also a forward-looking consideration. If your organisation has any kind of AI strategy that involves TM1 data — and most do, or will — the data foundation has to be reliable. A platform that crashes at month-end is not a foundation you can build AI capability on. Modernisation is not the end goal. It is the prerequisite for everything that comes next.

Ready to Modernise Your TM1 Environment?

Octane Solutions is an IBM Gold Partner with over 100 TM1 projects delivered and the #1 IBM Partner award in the APAC region. We work with organisations globally — across Australia, New Zealand, the Pacific, India, the Middle East, and North America — on a flexible, no lock-in basis.

Our support tiers (Octane Green, Blue, and Black) are transparently priced on our website. If you’re not sure where to start, a complimentary health check will give you a clear picture of where your environment sits and what to prioritise.

Visit us: https://www.octanesolutions.com.au/tm1-support

Or reach out to Amendra directly on LinkedIn to discuss your environment.

If you’re on version 10.2 or below, or still running on-premise without a plan to move — this conversation is worth having today.

Your Finance Team Loses 6–10 Days Every Month. Agentic AI Wants Them Back.

26 March 2026, 1:00:00 pm AEDT

9 min

How Octane FastClose is turning the month-end grind into a managed, automated, and human-controlled process — live today.

Every month, without fail, your finance team disappears. Not on a retreat. Not on holiday. They vanish into the black hole of financial close — a gruelling 6 to 10 day sprint of spreadsheets, exception chasing, manual journal entries, and last-minute reconciliations.

The statistics paint a stark picture. It is 2026, and teams are still copy-pasting between three systems to reconcile cash.

Octane FastClose was built to change that.

Why the Close Is Still Broken

The month-end close has a structural problem that more people, more spreadsheets, and even basic automation cannot solve. It is a multi-system, multi-team, multi-judgement process where every delay compounds and every error multiplies.

The core pain points are well known to any controller who has lived through them:

Manual reconciliations eat 30+ hours per month, with data spread across GL, ERP, sub-ledgers, and bank feeds that rarely agree.
Journal entries drafted in Excel, reviewed over email, and posted manually — slow, error-prone, and almost impossible to audit cleanly.
Variance analysis is produced in isolation, without the system context to explain whether a movement is a product launch spike or a genuine anomaly.
The "last mile" problem — chasing approvals, resolving intercompany disputes, and writing executive narratives that could have been automated from the data itself.

RPA helped with repetitive tasks. But RPA cannot reason. It cannot distinguish a genuine anomaly from a normal pattern shift. It cannot draft a correcting journal entry, present it for approval, and post it to the GL — all in one unbroken flow.

Traditional automation has hit its ceiling. The close needs something that thinks.

"67% of finance leaders say the close process is their team's single greatest source of month-end stress. The average mid-market close takes 6–10 business days. Errors in manual journal entry are a leading cause of restatements and audit findings."

— CFO Survey, 2026

Enter Agentic AI — And Why It Is Different

Most finance teams have already encountered AI in the form of chatbots and copilots. These tools are useful. They answer questions, summarise documents, and draft commentary. But they are fundamentally passive — they wait to be asked. They do not act.

Agentic AI is built on a different principle. An AI agent is designed to pursue goals autonomously — planning a sequence of steps, executing each one, assessing the result, and adapting. Applied to month-end close, this means an agent that does not just tell you there are 235 unallocated transactions. It identifies them, proposes the correct cost centres, presents a summary for your approval, and then posts the journals. The agent is a participant in the close process, not a bystander.

This is the architecture behind Octane FastClose.

Meet the FastClose Agents

FastClose deploys a team of specialist AI agents, each owning a defined piece of the close. A Master Orchestrator coordinates them — sequencing tasks, routing exceptions to humans, and learning from every cycle.

🔍 Recon Agent

Automatically matches intercompany, bank, and sub-ledger reconciliations. When a break occurs, it performs root-cause analysis and flags it — with context — for human review.

📝 Journal Entry Agent

Drafts accruals, reclassifications, and adjusting entries based on transaction patterns and accounting policy rules. Every proposed journal is presented for controller approval before posting.

📈 Flux Agent

Runs variance analysis and generates plain-English narratives explaining what drove P&L movements — automatically, at the moment the numbers are available.

🏢 Consolidation Agent

Handles intercompany eliminations, FX translation, and minority interest calculations across entities — the work that typically consumes a disproportionate share of the close for multi-entity groups.

✅ Validation Agent

Runs 200+ data integrity checks, SOX control assertions, and close readiness scoring. Surfaces issues before they become audit findings.

The Human Stays in Control — By Design

The most common concern we hear from finance leaders is governance. If the AI is making decisions, who is accountable?

FastClose answers this directly. The agent is not a decision-maker. It is an expert preparer.

Every proposed journal entry, every cost centre assignment, every reforecast scenario, and every period lock is gated behind an explicit human approval. The controller sees what the agent proposes, reviews the supporting analysis, and approves or rejects. The agent then acts on that instruction.

Every step is logged. Every decision — including rejections and revisions — is recorded in a full audit trail. Segregation of duties is maintained. The result is a close that is not only faster but more controlled and more auditable than the manual process it replaces.

The FastClose Principle: "The agent does the work. The controller makes the call."

FastClose is a force multiplier for your finance team — not a replacement for it.

Where FastClose Is Today

FastClose is not a prototype. It is a live, production-deployed system. Fifteen of the thirty-two tasks in the full close cycle are operational today, spanning all seven phases of the close.

The roadmap extends to intercompany reconciliation, automated accruals, fixed asset depreciation, bank reconciliation, balance sheet movement analysis, and a full close task board — all gated by human approval at every material step.

Why Now Is the Right Time

Finance functions that wait for agentic AI to become mainstream before acting will find themselves a full close cycle behind their peers.

The technology is mature enough to deploy reliably today. The organisations piloting these capabilities now are compressing close timelines, redeploying analyst time to higher-value work, and building institutional knowledge in governing AI-assisted financial processes.

Critically, adopting FastClose does not require replacing your ERP, restructuring your team, or undertaking a multi-year transformation programme. It is designed to work alongside your existing systems — starting with the highest-impact, most automatable tasks and expanding coverage progressively as your team builds confidence in the model.

See FastClose in Action

Join us at our upcoming live event to see the agent run a full month-end close in real time — on real data, with live approvals.

AI IBM

TM1 is never the Problem. Support is.

18 March 2026, 9:29:59 am AEDT

10 min

Why most TM1 environments underperform — and the four-step blueprint to fix it.

Support

When a finance team tells me their TM1 environment "doesn't work", I've learned to listen carefully — because the platform is rarely the real issue.

What they actually mean is one of four things: Excel is creeping back in. Enhancements are taking months instead of days. The system goes down at the worst possible moment — mid-budget cycle or during month-end close. Or, most dangerously, the entire environment is held together by one or two people, and a single resignation away from a crisis.

The root cause of all four problems is the same: the operating model is reactive, knowledge is trapped, and improvements never get prioritised. In short, TM1 is not the problem. Support is.

The Four Support Models and Where Each One Breaks

Most organisations fall into one of four support models. The first step to improving your environment is recognising which one you are.

Model 1: The In-House Specialist

You have one or two TM1 experts who own the environment. It works well when things are stable, and they're content. The breaking point arrives when they leave, or when the backlog grows faster than one person can manage. This is what the IT industry calls the "bus factor", what happens when the key person gets hit by a bus? It is not a strategy.

Model 2: Finance as the Admin

Finance has taken on the pseudo-admin role either because it was cheaper, more convenient, or gave them greater control. This works reasonably well in small deployments. It breaks when FP&A spends more time maintaining TM1 than using it for insights. The tool that was supposed to free finance ends up creating a new administrative burden, and Excel creeps quietly back in.

Model 3: Ad Hoc Consulting

You call a vendor or consultant when things break. This is common — especially after a difficult implementation. The problem is structural: you pay to fix the same issues repeatedly, there is no knowledge transfer, no improvement, and you are essentially funding someone else's consulting pipeline by never resolving the root cause.

Model 4: Hybrid or Managed Support (Where You Should Be Heading)

A model built for modernisation: clear governance, defined SLAs, mixed-maturity handling, peak-load capability, and measurable KPIs. This is the model that transforms support from a cost centre into a genuine business enabler.

What Modern TM1 Support Actually Looks Like

Not the brochure version. The operational reality.

Modern support is built around five pillars:

Coverage and Response. Defined SLA targets — not best effort. You know what Priority 1 means, when the clock starts, and what happens next. Communication is transparent.
Single Intake Funnel. No artificial split between support, development, and training. If it helps the business, it goes into one queue. No out-of-scope conversations.
Prevention Over Firefighting. The goal isn't faster ticket closure — it's eliminating the ticket in the first place. Every resolved issue is reviewed: Is this recurring? If so, it goes into a problem backlog and gets a permanent fix.
Reporting and Accountability. Monthly KPI reports, backlog trends, and SLA compliance data. You can see what's happening and hold your support partner accountable.
A Live Road Map. Structured improvement with capacity built into every engagement. Not a list of hopes an actual plan with milestones.

The key mindset shift: modern support is not about faster ticket closure. It is about predictable cost, reduced risk, and continuous improvement — a model that prevents recurring pain rather than profits from it.

The Prevention Imperative

This is where most support models genuinely fail. In a reactive model, organisations pay to fix the same problem every month. In a prevention-led model, you invest once in understanding the root cause — and then it's gone.

Based on our experience across client sites, more than 50% of ongoing support spend is going into repeatable issues that could be fixed once and automated. Think about that: half your support budget is fixing things that shouldn't be happening.

Effective prevention involves three things:

Recurring ticket elimination: Identify the patterns and close them permanently.
Proactive health checks: Log hygiene, performance baselines, and chore execution validation — run fortnightly or monthly before problems surface, not after.
Automation of manual issues: Every repeatable manual task is flagged for automation. As the environment stabilises, ticket volumes drop, and human intervention decreases.

Three Real Clients. Three Consistent Outcomes

The following outcomes come from actual client engagements — three different environments, three different sizes and industries, but strikingly consistent results.

Case Study 1: Large Media Group 35% Cost Reduction

A complex multi-entity TM1 environment with a heavy reporting load, mid-way through a cloud migration. By moving from a reactive break-fix model to a governed, predictable support structure with defined SLAs, this client eliminated their top five recurring issues within 90 days, reduced ticket volume by 40%, and completed their cloud migration with zero disruption to BAU operations including through a live budget cycle.

The lesson: The 35% cost saving is the headline. The mechanism predictable cost, removal of key-person risk, and proactive maintenance are what actually drove it.

Case Study 2: Large Financial Services Group: 10-Week Budget Cycle Reduction

Close to a thousand users, 50 rolling forecasts, and heavy allocation and scenario modelling workloads. The budget cycle was cut by ten weeks. Scenario modelling extended to a 60-month horizon. Finance shifted from process-heavy administration to genuine analysis.

The lesson: None of this came from a new module or feature. It came from platform stability. When the system isn't crashing, when changes don't take weeks, the team can actually start using it. That is what stable support contributes to planning transformation.

Case Study 3: Global Retailer Zero Spreadsheets, 120 Hours Saved Monthly

A finance team managing reporting across 15 to 20 offline spreadsheets. Reports that previously took weeks now take five minutes. 120 hours saved per month — roughly equivalent to one full-time finance analyst returned to the business for actual analysis. And zero offline spreadsheets remaining.

The lesson: For the CFO, eliminating spreadsheets was the most significant outcome not the time saving. Every uncontrolled spreadsheet is a version-control risk and an audit exposure. Stable TM1 support is not just an efficiency gain; it's a governance improvement.

The Support Maturity Curve: Where Are You?

Most organisations, if honest, sit at Level 1 or Level 2. Here is the full picture:

Level 1 — Reactive: Fix things when they break. Knowledge lives in people's heads. No road map.
Level 2 — Stable: The environment is reliable. A support model exists, even if imperfect. Recurring issues are managed but not eliminated.
Level 3 — Optimised: Prevention is active. Telemetry is live. Ticket volumes are declining. Finance is spending more time on analysis than administration.
Level 4 — Innovation Ready: The platform is a genuine competitive advantage. Planning is automated. AI tools are actively in use. Finance is leading the organisation forward as a true business partner.

The opportunity is not moving from Level 1 to Level 4 overnight. It is taking the next step. Levels 2 to 3 are where organisations see the biggest returns.

The 6–12 Month Road Map

Months 1–3: Stabilise

Audit the environment. Fix the top five recurring issues. Stand up a single intake queue. Get telemetry and reporting live within 48 hours. Most organisations attempt to modernise before they have stabilised — that is backwards. Worse still, many implement another tool because the current one feels broken. Changing the tool will not solve a support model problem.

Months 4–6: Optimise

Telemetry is fully deployed. Minor requests are automated. SLA reporting live. The knowledge base is built out. Around this period, support ticket volumes will begin to decline noticeably.

Months 7–12: Modernise

If you are still on-premises, cloud migration should be a key consideration here (IBM Cloud or AWS, depending on your scale and environment). Legacy models decommissioned. Finance has shifted from process work to analysis. If you are on an older version, anything 10.2 or below, IBM ended support for those versions in April 2024. You are running unsupported software. This conversation is urgent.

The True Cost of Poor Support

Most organisations measure support costs as consultant invoices. The real cost is much broader:

Finance time wasted managing the tool instead of using it
Audit risk from uncontrolled spreadsheet use
Delayed forecasting and planning cycles
A platform too unstable to build upon
AI projects that cannot move forward because the data foundation is unreliable

That last point is increasingly critical. If your organisation has an AI strategy that involves TM1 data, and TM1 is crashing every month, you will have no confidence, nor will the AI team or the board in using those cubes as a foundation. Stable support is not the end goal. It is the prerequisite for everything else.

Where to Start

Ask yourself ten questions about your current support model:

During peak periods, is the system fast and reliable or slow and crashing?
How many recurring bugs or tickets do you handle every month?
What happens to unused support hours? Are they lost, or rolled forward?
Can your support model handle development work without a separate commercial fight?
How long does it take to deliver a critical finance report days, hours, or instantly?
Do you have performance monitoring on stats control cubes to spot regressions early?
Are your TM1 logs monitored so that accumulation doesn't silently pressure the server?
Who controls the ticket priority: finance, IT, or whoever shouts loudest?
Is there a knowledge base or runbook being actively maintained and shared?
Do you have an active road map aligned to IBM's support lifecycle?

Score one point for each yes. If your total is under seven, your support model is introducing meaningful risk into your finance function.

The good news: this is entirely fixable. The road map is clear, the outcomes are proven, and the first step is simply understanding where you are.

Ready to assess your TM1 support model?

Contact the Octane Solutions team for a complimentary five-question health check assessment, or reach out to Amendra directly on LinkedIn. We work with organisations globally from Australia, New Zealand, and the Pacific through to India, the Middle East, and North America on a flexible, no lock-in basis.

Learn more and get started: www.octanesolutions.com.au/tm1-support

TM1 support

Why CIOs and CFOs Are Moving TM1 On-Prem to Planning Analytics SaaS on AWS

17 March 2026, 9:29:00 am AEDT

3 min

TM1 v11 on-prem has served organizations well for years. It’s fast, flexible, and trusted. But the expectations around enterprise platforms have changed. Today, leadership teams want lower risk, predictable costs, and systems that evolve without constant reinvestment.

That’s where Planning Analytics SaaS on AWS fits in—not as a new planning engine, but as a better way to run one.

You Stop Running Infrastructure

With on-prem TM1, you’re not just running a planning system—you’re running servers, storage, backups, patches, and disaster recovery. Even when nothing breaks, there’s ongoing effort and risk.

Planning Analytics SaaS changes that. The platform is fully managed by IBM on Amazon Web Services. Availability, backups, and resilience are built in. IT teams spend less time maintaining platforms and more time supporting the business.

Costs Become Predictable

On-prem costs rarely end with licenses. Hardware refreshes, DR environments, security fixes, and upgrade projects add up quietly over time.

SaaS replaces that with a subscription model. No capital spend, fewer surprises, and much clearer long-term cost visibility—something finance teams appreciate immediately.

Lower Risk, Stronger Security

In an on-prem setup, availability and security depend heavily on how much time and money the organisation can invest.

In Planning Analytics SaaS, resilience and security are standard. High availability, encryption, and regular security updates are part of the service, not optional extras. This reduces operational risk and simplifies audits.

No More Upgrade Projects

Upgrading on-prem TM1 is disruptive, which is why many systems stay untouched for years.

With SaaS, updates just happen. New features arrive without downtime or upgrade programs. The platform stays current without forcing the business into large, risky change initiatives.

Performance Scales When It Matters

Planning systems are pushed hardest during budgets and forecasts, but on-prem hardware is fixed year-round.

SaaS handles peaks without permanent over-investment. Performance stays consistent during critical cycles, without IT having to guess future capacity needs.

Faster Time-to-Value

Standing up new environments or supporting business growth takes time when infrastructure is involved.

With SaaS, environments are available faster, projects move more quickly, and new requirements can be supported without long lead times. This improves agility across finance and operations.

Cleaner, Modern Integration

Traditional file-based integrations are fragile and slow.

Planning Analytics SaaS supports secure, API-based integration, making it easier to connect planning with ERP systems and cloud data platforms. This aligns better with modern enterprise data strategies.

Better Governance by Design

SaaS comes with boundaries—no server access, no unsupported scripts, no hidden workarounds.

While that requires adjustment, it results in cleaner architectures, fewer production issues, and stronger governance. Over time, most organisations see this as a benefit, not a limitation.

The Bottom Line

Moving from TM1 on-prem to Planning Analytics SaaS on AWS isn’t about changing how you plan. It’s about reducing risk, simplifying operations, and making costs and performance more predictable.

For CIOs, it means less infrastructure and lower operational exposure.
For CFOs, it means clearer costs, better scalability, and fewer surprises.

In short, it’s a more modern way to run a planning platform—without losing what made TM1 valuable in the first place.

IBM Planning Analytics latest enhancements TM1 support

Modernising TM1: Why Cloud Migration Alone Doesn’t Solve the Problem

25 February 2026, 2:04:37 pm AEDT

7 min

IBM Planning Analytics (TM1) remains one of the most powerful planning and modelling engines used by Finance teams worldwide. Yet many organisations eventually experience frustration with their TM1 environments — slow performance, painful upgrades, rising support costs, and the quiet return of Excel.

Contrary to popular belief, these challenges are rarely caused by TM1 itself.

They are symptoms of a modernisation gap.

The Hidden Drift Problem in TM1 Environments

TM1 models often start clean, efficient, and purpose-built. Over time, however, incremental changes accumulate:

New dimensions added without structural discipline
Rules layered onto legacy logic
TI processes expanded beyond their original design
Reporting logic intertwined with source data
Upgrade cycles deferred
Risk and compliance requirements
Upgrade tolerance
Internal IT capabilities
Cost predictability objectives
Metadata-driven logic instead of hard-coded processes
Modular cube design separating input, calculation, and reporting
Decoupled reporting layers
Performance-first feeder strategies
Cloud-aware security models
Structured change management practices
Issues detected late
Knowledge concentrated with individuals
Upgrades treated as disruptive events
Costs becoming unpredictable

What emerges is not a broken system — but a fragile one.

Performance declines. Change cycles slow. Complexity rises.

The Common (But Incomplete) Response: “Move to Cloud”

When issues surface, organisations frequently default to infrastructure decisions:

✔ Move from on-premise to cloud
✔ Adopt SaaS
✔ Change hosting providers

While these shifts reduce infrastructure management overhead, they do not automatically modernise the model.

A poorly structured TM1 architecture behaves the same way regardless of where it is hosted.

Better infrastructure cannot compensate for design inefficiencies.

True TM1 Modernisation Requires Three Pillars

Sustainable TM1 environments align three interdependent areas.

1. Infrastructure Modernisation

Infrastructure choices should reflect:

Cloud platforms reduce maintenance effort — but they are only the foundation.

2. Architecture Modernisation (The Critical Lever)

Architecture modernisation is where the largest gains are realised.

Modern TM1 models typically prioritise:

Without architectural evolution, cloud migration simply relocates existing constraints.

3. Support Model Modernisation

Traditional break-fix support models introduce systemic risk:

Modern support approaches focus on:

✔ Proactive monitoring
✔ SLA-driven response models
✔ Continuous optimisation
✔ Upgrade lifecycle management
✔ Knowledge transfer

This operating philosophy underpins Octane Blue, our proactive TM1 managed services model.

Why This Matters for Finance Leaders

Modernised TM1 environments typically deliver:

✅ Faster budgeting and forecasting cycles
✅ Lower data errors
✅ Safer upgrade paths
✅ Reduced operational friction
✅ More predictable support costs

Most importantly, Finance teams regain time, stability, and confidence.

Final Perspective

Cloud migration is valuable — but it is not modernisation by itself.

Real TM1 modernisation redesigns how the model scales, performs, and evolves with the business.

📘 Download the TM1 Modernisation Roadmap (PDF)
📊 Request a Free Upgrade & Risk Estimate

Smarter Cube Views in IBM Planning Analytics Workspace 3.1.3

21 January 2026, 9:00:01 am AEDT

9 min

Introduction

IBM Planning Analytics Workspace 3.1.3 has made a noticeable difference to how I work with cube views every day. Instead of dragging views around and hoping I do not overwrite something important, I now use the built-in view selector to switch between different saved views in seconds.

With Undo and Redo available directly in the cube view, it feels safe to try new layouts, pivots, and filters because I can always step back if the result is not what I expected. Together, these changes reduce friction in my analysis, keep my report layouts intact, and help me move much faster from “idea” to “answer".

Part 1: From Drag-and-Drop to Smart View Selection

Before 3.1.3

Analysts had to drag views from the left panel onto the grid, risking accidental overwrites and losing custom filters and formatting with each swap. Finding the right view in a cluttered list of auto-generated names wasted time. Or they can click on three dots and click on ‘Add View’

Now in 3.1.3

The new View selector dropdown lets you switch views instantly while preserving layout, filters, and formatting. Search by keyword to find relevant views fast.

Key benefits:

Layout stays intact when switching views

Fast scenario comparisons (Actuals → Budget → Forecast)

Searchable view list for large libraries

Part 2: Undo and Redo for Cube Views

Before 3.1.3

Changes to cube explorations were permanent. Pivoting dimensions, reordering members, or adjusting filters felt risky, so modelers hesitated to experiment and often retreated to Architect.

Now in 3.1.3

Native Undo/Redo buttons on the toolbar let you experiment safely. Move dimensions, adjust filters, and reorder members with confidence—revert instantly if needed.

A screenshot of a computer
AI-generated content may be incorrect.

What you can undo/redo:

Move dimensions between context, rows, and columns

Expand/collapse hierarchies

Apply/remove filters

Reorder dimensions

Quick Wins

For report builders: Use the view selector to build flexible reports; users switch views at runtime.

For modelers: Experiment freely with Undo/Redo; refine layouts through rapid iteration.

Conclusion

Planning Analytics Workspace 3.1.3 simplifies cube view workflows with a smart view selector and Undo/Redo. These features accelerate analysis, reduce friction, and help teams transition confidently from legacy tools to modern Workspace.

Enabling the cube viewer switcher needs to be done for each view separately, which is a drawback. It would be good to have a universal configuration for enabling it for all the views.

References

[1] IBM. What's new in modelling – 3.1.3. IBM Documentation, 2025. https://www.ibm.com/docs/en/planning-analytics/3.1.0?topic=2025-whats-new-in-modeling-313

[2] IBM. What's new in Planning Analytics Workspace 3.1.x? IBM Documentation, 2025. https://www.ibm.com/docs/en/planning-analytics/3.1.0?topic=workspace-whats-new-in-planning-analytics

AI IBM Planning Analytics latest enhancements IBM WatsonX Orchestrate

Agentic AI in Finance & TM1: Why Everyone’s Suddenly Talking About It

10 December 2025, 9:00:01 am AEDT

7 min

If you’re a TM1 professional and have been near the finance or FP&A world lately, you’ve probably heard the buzzword of the season: Agentic AI.

It sounds fancy and must have wondered why suddenly everyone is talking about it, but honestly, it’s just AI that doesn’t sit around waiting for you to poke it. It does things — proactively and automatically.

And when you mix that with platforms like IBM Planning Analytics / TM1, things start getting interesting.

Orchestrate

So… What Exactly Is Agentic AI?

Imagine if your TM1 rules, processes, and chores had a brain.

Not just “if X then Y”, but something that can:

Notice something’s off
Decide what to do
Do it
Tell you what it did
Learn from the outcome

That’s, in a nutshell, agentic AI in the TM1 paradigm.

Think of it as giving your FP&A stack its own mini team member — minus the coffee breaks or the usual shenanigans that you’ve to bear with daily.

In practical terms, agentic AI can help rather than just be a buzzword decoration floating around in everyone’s LinkedIn posts or formal/informal conversations.

I like to highlight below a few basic things - yet very important – that agentic AI is really good at doing:

1. Automated Data Babysitting (Finally!)

Every TM1 admin knows the pain: source system changes, missing records, late files… chaos.

Agentic AI can:

Watch data pipelines for delays
Fix formatting issues on the fly for your TI process
Alert you before the morning refresh explodes

Basically, your nightly chore is that you just hired an assistant.

2. “Hey, Something’s Wrong” Alerts (That Make Sense)

Instead of a typical TM1 process error message that looks like it was written in 1995, agentic AI can:

Spot outliers, bad allocations, weird spikes – something you would do manually otherwise
Compared to historical patterns
Tell you, in plain English, why it’s weird

Something along the lines of:

“Hey, sales in APAC are 4x higher than normal for Mondays. It could be a missing filter. Want me to check?”

Yes, please.

3. Forecasting That Doesn’t Feel Like Guesswork

Sure, TM1 can forecast, and it can predictive forecast really well.

But agentic AI can simulate scenarios on its own and recommend the best one.

Examples:

Auto-build 20+ what-if scenarios
Rank them based on risk or probability
Push the best one straight into a cube

It’s like giving your CFO a crystal ball… a slightly nerdy one.

4. TM1 Admin Tasks… Done Automatically

This is the part TM1 developers love.

Agentic AI can:

Fix failing processes
Rewrite TurboIntegrator code
Clean up unused object
Suggest how to reduce the cube size

Admittedly, given it's all subjective, and it's easier said than done, but the possibilities do exist with the more quality data we can ingest and the more we can train the model.

5. Natural Language Access to TM1

We’ve already seen this with AI chat Assistant in PAW where instead of navigating a million cubes and views, we can prompt Planning Analytics such as, “Give me gross margin by product for Q3 vs last year and show me drivers of variance.”

And it does it a fine job.

No view-building. No subset drama. No filter pain.

6. Real-time Decision Automation

Finance teams love workflows and agentic AI is perfect for building the workflows.

It loves automating those workflows.

Approve expenses based on policy
Kick off TM1 processes when thresholds hit
Trigger emails, Teams alerts, Slack actions
Update commentary automatically

So instead of actively entering the forecasts or budgets, the agent proactively taking steps to initiate those steps for you.

With time, we’re only going to see more of:

AI agents running close cycles
AI agents building dashboards
AI agents talking to ERP, CRM, S3, APIs without humans touching integrations
AI agents are debugging your model while you sleep

Why TM1 Specifically Is a Perfect Fit

As we know, TM1 is:

Real-time
Calculation-heavy
Highly scriptable
Connected to everything
Used for tons of repetitive work

Which is exactly the playground where agentic AI thrives.

Plus, TM1 developers are already half-cyborg 😉 with the stuff they automate — agents just take it further.

So the biggest takeaway from all of this is that Agentic isn’t coming “in the future”, it's already there! Things are definitely moving and moving at a very fast rate in this space.

It’s already sliding into FP&A tools, APIs, planning models, and the daily grind of finance teams. If TM1 was the engine, then agentic AI is the turbocharger bolted on top.

And yes — as a disclaimer, it might even finally stop your chore from failing at 3 AM for no reason 😉

IBM Satellite Connector: Bridging the Gap Between Edge and Cloud

22 October 2025, 10:30:00 am AEDT

6 min

In the modern era of cloud computing, businesses are increasingly leveraging distributed architectures to meet the growing demand for faster, more efficient services. One of the key innovations that addresses this need is the concept of edge computing, where data processing occurs closer to the source of the data rather than relying on centralised cloud data centres. However, with the edge computing paradigm comes the challenge of connecting devices and systems distributed over a wide area and ensuring smooth communication between edge environments and the cloud. IBM’s Satellite Connector is designed to solve this problem by offering a seamless and secure way to integrate edge workloads with cloud services.

What is IBM Satellite Connector?

The IBM Satellite Connector is a robust solution that allows businesses to extend their IBM Cloud environment to the edge of their networks. It is part of IBM’s suite of solutions for edge computing and hybrid cloud environments, allowing organisations to run cloud-native applications and workloads closer to data sources, such as IoT devices or remote locations, while maintaining connectivity with IBM Cloud.

The Satellite Connector enables the connection between edge environments (often referred to as "satellites") and IBM’s central cloud infrastructure. It facilitates real-time data synchronisation, secure communication, and application deployment at the edge. This reduces latency, improves system responsiveness, and helps ensure that businesses can operate in a more decentralised and efficient manner.

Code Engine components

Mainly, the script provisions three different components, representing the different types of workloads that IBM Cloud Code Engine supports today.

Function - ideal for short running use cases that require low latency - acts as the HTTP proxy for the payload provided by the NGINX server
Job - ideal for long-running, run-to-completion tasks that require a lot of resources for some time - connects to the PostgreSQL database and inserts a record per submitted instance
App - the swiss knife, which is ideal for all sorts of HTTP server use cases that need to scale efficiently - connects to the PostgreSQL database to query stored data and provide it as a JSON payload on an HTTP endpoint

Key Features of IBM Satellite Connector

Seamless Integration with IBM Cloud: The Satellite Connector extends IBM Cloud services to remote locations by creating a hybrid cloud environment. This integration ensures that workloads can be run and managed both on the cloud and at the edge without disruptions or data silos.
Low Latency: One of the key benefits of edge computing is the reduction in latency. By processing data closer to the source, IBM Satellite Connector ensures that real-time insights are available without relying on the often slow transmission speeds of cloud-based processing.
Scalability: The Satellite Connector is designed to scale with the needs of your business. Whether you are managing a small set of edge devices or hundreds of remote systems, IBM’s solution can handle a diverse range of deployments. This scalability allows businesses to grow without the need for costly infrastructure changes.
Security and Compliance: IBM places a strong emphasis on security, and the Satellite Connector is no exception. The solution ensures encrypted communication between cloud environments and edge devices. It also supports compliance with industry-specific regulations, allowing businesses to maintain security standards and protect sensitive data across both edge and cloud systems.
Offline Operation: One of the standout features of the Satellite Connector is its ability to maintain functionality even when the edge device is temporarily disconnected from the central cloud. This feature is critical in remote or mobile environments where continuous connectivity is not guaranteed. Data and workloads are stored and processed locally, and once connectivity is restored, data synchronisation takes place automatically.
Edge Analytics: IBM Satellite Connector allows businesses to perform analytics at the edge. This means that large volumes of data generated by IoT devices, sensors, or other systems can be processed in real-time, reducing the need for data to be sent to the cloud for analysis. By processing data at the edge, organisations can derive faster insights and make quicker decisions.
Simplified Deployment: The Satellite Connector simplifies the deployment of edge workloads by leveraging containerization technology. IBM uses Red Hat OpenShift to orchestrate and manage the containers across cloud and edge environments. This ensures that developers can seamlessly build and deploy applications on both the cloud and at the edge, all from a unified platform.

Use Cases for IBM Satellite Connector

IBM Satellite Connector is highly versatile and can be used across a variety of industries and applications, including:

Industrial IoT (IIoT): In manufacturing environments, IoT devices generate massive amounts of data. IBM Satellite Connector enables real-time data processing on the factory floor, making it easier to monitor equipment performance, detect faults, and take corrective actions promptly.
Smart Cities: Cities are adopting IoT sensors to improve services such as traffic management, waste management, and public safety. IBM Satellite Connector helps to process and analyse the data from these sensors locally, ensuring a quick response to changing conditions while also transmitting relevant data to cloud-based systems for further analysis.
Healthcare: In healthcare, medical devices often need to operate in real-time and with minimal latency. IBM Satellite Connector ensures that critical health data can be processed and analysed at the edge, improving the speed and quality of care, while still ensuring that patient data is securely stored and transferred to cloud systems.
Retail: Retailers use edge computing for applications such as customer behaviour analytics, inventory management, and personalised services. IBM Satellite Connector allows data from IoT devices in retail locations to be processed locally, enhancing the customer experience while maintaining synchronisation with central cloud systems for business insights.
Autonomous Vehicles: In the case of autonomous vehicles, edge computing plays a critical role in processing data from various sensors and making decisions in real-time. IBM Satellite Connector facilitates secure communication between the vehicle’s edge computing system and central cloud resources, ensuring real-time operational intelligence.

Benefits of Using IBM Satellite Connector

Improved Efficiency: By processing data closer to where it is generated, businesses can achieve faster decision-making and reduce the burden on central cloud resources. This leads to enhanced operational efficiency and reduced latency.
Reduced Costs: IBM Satellite Connector allows businesses to optimize their infrastructure by offloading processing tasks to the edge. This reduces the need for significant investments in cloud-based computing resources and network bandwidth, helping to lower overall costs.
Increased Resilience: With offline capabilities and the ability to operate independently of cloud connections, IBM Satellite Connector increases system resilience. This is particularly valuable in remote or challenging environments where network connectivity may be unreliable.
Future-Proofing: As businesses continue to adopt more IoT devices and edge technologies, IBM Satellite Connector helps ensure that their cloud and edge environments remain scalable and adaptable. It supports the growing demands of edge computing while keeping businesses connected to their central cloud services.

In today’s fast-paced digital landscape, where data is generated at the edge and needs to be processed in real-time, the IBM Satellite Connector offers a reliable, secure, and scalable solution to extend the power of the cloud to edge devices. By enabling seamless communication between cloud services and distributed edge environments, it allows businesses to harness the full potential of edge computing while maintaining the benefits of cloud-based infrastructure. Whether it's in manufacturing, healthcare, retail, or any other industry, IBM Satellite Connector is an essential tool for businesses looking to improve efficiency, scalability, and innovation in their operations.

The future of computing lies at the edge, and with the IBM Satellite Connector, businesses are ready to meet that future head-on.

IT IBM

Transforming Finance with Generative AI

15 October 2025, 9:00:01 am AEDT

3 min

In a recent project with a leading media company in Australia, we set out to demonstrate how IBM Watsonx Orchestrate can revolutionise finance operations through the power of Generative AI. The Commercial Finance team, under constant pressure to deliver timely, accurate and insight-rich reports, needed a smarter way to move beyond manual data wrangling and deliver executive-ready outputs in record time.
Alan blog (1)

That’s where WatsonX Orchestrate came in. Unlike traditional BI or workflow automation tools, Watsonx Orchestrate leverages Generative AI to not only automate repetitive tasks but also to interpret, contextualise and generate meaningful outputs. The result is a system that empowers both analysts and executives to act faster, with confidence, while minimising human bottlenecks.

Automating Financial Report Generation

Key Capabilities:

Automated extraction, transformation and loading (ETL) of data from a data warehouse.
Automated generation of third-party and monthly executive summary reports.
AI-driven identification of key events influencing financial outcomes.
Analyst verification loop to ensure accuracy and compliance.

Business Impact:

Reports created in minutes rather than weeks.
Reduced data duplication and inconsistencies.
Analysts free to focus on high-value strategic analysis.
Executives receive timely, validated insights for faster decision-making.

Self-Service Financial Insights

Key Capabilities:

A bespoke AskFinance portal enabling natural language queries.
Secure access aligned with role-based permissions.
Pre-trained CFO scenarios to simulate executive decision contexts.
Integrated visualization tools for interactive reporting.

Business Impact:

Executives gain independence in accessing financial data.
Real-time insights without reliance on BI analysts.
Streamlined reporting across departments and report types.
Forecasting and scenario modeling made simple, accurate and quick.

Use Cases in Action

Producing Monthly YTD Monetisation Reports: Automating the calculations behind key metrics, seamless PowerPoint slide generation, clean, consistent reporting outputs in a standardised format.

Delivering Monetisation Insights: Automated chart creation and AI-driven callouts, generative commentary highlighting anomalies or areas needing attention, a natural language interface to query insights and commentary directly.

Tangible Benefits

~60% ROI: Analysts reallocated to higher-value activities, reducing attrition costs.
~99% efficiency gains: Manual reporting reduced to near-zero.
2 weeks → 10 minutes: End-to-end report creation compressed dramatically.
Improved data quality: Automated reconciliation reduces inconsistencies and errors.
Scalability: Built to handle larger datasets and evolving financial needs.

Beyond Media: Industry Relevance

The use case resonates strongly across industries, such as airlines, where BI Analysts and Finance teams spend significant time manually preparing and reconciling data. In one example, reliance on IBM Planning Analytics was slowing executive decision-making as stakeholders had to wait for analysts to deliver real-time data insights.

Watsonx Orchestrate bridges this gap by delivering:
Automation of complex financial workflows.
Generative insights at scale.
Democratisation of access to financial intelligence.

Curious how Agentic AI could reshape your finance operations? Let’s start a conversation tailored to your requirements.

financial transformation IBM Planning Analytics latest enhancements IBM

8 Forces Reshaping the Future of Finance

8 October 2025, 9:15:00 am AEDT

17 min

The 8 Forces Reshaping the Future of Finance – and How Agentic AI Helps CFOs Lead

Gartner has pin pointed 8 disruptive forces set to fundamentally transform the finance function. These changes—spanning technological advancements, organisational shifts, and regulatory upheavals- pose both risks and opportunities for CFOs. Success will belong to those who leverage Agentic AI, such as WatsonX Orchestrate, and Extended Planning & Analytics, like IBM Planning Analytics, to not merely adapt but to lead the transformation. Finance is standing at a critical juncture. Gartner emphasises that the role of finance is evolving from historical reporting to actively shaping the future of the business.

To lead in this new landscape, CFOs require more than automation. They need Agentic AI, like IBM Watsonx Orchestrate, to operate seamlessly across workflows and Extended Planning & Analysis (xP&A), such as IBM Planning Analytics, to serve as a unified, intelligent source for forecasting, scenario planning, and decision-making.

Together, these platforms form a new operational foundation for finance, striking a balance between cost efficiency, agility, governance, and innovation.

1. A Workforce of AI Agents

The Challenge: By 2027, one-third of enterprise software will embed Agentic AI. Finance tasks once performed manually will be supervised and executed by autonomous agents, driving exponential efficiency.

The Solution:

Watsonx Orchestrate deploys AI agents that autonomously reconcile data, build “what-if” scenarios, or flag exceptions across ERP, CRM, and finance platforms.
These agents don’t just predict outcomes; they act — re-routing approvals, generating reports, and escalating high-value tasks.

The Outcome: Finance staff move beyond low-value reconciliation and report prep, shifting their time to strategy, storytelling, and insight creation.

2. Machine-Dominated Decision Making

The Challenge: By 2028, 70% of finance functions will rely on AI-powered real-time decisioning. Human-led bottlenecks will give way to AI-enhanced scenario modelling and automated choices.

The Solution:

Planning Analytics creates driver-based models that focus on variables that truly move the business (e.g., unit margins, demand drivers, or tariff costs).
Watsonx Orchestrate translates these models into actions, running multiple scenarios in parallel and surfacing recommendations with governance and audit trails.

The Outcome: CFOs can make confident decisions faster — automating routine trade-offs while freeing analysts to stress-test strategy.

3. Rise of Do-It-Yourself Tech

The Challenge: Low-code and no-code platforms will see $41B in spend by 2028, enabling finance to become digitally self-sufficient.

The Solution:

Planning Analytics provides a governed sandbox for FP&A teams to run ad-hoc models, ensuring agility without fragmenting data integrity.
Watsonx Orchestrate acts as the connective tissue, pulling insights into workflows and presenting results conversationally.

The Outcome: True finance self-sufficiency — teams empowered to experiment and run scenarios, without losing enterprise-wide consistency.

4. The End of Transactional Customisation

The Challenge: By 2030, most finance functions will converge on identical transactional processes. Differentiation will come from insights and agility, not customisation.

The Solution:

Watsonx Orchestrate automates repetitive, non-differentiating processes (invoice matching, close cycles, reconciliations).
Planning Analytics ensures finance value lies in insight and foresight, not transactions — embedding real-time planning across the enterprise.

The Outcome: Finance becomes a growth engine, not a cost centre, investing resources in innovation and transformation rather than maintenance.

5. The Lonely Enterprise

The Challenge: Self-service tech adoption (20–50% penetration in 2 years) will push analysis out of finance and into the business.

The Solution:

Planning Analytics creates a living model of assumptions, policies, and KPIs.
Watsonx Orchestrate enables agents to auto-generate compliance reports, simulate regulatory impacts, and escalate issues proactively.

The Outcome: CFOs can stay ahead of regulators, ensuring confidence in disclosures and agility in response, without ballooning compliance costs.

6. Maximally Matrixed Organisations

The Challenge: By 2030, large enterprises will become increasingly matrixed — characterised by complex reporting lines, distributed decision-making, and cross-functional dependencies. While this model allows global scale, it comes at a cost: decision-making slows down, bottlenecks multiply, and finance often becomes the bottleneck rather than the enabler. Gartner predicts a significant reduction in corporate decision speed due to this complexity.

How CFOs Stay Agile with IBM

Watsonx Orchestrate cuts across silos by deploying AI agents that integrate data from disparate systems (ERP, CRM, HR, supply chain). These agents autonomously synthesise inputs, flag bottlenecks, and propose actions without waiting for endless email chains or manual escalations.
Planning Analytics provides a single source of truth across geographies and business units, enabling finance teams to run real-time, driver-based scenarios that reflect the complexities of a matrixed structure.

The Outcome: CFOs regain speed and agility. Instead of being trapped in the complexity of governance and approvals, decisions are powered by cross-system insights, actionable in minutes rather than weeks. Finance evolves into the “accelerator” in a maximally matrixed enterprise.

7. The Finance Talent Crash

The Challenge: The finance profession is heading toward a talent crunch. Demand for digital, analytical, and AI skills is skyrocketing, but the supply of finance professionals with this hybrid capability is scarce. Meanwhile, much of finance talent remains locked in repetitive tasks like reconciliations, reporting, and compliance — jobs that do little to attract or retain the next generation.

How IBM & Octane Mitigate the Crash

Agentic AI (Watsonx Orchestrate) automates routine, manual workflows such as reconciliations, reporting prep, and document processing. By doing so, it frees scarce talent to focus on strategic work: forecasting, scenario planning, and advising the business.
Planning Analytics amplifies finance professionals’ value by equipping them with tools to run advanced models, predictive forecasts, and multi-scenario analysis.
Octane’s AI Adoption Workshops (delivered in partnership with IBM) provide hands-on reskilling for FP&A teams. These workshops ensure finance professionals transition from “spreadsheet operators” to strategic analysts who understand both the business and the AI tools that power it.

The Outcome: CFOs can do more with less. Talent is not just retained but re-energised, focused on high-value activities that align with business growth. The talent gap becomes an opportunity: finance professionals become champions of digital transformation rather than casualties of automation.

8. The Era of Discontinuous Regulatory Change

The Challenge: Regulatory landscapes are evolving faster than ever. From ESG disclosures to cross-border tax regimes and industry-specific compliance requirements, CFOs face a constant barrage of discontinuous, unpredictable regulatory changes. Manual compliance frameworks can no longer keep pace, exposing firms to risk and spiralling costs of control.

How Watsonx Orchestrate & Planning Analytics Support

Watsonx Orchestrate embeds governance and compliance into every workflow. AI agents automatically generate audit trails, monitor transactions for anomalies, and escalate risks before they become issues. Instead of building compliance after the fact, governance becomes native and continuous.
Planning Analytics enables finance to run regulatory impact scenarios in real time — modeling, for example, how a new ESG disclosure requirement might affect capital allocation or how new tax rules impact profitability by geography.
Combined, they give CFOs the ability to adapt instantly, ensuring compliance while keeping costs under control.

The Outcome: Regulatory change becomes less of a disruption and more of a strategic advantage. CFOs can demonstrate resilience to boards and regulators, protecting reputation while ensuring agility.

Adaptive Scenario Planning: Why This Matters Now

The real battleground for CFOs is scenario planning. Traditional methods are too slow for today’s volatility. Adaptive approaches — powered by AI — allow finance leaders to:

Run rolling forecasts updated daily, not quarterly.
Build driver-based models that respond instantly to tariffs, FX rates, or demand shocks.
Generate multiple scenarios in real time and attach clear contingency playbooks.
Show investors not just one “answer,” but a strategic range of preparedness.

Here’s where the synergy between Planning Analytics and Watsonx Orchestrate is critical:

Planning Analytics ensures the data model, drivers, and assumptions are clean, integrated, and ready for real-time updates.
Watsonx Orchestrate enables CFOs to simply ask, “How does a 5% tariff change impact margin by region?” and instantly receive scenario outputs — plus trigger next steps (e.g., adjust budgets, reschedule supplier contracts).

The CFO’s Leadership Imperative

The forces reshaping finance — from matrixed complexity to talent shortages to regulatory turbulence — are daunting. But they also present a unique opportunity. CFOs who embrace Agentic AI today won’t just adapt to disruption; they’ll lead it.

With IBM Watsonx Orchestrate (Agentic AI) and IBM Planning Analytics (xP&A), the Office of Finance can:

Automate: Cut month-end close cycles by 3× while reducing manual errors.
Anticipate: Run real-time “what-if” scenarios with confidence, powered by driver-based models.
Adapt: Stay compliant amid discontinuous regulatory change with embedded audit trails and anomaly detection.
Amplify: Re-deploy scarce finance talent into strategic, growth-focused roles.

The message is clear: The 8 forces will reshape finance — but with Agentic AI, CFOs can lead the disruption, not be disrupted.

The Payoff: Efficiency Meets Innovation

When finance leaders integrate these technologies, the results are dramatic:

99% faster reporting – weeks of manual effort compressed into minutes.
3× faster close cycles – freeing capacity for forward-looking analysis.
60% ROI in Year One – cost savings plus strategic impact.
Cultural transformation – finance staff moving from routine tasks to high-value thinking: experimentation, scenario testing, and strategic advising.

Why Partner with Octane

Transformation isn’t just about technology; it’s about execution. That’s where Octane makes the difference, you’ll hear how leaders from IBM, Rinnai Australia, and Octane are already using AI to unlock efficiency, cut manual reporting by 40+ hours a week, and even accelerate M&A integration. Watch the recording:

AI Adoption Workshops: Delivered in partnership with IBM, Octane’s workshops provide hands-on reskilling for FP&A teams. These ensure finance professionals transition from “spreadsheet operators” to strategic analysts who understand both the business and the AI tools that power it.
Fixed-Price Upgrade Offer: Octane can modernise your xP&A platform on a fixed-price basis after just a 2-hour technical workshop with your team.
AI in Finance Use Cases: In parallel, after a 2-hour strategic workshop with your finance leadership, Octane will deliver two AI use cases tailored to your business — so you see tangible value in weeks, not months.

CFOs are no longer just guardians of cost, they are champions of transformation.

With Watsonx Orchestrate and Planning Analytics, powered by Octane’s delivery expertise, you can accelerate value in 6–8 weeks: modernise your platform, reskill your teams, and embed AI use cases that pay back immediately.

Bring your own Use Case

Bring to life your own use case that generates business value to your organisation with the help of our team of AI experts.

Talk to us!

IBM Planning Analytics latest enhancements IBM

AI Revolution in Finance: Rinnai's 12-Month Transformation to AI-Ready

22 September 2025, 6:23:39 pm AEST

5 min

For years, finance leaders have debated when the time would come to embed Artificial Intelligence (AI) into their operations. That time is no longer in the future. AI has become a business imperative—a driver of efficiency, agility, and competitive advantage for CFOs under mounting pressure to deliver more with less.

Group 41

This is not just theory. It’s happening today. And Rinnai Australia is a standout example.

In just 12 months, Rinnai has modernised its finance platform, embedding IBM Planning Analytics (PA) with support from Octane Software Solutions. The result? A finance function that has freed up 40 hours per week of manual effort, cut reporting cycles from weeks to four days, reduced reliance on spreadsheets by 50%, improved staff morale, and positioned the business for AI-driven forecasting, predictive models, and even faster M&A integration.

This story, and what other CFOs can learn from it, will be at the centre of the upcoming CFO Lunchtime Live Webcast, hosted by CFO Magazine’s James Solomons, featuring Dilend Chawda (Rinnai Australia), Darksha Nadesewaran (IBM ANZ), and Amendra Pratap (Octane Software Solutions).

The Starting Point: Fragmented, Manual, and Complex

Like many growing businesses, Rinnai faced the challenge of fragmented finance processes. Over the years, the organisation had grown in complexity through multiple subsidiaries and M&A activity. Finance was juggling different tools—from Cognos and TM1 to Essbase and Oracle OACS—with high spreadsheet dependency for budgeting and reporting.

The result was:

Long reporting cycles: Subsidiaries took weeks to close, delaying group-level insights.
Spreadsheet chaos: Dozens of versions, late-night reconciliations, and version-control headaches.
3-month budgeting cycles: A bottom-up approach involving countless files, links, and manual inputs.
Staff fatigue and low morale: Finance teams were bogged down in reconciliation and data wrangling rather than analysis.

The system simply wasn’t fit for a fast-moving organisation that needed real-time insights, scenario planning, and agility in the face of market volatility.

The Transformation Journey

1. Building a Modern Platform with IBM Planning Analytics

In mid-2024, Rinnai partnered with Octane SoftwarIn mid-2024, Rinnai partnered with Octane Software Solutions to modernise its finance platform with IBM Planning Analytics. Within weeks, the first modules were live:

August 2024: Group month-end reporting (P&L, Balance Sheet).
October 2024: Product profitability reporting—allocating operating profit down to individual products.
November 2024: Logistic demand planning—12-month SKU-level forecasting.
December 2024: Budget suite for 2025—integrated with sales, costing, workforce, capex, and manufacturing recovery cubes.
June 2025: Subsidiaries fully integrated—Xero trial balances from subsidiaries consolidated into group reporting.

This was a swift, phased deployment that made transformation tangible within months, not years.

2. Quantifiable Benefits Delivered

The outcomes have been both immediate and measurable:

40 hours/week saved: Automation of data consolidation and reporting removed manual wrangling.
Reporting cycle cut to 4 days: Down from weeks for subsidiaries.
50% fewer spreadsheets: Dramatically reducing version errors and reconciliation headaches.
Budgeting accelerated: From a painful 3-month cycle to a streamlined, collaborative process.
Staff morale uplift: Finance staff moved from data entry to analysis, improving job satisfaction and retention.
10% targeted inventory reduction: Through AI-enabled demand planning, reduce warehouse costs while ensuring sales coverage.

The shift has not just been technical—it has been cultural. Finance is no longer the bottleneck, but the enabler.

3. AI Foundations and Next Steps

Rinnai’s transformation has built the foundation for AI adoption. Already, the company is:

Running predictive models for working capital management.
Leveraging IBM PA Assistant (built on watsonx) for natural language queries, commentary automation, and outlier analysis.
Piloting agentic AI assistants (Watson Orchestrate) to automate workflows.
Experimenting with generative AI in Planning Analytics and Oracle—using natural language prompts for “Ask Rinnai” use cases.

Lessons for CFOs

From Rinnai’s journey, there are clear takeaways for other finance leaders:

Be bold and act early: Legacy systems will only get more expensive and harder to fix.
Start with a strong foundation: Modernising reporting, budgeting, and forecasting enables AI to scale.
Quantify benefits: Measure and communicate outcomes like hours saved, cycle time reduced, and morale lifted to keep momentum.
Embed governance and culture: AI adoption requires upskilling teams and embedding robust controls.
Think strategically: Modern platforms don’t just support finance—they enable faster M&A integration, strategic planning, and long-term growth.

The Bigger Picture: Finance as a Strategic Partner

What stands out most from the Rinnai story is how the role of finance has shifted. With automation and AI taking on manual processes, the finance team is now focused on:

Providing forward-looking insights through predictive analytics.
Partnering with the business on strategy, pricing, and resource planning.
Supporting growth through faster M&A integration.
Driving continuous improvement through new AI features and enhancements.

Finance has moved from a reactive scorekeeper to a proactive strategist.

Final Word

The Rinnai story proves that AI in finance is not a distant dream—it’s a present reality. With vision, leadership, and the right partners, CFOs can deliver faster, smarter, and more resilient finance functions that directly enable business growth.

The question is not if finance leaders should adopt AI, but how quickly they can embed it into their organisations.

Don’t wait until it’s harder and more expensive. Join us for this webcast and see how you can start your journey today.

WATCH RECORDING HERE

IBM

IBM Planning Analytics: Debugging and database explorer updates

19 August 2025, 7:56:19 pm AEST

2 min

IBM Planning Analytics has introduced new features that make development and administration tasks much easier. Two of the most impactful improvements are the ability to see variable values while debugging TI processes and the enhanced Database Explorer.

1. Hover to See Variable Values in TurboIntegrator Debugger

Debugging TI processes used to mean adding log statements and rerunning processes just to see variable values. With the new hover help, simply move your mouse over a variable in the debugger, and its value is displayed (e.g., sCube = 'Asset_Input').

✅ Benefit: Makes debugging much faster, eliminates extra logging, and helps you quickly confirm whether variables are behaving as expected.

Figure 1: Hovering over a variable shows its value instantly in the TI debugger

2. Database Explorer: A Smarter Way to Navigate Your Environment

Managing a Planning Analytics server or instance often involves checking how many objects exist—be it cubes, dimensions, processes, chores, or control objects. Previously, administrators and developers had to dig through folders or rely on TI scripts to gather this information. Now, with the Database Explorer, everything is accessible in one clean interface.

Key features include:

Quick Object Counts: Instantly see how many cubes, dimensions, processes, and chores are available.
Process Data Source Types: Displays what data source a process is using (e.g., Cube, ODBC, or 'No data source').
Organised View: Objects are grouped into categories, reducing clutter and making navigation straightforward.
Centralised Actions: Access logs, import/export, manage users, refresh security, or check server version from one place.

✅ Benefit: The Database Explorer improves transparency and efficiency, helping both administrators and developers work faster by providing a unified view of objects and their data sources.

Figure 2: Navigation through the Database Explorer menu

Figure 3: Object counts displayed in Database Explorer

Figure 4: Shows Data source when clicked on Processes

Small Changes, Big Impact

These updates may seem minor, but they greatly improve user productivity. From instantly checking variable values in debugging to exploring databases more efficiently, IBM Planning Analytics is now smarter and more user-friendly.

A developer’s guide: Value_Is_String

6 August 2025, 10:00:00 am AEST

2 min

Value_Is_String is a special, reserved local variable used internally by TurboIntegrator scripts. It is not necessary to explicitly declare or assign it within your script; TM1 automatically manages its value during data processing.

Purpose of Value_Is_String

The main role of Value_Is_String is to determine whether the data source value being processed is a string or a number. This helps in scenarios where different operations are needed depending on the data type, such as:

Formatting output differently
Validating data
Applying specific calculations or conversions

How does Value_Is_String work?

When the data source value is a string, Value_Is_String returns 1.
When the value is a number, the variable returns 0.

This binary indicator makes it straightforward to write conditional logic in your scripts.

Usage in scripts

Since Value_Is_String is a reserved variable, you don’t need to declare it. You simply check its value to determine the data type:

Value_Is_String = N;

If N equals 0, the cell contains a numeric value.
If N equals 1, the cell contains a string.

Example:

Value_Is_String = 0; // Check if the value is a number
If (Value_Is_String = 1);
// Handle string data
Else;
// Handle numeric data

EndIf;

Practical Applications

Suppose you are importing data where some cells contain text, and others contain numbers. Using Value_Is_String, you can craft logic to process each type correctly:

Value_Is_String = N;

If (Value_Is_String = 1);
// Process string data
Else;
// Process numeric data
EndIf;

This ensures your script handles each data type appropriately, avoiding errors or misinterpretations. ### Summary - `Value_Is_String` is a built-in, reserved variable in TurboIntegrator. - It automatically indicates if the current cell data is a string (`1`) or a number (`0`). - It simplifies data type detection, making your scripts more robust and flexible. - No need to define or assign it manually; just check its value in your logic. ### Final Thoughts Using `Value_Is_String` effectively can streamline your data processing workflows in TurboIntegrator, especially when dealing with mixed data types. Understanding this variable helps you build more intelligent scripts that adapt dynamically to the data they process.

Talk to us: media@octanesolutions.com.au

Why IBM ILOG CPLEX still Leads the way in 2025

30 July 2025, 10:00:00 am AEST

3 min

In an age where AI is often synonymous with machine learning, one of the most powerful, but often overlooked, tools in the AI toolbox is optimisation. At the heart of many real-world, high-stakes decisions lies a mathematical engine built to deliver the best possible outcome. And IBM ILOG CPLEX continues to be that engine of choice.

As we move deeper into 2025, one hot trend is hybrid AI, the combination of predictive models with prescriptive optimisation. Why predict what might happen if you can also decide what should happen? That’s where CPLEX shines.

Real-world impact: From supply chains to smart grids

Whether it’s dynamically routing fleets, allocating resources under uncertainty, or scheduling energy consumption during peak hours, organisations are leveraging CPLEX not just as a solver, but as a strategic decision engine.

Here are a few standout use cases:

Retail & E-Commerce: Predicting customer demand using ML, then using CPLEX to optimise fulfilment across a decentralised warehouse network.

Utilities: Combining real-time sensor data with CPLEX-based scheduling to balance load in smart grid systems.

Finance: Creating portfolio allocations that meet regulatory requirements and maximise return, all while adapting to market volatility.

Cloud-native optimisation: Scaling with IBM Cloud Pak for Data

Another major shift? Optimisation in the cloud. IBM's Cloud Pak for Data is helping companies operationalise CPLEX models in ways that were unimaginable just a few years ago. Think seamless integration with data lakes, real-time dashboards, and API-first deployment models.

Why It Matters

As business environments grow more complex, the ability to make data-driven, optimal decisions in real-time becomes a true competitive advantage. CPLEX brings mathematical certainty to uncertain times, and when combined with machine learning, it offers a full-spectrum AI approach that’s both predictive and prescriptive.

Are you exploring optimization as part of your AI strategy? Let’s connect, happy to exchange thoughts on where prescriptive analytics is heading next.

💬Talk to us: media@octanesolutions.com.au

Agentic vs. Classic Watsonx Orchestrate: Transforming Business Automation

23 July 2025, 10:00:00 am AEST

5 min

In today’s fast-moving business environment, automation isn’t just a nice-to-have — it’s essential. IBM Watsonx Orchestrate has already helped many organisations streamline tasks, save time and improve productivity across teams. However, with the introduction of the Agentic version of Watsonx Orchestrate, a noticeable shift is occurring in how companies approach automation.

Unlike the Classic version, which relies on well-defined rules and task sequences, the Agentic approach introduces intelligent agents that understand goals, adapt to changing inputs, and even make decisions in real-time.

So, what’s the practical difference between the two versions? And more importantly, which one should you be using?

Let’s walk through it.

Agentic vs classic

Classic vs. Agentic: A different approach to getting things done

The Classic Watsonx Orchestrate setup works exactly how you’d expect a traditional automation tool to work. You build a step-by-step workflow — for example, “If form is submitted, send it to Person A, then update System B.” It’s reliable, consistent and ideal for tasks that rarely change, like data entry or approval chains.

Agentic Watsonx Orchestrate flips that on its head.

Instead of just executing steps, it starts with a goal — say, “onboard this new employee” — and figures out the best way to achieve that outcome. It plans, adjusts and even asks for help when needed. It’s built to handle the real world, where not everything goes according to plan.

In other words, Classic is scripted. Agentic is strategic.

How They Make Decisions: One Follows Rules, One Thinks for Itself

This is where things start to diverge.

Classic orchestration follows predefined rules. If X happens, do Y. It’s fast and efficient — as long as everything goes as expected.
Agentic Orchestrate, on the other hand, understands context. If the usual input is missing or something unexpected comes up, it doesn’t just fail — it adapts. It learns from interactions and updates its plan as needed.

This kind of dynamic decision-making makes Agentic a better fit for processes where flexibility, personalisation, or real-time problem-solving are required.

When to Use What: It Depends on the Complexity

Not every process needs a thinking agent. Many don’t.

Here’s a simple guide:

Use Case	Best Fit
Leave requests, form approvals	Classic
Employee onboarding	Agentic
Performance reviews, coaching	Agentic
Simple helpdesk responses	Classic or Agentic

If your process is straightforward and repeatable, Classic is a solid choice. But if there’s variation, personalisation, or a need for real-time judgment, Agentic wins hands down.

What’s the Real Impact?

Let’s talk numbers for a second. Organisations that have started using Agentic Orchestrate are seeing:

Up to 50% faster HR processing
30–60% quicker onboarding
25% higher satisfaction from employees using automated support services

That’s because these agents don’t just check boxes — they respond in real-time, offer suggestions and help both employees and managers stay on top of their goals. Think of it like having a digital colleague who understands what you're trying to achieve.

Classic automation answers “What needs to be done?”
Agentic automation answers “Why are we doing this, and what’s the best way to get there?”

How to Choose What’s Right for You

If you’re just starting out with automation, or if you want to get some quick wins by automating simple tasks, the Classic version will serve you well.

But if your goal is to rethink how your business operates — especially in areas like HR, IT, or customer support — the Agentic version is where you’ll start seeing transformative results.

In most cases, the best approach isn’t either/or. It’s both. Use Classic for structured processes and Agentic for the ones that benefit from adaptability and intelligence.

The Future: Smarter, Self-Improving Automation

Agentic Orchestrate isn’t just an upgrade — it’s a complete evolution in how we think about automation. With its ability to learn, adapt and personalise at scale, it opens the door to:

Workflows that improve over time
AI agents that respond based on real-world context
Better support and alignment across teams

This is where automation is headed — not just faster, but smarter.

Ready to Explore What Agentic Orchestrate Could Do for You?

Whether you’re trying to modernise HR, improve service desk responsiveness, or simply reduce the manual load across departments, Agentic Watsonx Orchestrate gives you tools that work like partners — not just programs.

Let’s start a conversation. The future of intelligent work is here — and it’s Agentic.

IBM

Best Practices for maximumports in TM1: Avoiding PAW Connection Failures

15 July 2025, 10:03:17 pm AEST

2 min

Problem Summary:

Users encounter a "Logging on to the server failed" error when opening PAW reports when their }clientproperties have maximumports set to non-zero values (1-10).

Root Cause:

The issue stems from PA Workspace's modern architecture 2.1.5:

The client makes multiple simultaneous requests to the TM1 Server for improved throughput
Each request requires a separate connection
The application uses asynchronous processing to enhance performance and user experience

When maximumports is set too low (1-10), it restricts the number of concurrent connections the client can establish, causing connection failures during report loading.

Recommended Solutions:

Preferred Solution: Set maximumports = 0 (default value)

Removes any artificial limit on connections
Allows the client to establish as many connections as needed
Recommended unless there are specific reasons to limit connections

Alternative Solution: Increase the maximumports value

Try values like 10, 50, or 100
Requires testing to determine the optimal value for your environment
Note that the optimal value may vary based on:

Report complexity

Data payload size

Server capacity

Network conditions

Implementation Considerations:

There's no universal "correct" value for maximum ports, as connection needs vary by usage pattern
Higher values consume more server resources but prevent connection starvation
Lower values conserve resources but may cause performance issues or failures

The documentation suggests that setting maximum ports to 0 (unlimited) is generally the best approach unless specific constraints require limiting connections.

A Developer’s guide: Avoiding File Lock conflicts in TI with AsciiOutput

1 July 2025, 9:48:57 pm AEST

3 min

What is ASCIIOutputOpen

In IBM Planning Analytics (TM1), TurboIntegrator (TI) processes are essential for automating data operations. One of the most useful functions in TI scripting is ASCIIOutputOpen, which allows you to open a file for writing ASCII data. Whether you need to create a new file or append data to an existing one, this function provides the flexibility to control file access and modifications efficiently.

Key Features of ASCIIOutputOpen

Append or Overwrite: Choose whether to overwrite an existing file or add new data to the end.
Shared Read Access: Enable other processes or users to read the file while it’s being written.
Supports Multiple File Types: Works seamlessly with .csv and .txt files, making it ideal for various data export needs.

Syntax Breakdown

The basic syntax for ASCIIOutputOpen is:

ASCIIOutputOpen(FileName, OpeningMode);

Parameters Explained

FileName
- The full path and filename (including extension) where data will be written.
- Example: "C:\Data\Report.csv"
OpeningMode
- A numeric code that determines how the file is accessed.

Mode	Description	Behaviour
0	Overwrite without shared read access	Creates or overwrites the file; no other process can read simultaneously.
1	Append mode without shared read access	Adds data to the end of the existing file; no sharing.
2	Overwrite, shared read access enabled	Overwrites if the file exists; allows other processes to read concurrently.
3	Append, shared read access enabled	Adds data to the end; allows other processes to read the file simultaneously.

Related Functions

For more granular control, you can also use:

FILE_OPEN_APPEND() – Opens a file in append mode.
FILE_OPEN_SHARED() – Opens a file with shared read access.

Combining these functions can provide finer control over file operations.

Practical Examples

Example 1: Overwriting a File with Shared Read Access

If you want to generate a new CSV report (overwriting any existing version) while allowing others to read it:

ASCIIOutputOpen("C:\\Reports\\SalesData.csv", 2);

Example 2: Appending Data with Shared Access

If you need to add new records to an existing file without locking it:

ASCIIOutputOpen("C:\\Reports\\SalesData.csv", 3);

Conclusion

ASCIIOutputOpen is a powerful function in TurboIntegrator that helps manage file exports efficiently. By understanding its different modes, you can ensure seamless data operations—whether you're generating reports, logging data, or integrating with external systems.

Pro Tip: Always verify file paths and permissions before running TI processes to avoid errors!

Have you used ASCIIOutputOpen in your projects? Share your experiences in the comments! 🚀

IBM SaaS on AWS launches India – Supercharge your IBM Planning Analytics cloud journey with Octane!

3 June 2025, 9:09:33 pm AEST

2 min

Big news for Indian enterprises: IBM Planning Analytics as a Service is now officially available on AWS in India. This means faster performance, stronger data sovereignty, and AI-powered insights—all on a scalable cloud platform. At IBM India Services, together with Octane Software Solutions, we're excited to help you harness this opportunity.

AWS launch (1)

Why this AWS India expansion changes everything

Ultra-low latency for real-time planning & analytics
Local data residency for compliance with India's DPDP Act
IBM Planning Analytics on AWS - cloud-powered FP&A at scale
AI-enhanced insights through IBM's agentic AI capabilities

Meet Octane Software Solutions: India’s trusted IBM Planning Analytics experts

Octane Software Solutions is a leading IBM implementation partner, known for deep expertise in financial analytics and enterprise planning. Here’s why Indian enterprises trust Octane:

Home to 3 IBM Champions: Recognised by IBM for outstanding technical leadership and contribution to the global IBM community.
Winner of the 2025 IBM Partner Plus Award: A testament to Octane’s excellence in delivering high-impact, scalable solutions with IBM technologies.
Local expertise for Indian businesses: Deep understanding of compliance, data localisation, and sector-specific requirements in India.
Proven delivery methodology: A structured, risk-free approach that covers everything from cloud migration and solution customisation to training and optimisation.

Why choose IBM + Octane?

Best-in-class technology (IBM)+ 🛠️ Best-in-class implementation (Octane)
End-to-end journey support—from planning to ongoing success
Customisable, AI-powered FP&A solutions built for your needs
Security and compliance assurance for Indian regulatory standards

Take the next step today

Whether your business is:

Planning to modernise financial systems
Exploring AI-powered forecasting and analytics
Seeking to optimise cloud infrastructure and cost efficiency

Let’s make your vision a reality—quickly, securely, and intelligently.

📧 Contact Octane Software Solutions at hello@octanesolutions.com.au
Let’s build a customised roadmap to transform your planning and analytics on IBM Cloud + AWS India.

IBM

Enabling and configuring alerts for IBM Planning Analytics application and server

28 May 2025, 10:07:38 am AEST

6 min

Looking for ways to monitor the health and status of your IBM Planning Analytics (PA) Application and Server?

Here are some methods for automating monitoring and receiving alerts whenever issues arise in the backend of your PA applications.

Before enabling these alerts, it's important to understand the key areas to monitor. Monitoring these aspects ensures your PA applications remain healthy, stable, and optimised for performance.

NOTE: Your Access role should be Administrator to view and perform all the below.

1. Database Health Monitoring

To assess the health of your PA applications and databases, follow these steps:

1. Log in to IBM Planning Analytics Workspace.

2. Navigate to Administration and click Databases.

3. Under Databases, select the desired PA application.

4. On the right-hand side of the page, click on Details to view the status and health metrics.

This provides a quick overview of the database’s performance and any potential issues that may require attention.

Sample Screenshot:

Planning analytics database

You will see various status icons that indicate the current health of the PA application. Here's what each icon represents:

Indicates that the PA application is healthy and running without any issues.

Indicates that the PA application is at risk of moving into a critical state. Proactive attention may be required.

Indicates that the PA application is in a critical state and may potentially
lead to a system failure or downtime if not addressed immediately.

To set up automatic alerts for your PA application:

1. On the right-hand side of the application's detail page, click on Alerts.

2. From there, you can configure the threshold values that will trigger alerts based on system performance or issues.

3. To enable the Alerts, click on the respective button, which changes to indicate it's enabled.

This allows proactive monitoring by notifying you when predefined conditions are met.

Sample Screenshot:

5. We can define the Warning threshold and Critical threshold values based on the size and memory utilised by the PA application under stable conditions.

6. Apart from that, we have options to define Critical Threshold values for factors such as –

Max thread wait time: we can set the Critical Threshold for maximum thread wait time for the respective PA Application to make sure the PA instance is not slowing down as we can kill thread as soon as possible.
Thread in run state: we can set the Critical Threshold to make sure the threads are not in Run state for more than expected in the respective PA Application, which has the possibility of slowing down the server.
Database unresponsive: we can set the Critical Threshold to note if Database/PA application is not responsive which helps us to action it as soon as possible.

6. We can enable the Database Shutdown Alert to enable notifications on the PA Application Stop/Downtime and Start/Restart activity.

7. We can add multiple email IDs to receive the notifications of the enabled Alerts, separated by a comma (‘, ’) in the Notify email IDs text box.

8. Click Apply to save the change made to the Alerts.

2. Agent/PA Server Health Monitoring

To assess the health of your PA Server/Agent, follow these steps;

1. Log in to IBM Planning Analytics Workspace.
2. Navigate to Administration and click Databases.
3. Under Agents, select the desired Agent.
4. On the right-hand side of the page, click on Details to view the status and health metrics.

This provides a quick overview of the database’s performance and any potential issues that may require attention.

Sample screenshot:

To set up automatic alerts for your PA Server:

1. On the right-hand side of the application's detail page, click on Alerts.
2. From there, you can configure the threshold values that will trigger alerts based on system performance or issues.
3. To enable the Alerts, click on the respective button, which changes to indicates it's enabled.

Sample Screenshot:

4. We can define the Warning threshold and Critical threshold values based on the size and memory utilized by the PA application on stable conditions.
5. We can add multiple emails IDs to receive the notifications of the enabled Alerts separated by a comma (‘ , ’) in the Notify email IDs text box.
6. Click Apply to save the change made to the Alerts.

AI IBM Planning Analytics latest enhancements

From compliance to command: How IBM controller empowers the modern CFO

21 May 2025, 9:00:00 am AEST

12 min

As the role of the CFO evolves from financial steward to strategic architect, the expectations have never been higher.

CFOs today are expected to:

Deliver rapid, accurate close cycles.

Ensure compliance across a complex regulatory landscape.

Provide forward-looking insights that drive executive decisions.

Lead finance transformation in a data-driven world.

integration (1)

This shift demands more than spreadsheets and legacy consolidation tools. It calls for intelligent automation, real-time transparency, and strategic control.

This is where IBM Controller steps in as a financial consolidation platform and a strategic lever for modern CFOs.

Why CFOs need more than just a close process

Financial close is no longer just a back-office routine; it’s a board-level priority. Late or inaccurate reporting can erode stakeholder trust, disconnected systems can slow you down, and manual errors open the door to audit risks.

With IBM Controller, CFOs move from reactive to proactive by:

Accelerating the close cycle without compromising accuracy.

Ensuring compliance across multiple GAAPs, IFRS, and local statutory rules.

Reducing manual effort with automated intercompany eliminations, minority interest handling, and ownership calculations.

Maintaining full audit trails to satisfy internal and external audits effortlessly.

Speed + Accuracy + Control = Confidence at Every Close.

From static numbers to strategic narratives

Today’s executive team needs more than a balance sheet. They need answers:

What’s driving margin erosion?

How are subsidiaries impacting group performance?
Where should we invest next?

IBM Controller delivers real-time insights, not just reports. When integrated with IBM Planning Analytics, CFOs can:

Drill down into entity, region, or line-of-business level performance.

Compare actuals vs. budget with clear variance explanations.

Model multiple financial scenarios in the same ecosystem.

This empowers CFOs to shift the narrative from “what happened” to “what’s next”, backed by data that leadership can trust.

Built-in compliance, without the complexity

The regulatory landscape is only getting tougher, taxonomies are changing, ESG requirements are emerging, and cross-border regulations are becoming more intricate.

IBM Controller helps CFOs stay ahead by:

Supporting multi-GAAP reporting and localisation.

Offering governance frameworks with role-based controls and data lineage.

Providing full auditability and traceability of financial data.

Enabling continuous compliance—not just at quarter-end.

This isn’t just regulatory peace of mind, it’s risk mitigation at scale.

Scalability that matches business growth

CFOs aren’t just managing today, they’re preparing for tomorrow. Whether it’s a merger, acquisition, spin-off, or expansion into new markets, the finance function must scale fast.

IBM Controller delivers the agility to:

Seamlessly onboard new entities and the chart of accounts.

Adjust the consolidation logic as ownership structures evolve.

Adapt to new taxonomies, KPIs, and compliance requirements.

With a flexible, rule-driven architecture, IBM Controller grows as your business grows without rewriting your finance playbook.

Cloud-powered, CFO-friendly

Modern CFOs are embracing the cloud not just for IT efficiency, but for strategic advantage.

IBM Controller on Cloud offers:

Lower total cost of ownership, no infrastructure or heavy IT dependency.
Always-on availability, global scalability, and robust security.
Faster upgrades, with immediate access to new features and enhancements.

This allows finance teams to focus on what matters- strategy, performance, and growth, not system maintenance.

One source of truth. Many paths to insight.

With IBM Controller, the CFO gains more than visibility, they gain command.

The solution becomes a single source of truth for the office of finance. Whether you're reporting to the board, regulators, or investors, the numbers always align, no recon, no surprises.

And with powerful integration to tools like IBM Planning Analytics, Cognos Analytics, and Excel, you’re not just seeing the past. You’re shaping the future

The Strategic Payoff: A CFO’s Competitive Advantage

Let’s be clear, IBM Controller isn’t just a tool for the finance team. It’s a strategic platform for CFOs who want to:

Close faster, with fewer errors
Ensure regulatory compliance across borders
Gain real-time visibility into performance
Scale finance operations with business growth
Drive executive decisions with confidence

In an era where data is your most valuable asset and insight is your sharpest edge, IBM Controller puts you at the helm of financial command.

"Be the CFO who leads, not just reports"

You’re not just responsible for the numbers, you’re shaping the story they tell.

With IBM Controller, you gain the speed, clarity, and control to lead with impact every quarter, every decision, every time.

IBM

Unlocking the future of financial planning with IBM Planning Analytics and AI assistant

7 May 2025, 8:30:00 am AEST

7 min

The need for agile financial planning

In today’s rapidly evolving business landscape, organisations face unprecedented volatility, supply chain disruptions, fluctuating demand, inflationary pressures, and geopolitical uncertainties. Traditional financial planning methods, reliant on static spreadsheets and manual processes, are no longer sufficient. Businesses need real-time insights, predictive foresight, and the ability to pivot quickly in response to changing conditions.

Enter IBM Planning Analytics with Watson’s AI Assistant, a cutting-edge solution that combines multidimensional modelling with conversational AI. This solution is transforming how enterprises approach budgeting, forecasting, and performance management. This isn’t just an incremental improvement; it’s a paradigm shift in financial planning and analysis (FP&A).

Future of finance (1)

What is IBM Planning Analytics with AI Assistant?

IBM Planning Analytics, built on the powerful TM1 engine, has long been recognised for its:

In-memory computing for lightning-fast calculations
Multidimensional modelling for complex scenario analysis
Seamless Excel integration for user-friendly analytics

Now, with the AI Assistant, the platform goes beyond traditional analytics by embedding Watson-powered artificial intelligence directly into the planning workflow. This AI-driven co-pilot enables users to interact with their data conversationally, uncovering insights that would otherwise require deep technical expertise.

How does the AI assistant work?

Think of it as a data-savvy colleague who can:

Answer complex financial questions in natural language (e.g., *“Why did Q2 profitability decline in the Asia-Pacific region?” *)
Automatically detect anomalies and suggest corrective actions
Generate predictive forecasts based on historical trends and external factors
Run instant what-if scenarios (e.g., “What happens if raw material costs increase by 15%?”)

Unlike traditional BI tools that require users to write queries or build complex models, the AI Assistant democratizes analytics, making advanced insights accessible to finance teams, business leaders, and operational managers alike.

Key Benefits of IBM Planning Analytics with AI Assistant

Natural language queries – No coding required

Gone are the days of struggling with MDX or complex formulas. Users can simply ask questions in plain English, such as:

“Show me sales performance by region last quarter.”
“Why are operating expenses higher than forecast?”
“Predict next quarter’s revenue based on current trends.”

The AI Assistant interprets intent, retrieves relevant data, and presents answers in interactive dashboards, charts, or drill-down reports, eliminating the need for IT intervention.

Real-time cognitive insights

The AI Assistant continuously monitors data patterns, flagging anomalies and suggesting corrective actions before they escalate into bigger issues. For example:

“Inventory turnover in the Northeast is 20% below target, recommend adjusting procurement orders.”
“Marketing spend is exceeding budget due to higher-than-expected digital ad costs.”

This proactive intelligence helps businesses stay ahead of risks and opportunities.

Instant scenario modelling & what-if analysis

Strategic planning no longer takes weeks. With AI-powered scenario modelling, finance teams can:

Test multiple business conditions in seconds (e.g., “What if interest rates rise by 2%?”)
Compare outcomes side-by-side
Adjust assumptions dynamically

This capability is invaluable for risk management, capital allocation, and growth planning

Democratised analytics for cross-functional teams

The AI Assistant breaks down data silos, allowing:

Finance teams to explore profitability drivers
Sales leaders to assess pipeline impacts
Supply chain managers need to optimise inventory levels

By making analytics self-service, organisations reduce dependency on IT and accelerate decision-making.

Explainable AI: Not just predictions, but reasons

Many AI tools provide forecasts but fail to explain why a trend is occurring. IBM’s AI Assistant goes further by:

Highlighting key drivers behind variances (e.g., “Q3 revenue dipped due to delayed product launches in Europe.”)
Suggesting actionable recommendations (e.g., “Consider reallocating budget to high-growth markets.”)

This transparency builds trust in AI-driven insights.

Real-world use case: Transforming a CFO’s workflow

Imagine a CFO who starts their day with an AI-generated briefing:

“Good morning. Last week, operating margins in the retail division fell by 8% due to higher logistics costs. Supplier X increased rates by 12%. Recommended actions: Renegotiate contracts or explore alternative vendors. Additionally, Q4 demand forecasts suggest a 15% increase, consider ramping up production.”

This level of automated, intelligent guidance enables faster, more informed decisions, reducing planning cycles from weeks to hours.

Seamless Integration with Existing Tools

IBM Planning Analytics doesn’t operate in isolation. It integrates with:

Microsoft Excel (for familiar spreadsheet-based planning)
Power BI & Tableau (for advanced visualisations)
ERP systems (SAP, Oracle, NetSuite) for real-time data synchronisation

The AI Assistant acts as a universal translator, bridging gaps between disparate systems and delivering unified insights.

The future of work: Augmented, not automated

A common fear is that AI will replace human jobs. However, IBM Planning Analytics is designed to augment—not replace, FP&A teams.

AI handles data processing, anomaly detection, and predictive modelling.
Humans focus on strategy, stakeholder collaboration, and creative problem-solving.

The result? Higher productivity, deeper insights, and more strategic impact.

Is your organisation ready for AI-driven planning?

Adopting IBM Planning Analytics with AI Assistant requires:

A shift from manual to automated processes
Trust in data-driven decision-making
Willingness to experiment with AI-powered insights

For companies that embrace this transformation, the rewards are substantial:

Faster, more accurate forecasts
Proactive risk mitigation
Empowered teams with self-service analytics

Start your AI-powered planning journey

The best way to experience the power of IBM Planning Analytics with AI Assistant is to run a pilot project. Begin with a single department, finance, sales, or operations and measure the impact.

Your planning process will never be the same.

📅 Ready to explore how AI can revolutionise your financial planning? Contact us for a demo and learn more.

Octane wins 2025 IBM Partner Plus award in APAC: How agentic automation is shaping the future of work

9 April 2025, 9:00:00 am AEST

5 min

In a world where speed, scale, and trust define success, Octane has emerged as a leader in enterprise AI-driven transformation. Honoured with the 2025 IBM Partner Plus Award in APAC for automation, Octane’s groundbreaking use of IBM Watsonx Orchestrate is setting a new benchmark for how intelligent automation can empower business users and scale human productivity.

The Award: Increasing performance through automation

IBM Partner Plus Awards_Automation_Geo Winners Tile_APAC_LinkedIn

The IBM Partner Plus Award for Automation celebrates business partners who are delivering new levels of performance with speed, scale and security, enabling systems, business processes, and people to be more efficient.

For Octane, this means:

Speed: Accelerating workflows by over 70% through AI orchestration.
Adaptability: Designing solutions that flex with business demands.
Security: Ensuring compliance through enterprise-grade automation guardrails.

“This award reinforces our belief that automation should be intuitive, intelligent, and human-first,” said Amendra Pratap, Managing Director. “Our collaboration with IBM brings this vision to life.”

IBM Watsonx Orchestrate: A new paradigm in intelligent automation

Watsonx Orchestrate is IBM’s enterprise-ready solution that helps create, deploy, and manage AI assistants and agents. It blends AI and workflow automation, enabling users to interact with systems using natural language prompts, representing a powerful step toward fully autonomous agentic AI systems. It enables multi-step, goal-driven task orchestration and integration with existing business systems, connecting to multiple proprietary and third-party AI models and automation tools.

How Octane leverages Watsonx Orchestrate:

Skill-based AI execution: Prebuilt “skills” automate repetitive actions—like sending emails, updating records, or scheduling interviews.
No-code integrations: Plug-and-play connections to enterprise tools like SAP, Salesforce, and Workday.
Conversational interface: Users trigger complex workflows through Slack, Teams, or email with simple prompts.

“Watsonx Orchestrate isn’t just for building chatbots—it’s an AI-powered teammate,” says Amendra Pratap, Managing Director, “Watsonx Orchestrate is your next hire. By orchestrating tasks across various assistants, agents and systems, it helps boost workforce efficiency and reduces manual load by surfacing the right tools when you need them."

Real-world impact:

Fiji Airways Report Generation with PA x orchestrate - PoR_10-1

What we achieved:

Fiji Airways Report Generation with PA x orchestrate - PoR_11

What sets Octane apart: Augmented intelligence, not just automation

Traditional automation follows rules. Octane uses AI to understand goals, context, and next best actions.

With Watsonx Orchestrate, Octane delivers:

Adaptive workflows: Adjusts based on real-time data (e.g., re-routing approvals during outages).
Multi-agent collaboration: Skills collaborate (e.g., sentiment analysis + ticket escalation).
Continuous learning: Models improve from feedback to streamline operations over time.

“We don’t just automate tasks—we augment thinking,” says Steny Sebastian, Principal - Data and AI Platforms. “That’s how we deliver smarter outcomes, not just faster ones.”

Ready to orchestrate intelligence into every workflow?

Explore how Octane’s award-winning AI solutions can help you scale with confidence.

Learn how advanced your organisation is with AI adoption and how Orchestrate can help. - Take the next step. Try IBM Watsonx Orchestrate at no cost, or book a consultation with an expert

Learn more about the IBM Partner Plus Awards: https://www.ibm.com/partnerplus/awards

#AgenticAI #EnterpriseAutomation #OctaneSolutions #WatsonxOrchestrate #IBMPartnerPlus #DigitalTransformation

AI IBM

Embracing the future with AI: My thrilling first week at Octane Software Solutions

25 March 2025, 8:30:00 am AEDT

11 min

There’s nothing quite like starting a new role at the forefront of innovation. My first week at Octane Software Solutions was nothing short of electrifying—the highlight was a full-house customer event buzzing with energy, visionary ideas, and the promise of AI-driven transformation.

The focus?

 IBM AI Platform, Watsonx Orchestrate, is poised to redefine how businesses harness automation, AI agents, and predictive intelligence to unlock unprecedented efficiency. Let me take you through this exhilarating journey and why Watsonx Orchestrate, paired with Octane’s expertise, is the future of work.

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As we close out 2024, a year that has been revolutionary for AI adoption, let’s pause to reflect on the data driving this transformation. Companies have spent 2023-2024 experimenting with generative AI, deploying AI assistants, and running pilots, many of which have evolved into concrete plans for 2025. Now, the focus shifts to finding the right partner to turn these blueprints into reality.

These statistics aren’t just metrics; they prove that AI is fulfilling its promise to amplify productivity while elevating outcomes. Here’s how Watsonx Orchestrate’s AI agents are reshaping enterprises:

Enhanced User Experience

AI agents deliver intelligent, multi-turn conversational experiences that solve complex tasks seamlessly. For instance, integrating Watsonx Orchestrate with tools like IBM Planning Analytics (TM1) allows finance teams to automate data reconciliation while maintaining compliance.
Reduced Total Cost of Ownership (TCO)

By leaning on AI to automate tasks at scale, enterprises cut costs while boosting efficiency. Watsonx Orchestrate’s pre-built Skills and low-code studio let businesses extend existing Gen AI investments—like chatbots or co-pilots—without overhauling systems.
Agility & Future-Proof Flexibility

AI agents enable organisations to pivot rapidly as markets shift. With Watsonx Orchestrate’s autonomous orchestration, businesses adapt workflows in real-time, whether rerouting customer inquiries during peak demand or updating financial forecasts using TM1.

If below is what you are thinking:

How do you integrate AI agents into your existing digitised workflows?
How do you maximise ROI from current AI tools?
How do you retain control as AI evolves?

Octane: Is Your Partner for Scaling Customer-Centric AI

While many providers offer AI tools, Octane stands apart as a force for customer-centric innovation. Here’s why:

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We don’t believe in one-size-fits-all solutions. Together with IBM, Octane’s team works side-by-side with clients to:

Map AI use cases to your unique customer journey.
Integrate Watsonx capabilities with niche tools
Prioritise ethical AI practices, ensuring transparency and trust at every interaction.

Octane: Delivering Real-World Impact with Watsonx Orchestrate—An Airline’s Journey to AI-Driven Intelligence

Let’s cut through the hype and dive into a tangible example of how IBM Watsonx Orchestrate, implemented by Octane, transformed operations for a global airline—a case study that exemplifies the platform’s power to turn data chaos into strategic clarity.

The Challenge: Manual Mayhem in Business Intelligence

The airline’s Business Intelligence (BI) and Finance teams were drowning in manual processes:

2-3 days wasted monthly on report generation, with analysts manually cleaning, reconciling, and validating data in IBM Planning Analytics (TM1).
Knowledge bottlenecks: Executives relied on BI teams for real-time insights during critical meetings, creating delays and frustration.
Human errors: Manual calculations led to costly rework, while commercial decisions were stalled by a 3-week data validation cycle.

The stakes? Missed deadlines, strained resources, and executives flying blind in a competitive market.

The Solution: Watsonx Orchestrate in Action

Octane partnered with the airline to integrate Watsonx Orchestrate with their existing IBM Planning Analytics deployment. In just two weeks, we automated workflows and unleashed AI-driven efficiency:

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Automated Data Cleaning & Reconciliation

AI-Powered Automation:

Manual data cleaning reduced from 2-4 days/month to minutes.

Data reconciliation slashed from 3-4 hours/month to 1 minute.

Self-Correcting Workflows:
Watsonx Orchestrate’s AI agents flagged inconsistencies, auto-corrected errors, and validated datasets, ensuring 99% accuracy in financial reports.

Empowering Executives with NLP-Driven Insights

Natural Language Queries:
Executives could now ask, “Show me Q3 revenue trends vs. forecasts” in plain language. Watsonx Orchestrate generated real-time insights, reducing reliance on BI teams by 90%.

Faster Decisions:
Monthly reports that once took 2-3 days were generated in 10 minutes, accelerating commercial decisions from weeks to hours.

Eliminating Knowledge Silos

Democratized Data Access:
By codifying tribal knowledge into AI workflows, the airline mitigated key-person risk and ensured continuity during staff turnover.

Scalable Governance:
Octane embedded compliance checks into automated processes, aligning with IBM’s enterprise-grade LLMs for audit-ready outputs.

Business Outcomes: From Friction to Flight

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"With Octane and Watsonx Orchestrate, we’re not just surviving data chaos—we’re soaring above it." - Fortune 500 Airline Client

Three Pathways to AI Transformation

Test-drive Watsonx Orchestrate on our dedicated platform
Client Briefing: Dive deep into a 2-4 hour session to align AI strategy with your goals.
Pilot Program: Co-develop a 1-4 week proof-of-concept with Octane’s AI engineers.

The Future is Autonomous—Let’s Build It Together

Reflecting back on the event, the energy in the room was infectious. Attendees left inspired by Watsonx Orchestrate’s ability to blend autonomous AI with human ingenuity. By automating the mundane, enhancing precision, and scaling seamlessly, this platform isn’t just a tool—it’s a productivity revolution.

As I begin my journey with Octane, I’m energised by the possibilities. Whether you’re optimising finance with TM1, Anaplan, transforming employee productivity with SAP, or reimagining customer service with Salesforce / ServiceNow, IBM Watsonx Orchestrate—powered by Octane—is your catalyst for growth. The future of enterprise productivity isn’t just automated—it’s augmented. With AI Agents handling the grind, your team can focus on what humans do best: innovating, strategising, and delivering exceptional value.

Ready to turn your 2025 AI vision into reality?

Contact Octane to discover how Watsonx Orchestrate can accelerate your journey—with the stats to back it up.

Steny Sebastian
Principal - Data and AI Platforms
Octane Solutions 🗓️ Book me
https://www.octanesolutions.com.au/

AI IBM

IBM Planning Analytics AI assistant - revolutionising business planning with artificial intelligence

19 March 2025, 9:00:00 am AEDT

7 min

In today’s fast-paced business environment, companies are constantly looking for ways to streamline their operations, improve decision-making, and stay ahead of the competition. One of the tools that has gained significant attention in the world of business intelligence and analytics is IBM Planning Analytics, which harnesses the power of AI to enhance financial planning, forecasting, and reporting. One of the standout features of IBM Planning Analytics is its AI Assistant, an innovative tool that leverages artificial intelligence to provide smarter, more efficient planning and analytics capabilities.

AI in finance and watsox orchestrate-1

In this blog, we’ll dive into the key features of the IBM Planning Analytics AI Assistant and explore how it is transforming business planning for organisations around the world.

What is IBM Planning Analytics AI Assistant?

IBM Planning Analytics is a cloud-based solution designed to help businesses automate their planning, budgeting, forecasting, and analysis processes. The AI Assistant embedded within the platform brings cognitive capabilities to the table, making it more intuitive and user-friendly.

The AI Assistant uses natural language processing (NLP) and machine learning algorithms to understand and respond to user queries in plain language, enabling business users—whether financial analysts, planners, or executives—to interact with the system more naturally. Instead of relying on complex formulas or spending hours running reports, users can simply ask questions like, "What was our sales growth in Q3?" or "How much did our expenses increase year-over-year?" The AI Assistant then processes these requests and provides quick, data-driven insights.

Key Features of IBM Planning Analytics AI Assistant

Conversational analytics

One of the most impressive features of the AI Assistant is its ability to enable conversational analytics. Traditionally, getting insights from business intelligence tools involved navigating through multiple layers of data, setting up reports, or writing complex queries. The AI Assistant eliminates this complexity by allowing users to ask questions in natural language, just like they would talk to a colleague or consultant.

For example, a user can ask, "What were our sales for last quarter?" and the AI Assistant can instantly pull up relevant data, graphs, or reports. This conversational interface makes it easier for non-technical users to engage with analytics and access valuable insights without having to be data experts.
Data-driven decision-making

The AI Assistant doesn’t just provide static answers—it actively helps users analyse trends, identify anomalies, and make data-driven decisions. For instance, the Assistant can compare historical data, identify seasonal patterns, and even suggest potential adjustments to forecasts based on changing market conditions. This empowers decision-makers to quickly assess different scenarios and make informed choices.

Additionally, the Assistant can provide context behind the data, such as explanations of why certain numbers are trending upward or downward. This deeper level of understanding enables organisations to plan with greater confidence.
Predictive analytics and forecasting

In addition to assisting with retrospective analysis, the AI Assistant is also equipped to help users with predictive analytics. Using historical data, market trends, and other variables, the Assistant can generate forecasts for various business aspects like sales, revenue, and operational costs.

For instance, planners can ask the AI Assistant, "What is the projected revenue for the next quarter based on current trends?" The Assistant then leverages machine learning models to provide accurate, forward-looking forecasts. By incorporating AI-driven insights, businesses can improve their planning accuracy and reduce the risks associated with manual forecasting.
Automated insights and recommendations

One of the standout benefits of AI in business planning is its ability to go beyond simple reporting. The IBM Planning Analytics AI Assistant is capable of delivering automated insights and recommendations that are tailored to the needs of the organisation. By analysing past performance, the Assistant can highlight areas of opportunity or potential risk that may require attention.

For example, if expenses are increasing faster than revenue, the Assistant might recommend strategies for cost-cutting or optimising operations. These automated recommendations allow planners and analysts to quickly address potential issues and capitalise on emerging opportunities.
Seamless integration with IBM Planning Analytics Workspace

The AI Assistant is fully integrated with the IBM Planning Analytics Workspace, which is the central hub for business users to manage and analyse data. This integration ensures that users have a smooth experience when interacting with their data, whether they are leveraging the AI Assistant for ad-hoc analysis or using the broader tools available in Planning Analytics for long-term strategic planning.

The seamless integration between the Assistant and the workspace also means that businesses can continue to rely on traditional data management and reporting workflows while taking advantage of AI-powered insights without disruption.

Benefits of IBM Planning Analytics AI assistant

AI in finance and watsox orchestrate

Faster Decision-Making

The AI Assistant accelerates decision-making by delivering insights in real-time. Users can ask questions and get answers instantly, without having to manually sift through large datasets or run complex queries. This speeds up planning cycles and ensures that decisions are based on the latest data.
Empowerment of Business Users

With the AI Assistant, business users who may not have deep technical expertise can now access analytics and make informed decisions. This democratisation of data ensures that all teams—finance, marketing, operations—are equipped to contribute to planning processes and drive organisational success.
Reduced Errors

Since the AI Assistant uses machine learning models to predict and analyse data, the likelihood of human error in forecasting and planning is significantly reduced. Automated insights and recommendations are based on sophisticated data analysis, helping to eliminate mistakes caused by manual data handling.
Scalable Insights Across Teams

The AI Assistant enables businesses to scale their analytics capabilities across teams and departments. Whether a team is working on financial forecasts, sales targets, or operational efficiencies, the Assistant can be used to generate insights that are relevant to each department’s specific goals and objectives. This scalability ensures that AI-powered decision-making benefits the entire organisation.

Real-World Use Cases

Finance Teams

For finance teams, the AI Assistant is a game-changer in managing budgets, forecasting, and scenario planning. It can quickly identify deviations from expected results, recommend corrective actions, and forecast the financial outlook based on real-time data.
Sales and Marketing Teams

Sales and marketing teams can use the Assistant to gain quick insights into customer behaviour, sales trends, and marketing ROI. By understanding which campaigns are driving results and which aren’t, they can adjust strategies on the fly and optimise their efforts.
Operations and Supply Chain

Operations managers can use the AI Assistant to forecast demand, optimise inventory, and predict potential supply chain disruptions. By understanding these dynamics earlier, businesses can mitigate risks and improve operational efficiency.

Conclusion

The IBM Planning Analytics AI Assistant represents a significant leap forward in the world of business analytics. By combining artificial intelligence, natural language processing, and predictive analytics, it transforms the way businesses plan, forecast, and make decisions. With its ability to provide faster insights, automate recommendations, and empower users across the organisation, the AI Assistant is not just a tool—it’s a strategic asset that helps businesses become more agile and data-driven in their operations.

As businesses continue to face increasingly complex challenges, tools like IBM Planning Analytics AI Assistant will become indispensable for navigating the future of planning and decision-making.

Ready to take AI to the next level? Talk to us!

AI IBM WatsonX Orchestrate

Dipping your toes into AI in Finance with Watson Orchestrate: A Step-by-Step Journey

26 February 2025, 8:00:00 am AEDT

7 min

The world of finance is evolving rapidly, and AI is no longer a futuristic concept—it’s a practical tool that can transform how finance teams operate. But for many organisations, the idea of integrating AI into their workflows can feel overwhelming. Where do you start? How do you ensure success? The answer lies in taking a gradual, strategic approach. With Watson Orchestrate and IBM Planning Analytics, you can start small, prove the value, and confidently scale your AI initiatives. At Octane, we guide you through this journey, from exploring use cases to delivering impactful projects.

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Why Start Small with AI in Finance?

AI has the potential to revolutionise finance by automating repetitive tasks, enhancing decision-making, and improving accuracy. However, diving headfirst into a full-scale AI implementation can be risky. Starting small allows you to test the waters, build confidence, and demonstrate tangible results before committing to larger investments. This is where Watson Orchestrate shines—it’s designed to integrate seamlessly with your existing tools, like IBM Planning Analytics, and automate specific tasks without disrupting your workflows.

Step 1: Explore Use Cases with Octane’s Workshops

The first step in your AI journey is identifying where it can add the most value. We work closely with IBM client engineering team and run interactive workshops to help you explore potential use cases for Watson Orchestrate within your finance team. These workshops are designed to:

Understand Your Pain Points: We work with your team to identify repetitive, time-consuming tasks that are ripe for automation, such as data consolidation, report generation, or budget reconciliation.

Brainstorm Solutions: Together, we brainstorm how Watson Orchestrate can address these challenges, leveraging its AI capabilities to automate processes and enhance efficiency.

Prioritise Opportunities: Not all use cases are created equal. We help you prioritise the ones that offer the quickest wins and the highest impact.

Step 2: Prove the Value with a Proof of Concept (POC)

Once we’ve identified promising use cases, the next step is to validate them through a Proof of Concept (POC). A POC allows you to see Watson Orchestrate in action, delivering real results in a controlled environment. Here’s how it works:

Define Success Metrics: We work with you to define clear objectives and success metrics for the POC, ensuring that the results are measurable and aligned with your goals.

Build and Test: Our team builds the POC, integrating Watson Orchestrate with IBM Planning Analytics to automate the selected use case. We test the solution rigorously to ensure it meets your requirements.

Evaluate Results: After the POC, we evaluate the results together. Did it save time? Improve accuracy? Enhance productivity? These insights help you decide whether to move forward with a full-scale implementation.

Step 3: Deliver the Project and Scale

If the POC demonstrates value, we move into the  project delivery phase. Our team works closely with yours to implement the solution, ensuring it’s tailored to your specific needs and integrated seamlessly into your workflows. Once the initial project is delivered, you can scale the solution to address additional use cases, gradually expanding the role of AI in your finance operations.

Real-World Impact: A Gradual Approach to AI

Many organisations have successfully adopted AI in finance by starting small and scaling strategically. For example, an airline participated in one of Octane’s workshops and identified report generation as a key pain point. Through a POC, they automated the process using Watson Orchestrate, reducing the time required from 2 days to just 30 minutes. Encouraged by the results, they expanded the solution to automate budget reconciliation, achieving even greater efficiencies.

Why Choose Octane?

At Octane, we specialise in helping organisations like yours navigate the complexities of AI adoption in Finance teams. Our phased approach—starting with workshops, moving to POCs, and then delivering projects—ensures that you can dip your toes into AI without taking on unnecessary risk. We bring deep expertise in Watson Orchestrate and IBM Planning Analytics, along with a commitment to delivering measurable results.

Take the First Step Today

AI is no longer a distant dream—it’s a practical tool that can transform your finance team. By starting small with Watson Orchestrate and IBM Planning Analytics, you can explore the potential of AI, prove its value, and scale your initiatives with confidence. Ready to get started? Contact Octane today to schedule a workshop and begin your AI journey. Email us at  media@octanesolutions.com.au  to learn more.

The future of finance is AI-powered, and the journey starts with a single step. Let Octane guide you every step of the way.

AI IBM WatsonX Orchestrate

Unlocking the power of IBM Planning Analytics with execute HTTP request

5 February 2025, 6:25:54 pm AEDT

4 min

Today, we’re excited to explore a game-changing function that enhances the versatility of your Planning Analytics platform. Imagine a tool that not only streamlines your data processes but also connects your Planning Analytics seamlessly with external systems. This innovation allows you to execute HTTP requests directly within your TurboIntegrator (TI) processes, transforming your Planning Analytics into an integral part of your interconnected ecosystem. Join us as we delve into the possibilities this function brings and how it can elevate your data management strategies to new heights.

Why Execute HTTP Request is a Game-Changer

What makes this function truly versatile is its ability to connect with any external system that supports APIs. The only limit is your imagination and the capabilities of the APIs you wish to connect to. Integrating Planning Analytics with external systems allows developers to break free from traditional limitations and extend the functionality of their applications.

This session will include practical demonstrations of several use cases that highlight the power of the Execute HTTP Request. By the end, I hope to inspire you to explore how you can leverage this function to enhance your TM1 applications and workflows.

Demo 1: Hot Promotion of Objects Between Instances

Let's dive into our first demonstration on how to perform hot promotion of objects from one instance to another. Traditionally, migrating objects between instances involved shutting down the target server. However, using the Execute HTTP Request, we can do this in real-time.

Setting Up the TI Process:
- Open the workbench in your workspace and create a new TI process.
- Declare necessary constants and set your source and target instances (e.g., SmartCo to Demo Server).
Use of HTTP Execute Request:
- Fetch dimensions from the source instance and check for their existence in the target instance.
- For non-existing dimensions, save them as JSON files and use the HTTP Execute Request to migrate them to the target instance.

Let’s execute this process! Once completed, you’ll see that the dimensions have been successfully migrated.

Demo 2: Executing a Process Across Instances

Next, we'll demonstrate the ability to execute a process from one instance in another:

Migrate TI Processes:
- Similar to dimension migration, retrieve the TI process (like Sample TI) from the source instance and save it as a JSON file.
Execute the TI Process:
- Use the Execute HTTP Request to trigger execution from the target instance while utilising its response to capture status codes and log outputs.

After running this process, you should see that both the processes have been migrated and executed successfully.

Demo 3: Loading Currency Conversion Rates

In this demo, we will load real-time currency conversion rates from a website using its API:

Call the API:
- Set up an HTTP GET request to retrieve USD conversion rates.
Extract and Utilise Data:
- Capture the JSON response and extract required currency rates using JSON functions.

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Run the process, and you will observe the real-time conversion rates being fetched and displayed.

Demo 4: Sending Teams Notifications

Next, I’ll show you how to send automated notifications to Microsoft Teams:

Integrate with Microsoft Power Automate:
- Set up a Power Automate flow to send notifications.
Trigger Notification from System:
- Use Execute HTTP Request to trigger alerts in Teams based on process execution results.

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After execution, you should see notifications appear in your Teams channel.

Demo 5: Sending Emails via HTTP Requests

Finally, we'll explore how to send emails:

Power Automate for Email Notifications:
- Again, set up Power Automate to manage email sending through appropriate HTTP requests.
Dynamic Email Content:
- Utilise dynamic fields for subject and body based on execution results.

After executing this process, you will receive the email in your mailbox.

Conclusion

Today, we have unlocked the extensive capabilities of the Execute HTTP Request function in IBM Planning Analytics. We showcased hot promotion between instances, cross-instance process execution, real-time data fetching, as well as integration with Microsoft Teams and email notifications.

Thank you all for attending this session. I hope you found it beneficial and feel inspired to explore the functionality of Planning Analytics further. Let’s move toward a more integrated and dynamic future in our analytics processes!

PAW IBM Planning Analytics latest enhancements

Balancing year-end demands: Top 5 stress-free approach for Finance Teams this festive season

17 December 2024, 8:30:00 am AEDT

6 min

The holiday season is a time for joy, relaxation, and quality time with loved ones. However, for many finance teams, it’s also a period of intense activity. Year-end closings, budgeting, and reporting deadlines can pile up, creating a stressful and demanding environment.

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Imagine spending your holiday season worrying about potential system outages, data load failures, or urgent user queries. It’s a scenario that can disrupt your well-deserved break and compromise your team’s productivity.

Leverage Dedicated TM1 Support During the Holidays

Engage a reliable TM1 support partner to handle system maintenance, troubleshooting, and year-end processes so your team can enjoy a well-deserved break without stress.

Automate Routine Tasks and Processes in TM1

Use TM1’s automation capabilities to schedule recurring tasks like data loads, reconciliations, and report generation, ensuring everything runs smoothly while minimizing manual effort.

Ensure Proactive Monitoring and Downtime Prevention

A TM1 support team can proactively monitor your environment, identify potential issues before they escalate, and ensure critical systems remain up and running during the festive season.

Outsource Last-Minute Reporting and Forecasting Support

Avoid scrambling to meet year-end deadlines by outsourcing TM1 reporting tasks to a team that can handle changes, corrections, and urgent requests with expertise and speed.

Plan Ahead with a Holiday Support Coverage Model

Partner with a TM1 managed services provider who offers holiday-specific coverage, ensuring your team has access to skilled resources when needed, without disrupting workflows or personal time.

Why TM1 support (Octane Blue) is the Perfect Solution

That’s where Octane Blue comes in. Our comprehensive support service is designed to alleviate your holiday stress and ensure business continuity. With 40 hours of dedicated support, you can rest easy knowing that your TM1 environment is in expert hands.

What Does Octane Blue Offer?

Proactive System Monitoring: Our team will keep a watchful eye on your TM1 environment, identifying and resolving potential issues before they escalate.
Rapid Incident Response: Should any issues arise, our experienced support engineers will be on hand to diagnose and fix them promptly.
Data Load and Reconciliation Support: We’ll assist with data load processes, ensuring accurate and timely data integration.
Security and Access Management: Our team will help maintain the security of your TM1 environment and manage user access rights.
Security and Access Management: Our team will help maintain the security of your TM1 environment and manage user access rights.
Technical and Functional Support: We’ll provide expert guidance on a wide range of TM1 topics, from technical troubleshooting to functional best practices.
User Support: Our team will be available to assist your users with any questions or issues they may encounter.

Frequently Asked Questions

How many hours of support are included in Octane Blue?

40 hours.

What happens if I don’t use all 40 hours during the festive season?

Unused hours will be rolled over for one additional month.

Do I need to be an existing Octane client to sign up for Octane Blue?

No, Octane Blue is available on any TM1 site.

Do I need to sign a long-term contract?

No, you can purchase Octane Blue on a one-time basis or as needed.

How much does Octane Blue cost?

https://www.octanesolutions.com.au/tm1-support#switch

How do I purchase Octane Blue?

Purchase now

Can I schedule a meeting to discuss Octane Blue further?

Book a meeting with us

Don’t let the holiday season stress you out. Let Octane Blue take care of your TM1 environment so that you can enjoy a peaceful and productive holiday.

IBM

Navigating the Storm: A Double Migration

4 December 2024, 9:30:00 am AEDT

4 min

The past few weeks have been a whirlwind, a high stakes balancing act that tested the limits of our team's resilience and expertise. We simultaneously managed two major client migrations. Both were for high profile large clients and part of their IBM Planning Analytics Modernisation initiative:

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The clients

News Corp - News Corp Australia tells the stories that matter to 18.2 million Australians every month as an important part of News Corp, a diversified global media and information services company. From breaking news in the morning to deciding dinner that night, Australia trusts our brands to inform, inspire and delight across the day – including The Australian, The Daily Telegraph, Herald Sun, The Courier-Mail, The Advertiser, Mercury, NT News, Townsville Bulletin, The Cairns Post, Gold Coast Bulletin, Geelong Advertiser, news.com.au, Vogue, GQ, Kidspot, taste.com.au and plenty more. More here https://www.newscorpaustralia.com/

A long-time TM1 user was looking at refreshing its application and modernise it by going to the cloud and utilising new dashboarding capabilities of the workspace and starting some testing on AI capabilities for the Finance team. TM1 is one of the core applications within the Finance team and they could not afford to run the risk of a prolonged upgrade to the cloud.

BlueScope They are a global leader in metal coating and painting products for the building and construction industries, providing vital components for houses, buildings, structures, vehicles, and more.

They have built a solid foundation for growth with a diverse portfolio of businesses in some of the largest and fastest-growing economies of the world. They are headquartered in Australia, with our people and operations spread across North America, Australia, New Zealand, the Pacific Islands, and throughout Asia. More here https://www.bluescope.com/

BlueScope is also a long-term TM1 user with the application used for a number of areas including demand planning, forecasting and Reporting in the Finance teams. They were on an older version of on–prem TM1 and upgrading to the latest version of on-prem IBM Planning Analytics TM1

A Perfect Storm

Both projects presented unique challenges. NEWS's migration required significant user training and change management, while BlueScope's upgrade involved complex technical configurations and intricate coordination with multiple stakeholders. To make matters even more challenging, both go-lives were scheduled for the same day!

Overcoming the Odds

How did we navigate this perfect storm?

Strong Leadership: Clear and decisive leadership was crucial in keeping the projects on track. By setting clear expectations, prioritising tasks, and making timely decisions, we were able to mitigate risks and ensure smooth execution.
Effective Teamwork: Our team demonstrated exceptional teamwork and collaboration. By working closely together, we could share knowledge, support each other, and address challenges proactively.
Agile Methodology: We adopted an agile approach, breaking down the projects into smaller, manageable phases. This allowed us to adapt to changing circumstances and deliver value incrementally. We have built up a comprehensive checklist for our upgrade TM1 upgrades and this makes upgrades easier and risk free.
Robust Communication: Open and transparent communication was key to keeping all stakeholders informed and aligned. Regular status updates, clear documentation, and effective problem-solving ensured a smooth transition.

Lessons Learned

These experiences have taught us valuable lessons:

Prioritise and Plan: Careful planning and prioritization are essential, especially when managing multiple projects simultaneously.
Embrace Flexibility: Be prepared to adapt to unexpected challenges and changes in scope.
Build Strong Relationships: Strong relationships with clients and team members are crucial for successful project delivery.
Learn from Mistakes: Analyse past projects to identify areas for improvement and avoid repeating errors.

Successful outcomes for both clients

The upgrade was successful for both clients and went live on the same day. This was a testament to my team’s technical ability and tenacity to ensure we followed our upgrade checklist. All testing and end-user training went well. One of our key tenets of upgrades is to ensure that client communication around changed functionality and look and feel is explained, trained and tested. The stakeholders from our client side were great and the whole team went above and beyond during the upgrade and deployment.

Both the upgrade took under 6 weeks to complete with minimal disruption to the business.

A huge shoutout goes to Alpheus and Rajan for their stellar work on the NEWS migration, ensuring a smooth transition to the Cloud, and to Baburao for expertly managing the BlueScope upgrade, and overcoming every hurdle that came our way.

Your Turn

Have you faced similar challenges in managing multiple simultaneous projects? How did you overcome them? Share your experiences and insights in the comments below.

If you are looking to modernise or upgrade your IBM Planning Analytics, then contact us and we would be happy to guide you.

Analytics TM1 tech tips TM1 newbies IBM Planning Analytics latest enhancements

Highlights & Triumphs: IBM TechXchange 2024

6 November 2024, 8:30:00 am AEDT

4 min

Attending IBM TechXchange in Mandalay Bay, Las Vegas was an exhilarating experience that showcased the cutting-edge IBM technologies shaping various industries today. The conference brought together technologists, IBM Champions, thought leaders, industry experts, and innovators eager to share insights and explore the latest advancements in IBM's technology portfolio. From AI to cloud computing, the event highlighted how these tools are transforming businesses, making operations more efficient, and ultimately driving better decision-making.

As an IBM champion we were treated as VIPs with row seats in all keynote sessions, special champions lounge and special dinners and networking sessions. It was great to see IBM champions sporting the blue jackets throughout the conference.

One of the standout moments for me was the opportunity to present alongside Ashika Singh from Fiji Airways. Our session focused on their usage of IBM Planning Analytics, which allowed Fiji Airways to navigate the challenges posed during the COVID-19 shutdown. The pandemic created unprecedented obstacles for the airline industry, with travel restrictions and safety concerns leading to a dramatic decline in passenger numbers. However, Fiji Airways leveraged IBM Planning Analytics to make data-driven decisions that prepared them for the eventual reopening of global travel.

During our presentation, we shared how the airline utilised forecasting and scenario planning capabilities in the tool to assess various outcomes and devise strategies for recovery. By analysing all drivers and generating up to 60- what-if scenarios at a time, Fiji Airways was able to predict future demand and align their resources accordingly. This proactive approach not only ensured they were ready when the skies reopened but also positioned them to adapt quickly to changing circumstances, ultimately enhancing their resilience. This led to numerous awards and Fiji Airways is now ranked 14^th in the world in Skytrax ranking 2024. They have overtaken Qantas and Air New Zealand which traditionally dominated the rankings in the region.

The discussions throughout the conference were incredibly enlightening. Industry leaders spoke about the importance of digital transformation and how organizations must prioritise agility and innovation to thrive in today's fast-paced environment. There was a strong emphasis on how leveraging AI and analytics can unlock new opportunities, streamline operations, and create a more personalized customer experience. These insights resonate deeply, especially in sectors like travel and hospitality that have been profoundly affected by global events.

Networking opportunities were plentiful at TechXchange, allowing me to connect with other professionals who share a common goal of harnessing technology for business growth. Conversations flowed about the challenges and triumphs faced during the pandemic, highlighting how collaboration and knowledge-sharing have played vital roles in overcoming adversity. Each conversation reinforced the idea that we are all part of a larger community that supports each other's journeys toward transformation. There were lots of opportunities to network with other TM1 specialists from around the world.

The event was also filled with hands-on labs and demonstrations, showcasing IBM's latest products and solutions. Exploring new functionalities and engaging with the technology firsthand enhanced my understanding of how these tools can be applied in various contexts. It was exciting to envision how businesses can harness these innovations to optimize their operations and improve overall performance. IBM also used the conference to announce the launch of the Granite 3.0 AI model. (Read more here Granite 3.0)

Looking back on my experience at IBM TechXchange, I am inspired and optimistic about the future. Planning Analytics has a range of new functionality to improve performance, deployment options and integration of IBM AI onto the platform will cement its position as a leader in the XP&A space. Presenting with Ashika was a highlight that illustrated not only the adaptability of Fiji Airways but also the potential of data-driven decision-making for all of our clients. There was a lot of interest in how we integrate Watson Orchestrate with Planning Analytics to boost the AI functionality in finance teams.

In conclusion, attending IBM TechXchange has provided a great platform to see where IBM is going in the future – and it's looking exciting. As AI becomes more mainstream and use cases continue to evolve it was interesting to see how our peers and IBM are harnessing this technology to deliver business value for clients. The dates for 2025 Techxchange in Orlando are already announced and Octane will once again attend along with our clients. (More details IBM Techxchange conference)

IBM

Enhancing Planning Analytics Workspace (PAW) visualisations using MDX

23 October 2024, 8:30:00 am AEDT

5 min

Planning Analytics Workspace (PAW) offers a robust suite of visualizations, enabling users to create rich and compelling reports and dashboards with remarkable flexibility. However, even with these capabilities, you may occasionally encounter requirements that push the limits of what PAW provides out of the box.

One such scenario I encountered was the need to create a column chart comparing Actual vs Budget variance. The twist? Any negative variance should be highlighted with a red bar, while positive variance should be displayed in green, as shown below:

A screenshot of a computer screen
Description automatically generated

PAW’s default settings don't currently offer this kind of custom conditional formatting for visualizations. However, with a little MDX magic and a few formatting tweaks, you can achieve this effect in just five simple steps.

Step-by-Step Guide to Creating Custom Visualizations in PAW

Step 1: Position the Version Dimension in the Column

Start by positioning the Version dimension in the column of the Exploration view. This is where we will apply the MDX logic to derive the desired results.

Step 2: Use MDX to Create Calculated Members

Next, you'll need to update the MDX query by creating three calculated members to represent Actual vs Budget (AvB), Positive Variance, and Negative Variance.

Here’s the MDX code:

MDX code:

WITH

MEMBER [Version].[Version].[AvB] AS [Version].[Version].[Actual] - [Version].[Version].[Budget]

MEMBER [Version].[Version].[Positive] AS IIF([Version].[Version].[AvB] > 0, [Version].[Version].[AvB], "")

MEMBER [Version].[Version].[Negative] AS IIF([Version].[Version].[AvB] < 0, [Version].[Version].[AvB], "")

Note: The AvB calculation could also be done using a consolidated member in the Version dimension, where the Budget has a negative weight.

Step 3: Replace the MDX in the Row Axes

Now, replace the MDX in the Row Axes relating to the Version dimension to show only the Positive and Negative calculated members, while excluding the AvB calculation (and any other member):

MDX code:

{

EXCEPT(

{

[Version].[Version].[AvB],

[Version].[Version].[Positive],

[Version].[Version].[Negative]

{

[Version].[Version].[AvB]

ALL

)

}

This MDX will generate a view that displays only Positive and Negative members in the Version dimension, leaving the non-relevant member (whether positive or negative) as blank, depending on the AvB value.

Step 4: Convert the Exploration View into a Column Chart

Once the MDX has been applied, convert the Exploration view into a Column Chart. By default, PAW will show the columns for positive and negative values with its standard color scheme.

A graph of a number of states
Description automatically generated

Step 5: Apply a Custom Color Palette

To finalize the visualization, we’ll apply a custom color palette. Navigate to the visualization properties and create a color palette that includes only two colors: green for positive values and red for negative values.

A blue and white flag
Description automatically generated with medium confidence

Conclusion

With just a few lines of MDX and a bit of customization, you can significantly enhance PAW visualizations. This technique allows you to move beyond the standard out-of-the-box options, giving you the flexibility to create more intuitive and visually effective reports. Whether you're comparing Actual vs Budget or any other metrics, these methods help you build visuals that not only convey the necessary information but do so in a way that is easy to interpret at a glance.

By leveraging MDX and PAW’s formatting tools, you can push the boundaries of your reporting and create dynamic, insightful dashboards tailored to your business needs.

PAW IBM

Integrating transactions logs to web services for PA on AWS using REST API

7 August 2024, 8:30:00 am AEST

5 min

In this blog post, we will showcase the process of exposing the transaction logging on Planning Analytics (PA) V12 on AWS to the users. Currently, in Planning Analytics there is no user interface (UI) option to access transaction logs directly from Planning Analytics Workspace. However, there is a workaround to expose transactions to a host server and access the logs. By following these steps, you can successfully access transaction logged in Planning Analytics V12 on AWS using REST API.

integratetranslogs-ezgif.com-optimize

Step 1: Creating an API Key in Planning Analytics Workspace

The first step in this process is to create an API key in Planning Analytics Workspace. An API key is a unique identifier that provides access to the API and allows you to authenticate your requests.

Navigate to the API Key Management Section: In Planning Analytics Workspace, go to the administration section where API keys are managed.
Generate a New API Key: Click on the option to create a new API key. Provide a name and set the necessary permissions for the key.
Save the API Key: Once the key is generated, save it securely. You will need this key for authenticating your requests in the following steps.

Step 2: Authenticating to Planning Analytics As a Service Using the API Key

Once you have the API key, the next step is to authenticate to Planning Analytics as a Service using this key. Authentication verifies your identity and allows you to interact with the Planning Analytics API.

Prepare Your Authentication Request: Use a tool like Postman or any HTTP client to create an authentication request.
Set the Authorization Header: Include the API key in the Authorization header of your request. The header format should be Authorization: Bearer <API Key>.
Send the Authentication Request: Send a request to the Planning Analytics authentication endpoint to obtain an access token.

Detailed instructions for Step 1 and Step 2 can be found in the following IBM technote:

How to Connect to Planning Analytics as a Service Database using REST API with PA API Key

Step 3: Setting Up an HTTP or TCP Server to Collect Transaction Logs

In this step, you will set up a web service that can receive and inspect HTTP or TCP requests to capture transaction logs. This is crucial if you cannot directly access the AWS server or the IBM Planning Analytics logs.

Choose a Web Service Framework: Select a framework like Flask or Django for Python, or any other suitable framework, to create your web service.
Configure the Server: Set up the server to listen for incoming HTTP or TCP requests. Ensure it can parse and store the transaction logs.
Test the Server Locally: Before deploying, test the server locally to ensure it is correctly configured and can handle incoming requests.

For demonstration purposes, we will use a free web service provided by Webhook.site. This service allows you to create a unique URL for receiving and inspecting HTTP requests. It is particularly useful for testing webhooks, APIs, and other HTTP request-based services.

Step 4: Subscribing to the Transaction Logs

The final step involves subscribing to the transaction logs by sending a POST request to Planning Analytics Workspace. This will direct the transaction logs to the web service you set up.

Practical Use Case for Testing IBM Planning Analytics Subscription

Below are the detailed instructions related to Step 4:

Copy the URL Generated from Webhook.site:
- Visit siteand copy the generated URL (e.g., https://webhook.site/<your-unique-id>). The <your-unique-id> refers to the unique ID found in the "Get" section of the Request Details on the main page.

Subscribe Using Webhook.site URL:
- Open Postman or any HTTP client.
- Create a new POST request to the subscription endpoint of Planning Analytics.
- In Postman, update your subscription to use the Webhook.site URL using the below post request:

In the body of the request, paste the URL generated from Webhook.site:

{
"URL": "https://webhook.site/your-unique-id"
}
<tm1db> is a variable that contains the name of your TM1 database.

Note: Only the transaction log entries created at or after the point of subscription will be sent to the subscriber. To stop the transaction logs, update the POST query by replacing /Subscribe with /Unsubscribe.

By following these steps, you can successfully enable and access transaction logs in Planning Analytics V12 on AWS using REST API.

Tips on how to manage your Planning Analytics (TM1) effectively

31 July 2024, 8:30:00 am AEST

3 min

Effective management of Planning Analytics (TM1), particularly with tools like IBM’s TM1, can significantly enhance your organization’s financial planning and performance management.

TM1 newsletter

Here are some essential tips to help you optimize your Planning Analytics (TM1) processes:

1. Understand Your Business Needs

Before diving into the technicalities, ensure you have a clear understanding of your business requirements. Identify key performance indicators (KPIs) and metrics that are critical to your organization. This understanding will guide the configuration and customization of your Planning Analytics model.

2. Leverage the Power of TM1 Cubes

TM1 cubes are powerful data structures that enable complex multi-dimensional analysis. Properly designing your cubes is crucial for efficient data retrieval and reporting. Ensure your cubes are optimized for performance by avoiding unnecessary dimensions and carefully planning your cube structure to support your analysis needs.

3. Automate Data Integration

Automating data integration processes can save time and reduce errors. Use ETL (Extract, Transform, Load) tools to automate the extraction of data from various sources, its transformation into the required format, and its loading into TM1. This ensures that your data is always up-to-date and accurate.

4. Implement Robust Security Measures

Data security is paramount, especially when dealing with financial and performance data. Implement robust security measures within your Planning Analytics environment. Use TM1’s security features to control access to data and ensure that only authorized users can view or modify sensitive information.

5. Regularly Review and Optimize Models

Regularly reviewing and optimizing your Planning Analytics models is essential to maintain performance and relevance. Analyze the performance of your TM1 models and identify any bottlenecks or inefficiencies. Periodically update your models to reflect changes in business processes and requirements.

6. Utilize Advanced Analytics and AI

Incorporate advanced analytics and AI capabilities to gain deeper insights from your data. Use predictive analytics to forecast future trends and identify potential risks and opportunities. TM1’s integration with other IBM tools, such as Watson, can enhance your analytics capabilities.

7. Provide Comprehensive Training

Ensure that your team is well-trained in using Planning Analytics and TM1. Comprehensive training will enable users to effectively navigate the system, create accurate reports, and perform sophisticated analyses. Consider regular training sessions to keep the team updated on new features and best practices.

8. Foster Collaboration

Encourage collaboration among different departments within your organization. Planning Analytics can serve as a central platform where various teams can share insights, discuss strategies, and make data-driven decisions. This collaborative approach can lead to more cohesive and effective planning.

9. Monitor and Maintain System Health

Regularly monitor the health of your Planning Analytics environment. Keep an eye on system performance, data accuracy, and user activity. Proactive maintenance can prevent issues before they escalate, ensuring a smooth and uninterrupted operation.

10. Seek Expert Support

Sometimes, managing Planning Analytics and TM1 can be complex and may require expert assistance. Engaging with specialized support services can provide you with the expertise needed to address specific challenges and optimize your system’s performance.

By following these tips, you can effectively manage your Planning Analytics environment and leverage the full potential of TM1 to drive better business outcomes. Remember, continuous improvement and adaptation are key to staying ahead in the ever-evolving landscape of financial planning and analytics.

For specialized TM1 support and expert guidance, consider consulting with professional service providers like Octane Software Solutions. Their expertise can help you navigate the complexities of Planning Analytics, ensuring your system is optimized for peak performance. Book me a meeting

Analytics TM1 tech tips shared resources TM1 support

ChatGPT for Enterprise: Reimagine how works gets done with AI powered automation

5 March 2024, 8:15:00 am AEDT

5 min

In the realm of digital transformation, the concept of digital labor has emerged as a game-changer for businesses seeking efficiency, agility, and innovation. IBM WatsonsX Orchestrate, a powerhouse in the AI and data orchestration space, takes center stage in this digital evolution. This blog explores the pivotal role played by WatsonsX Orchestrate in reshaping digital labor and how it empowers organizations to harness the full potential of artificial intelligence (AI) and data science.

Orchestrate allows you to add and train new automations from a variety of sources, enabling users to easily work across existing systems using a single UI.

fiji blog (1)

Understanding Digital Labor:

Digital labor refers to the use of digital technologies, including AI, automation, and robotics, to augment or replace human tasks and processes. It's a paradigm shift in how work is done, leveraging technology to enhance productivity, reduce errors, and enable humans to focus on more strategic and creative aspects of their roles.

"Companies that effectively apply intelligent automation across the enterprise expect to outshine peers in profitability, revenue growth, and efficiency over the next 3 years."

IBM WatsonsX Orchestrate and Digital Labor:

3 points entry

Workflow Automation for Operational Efficiency: One of the key pillars of digital labor is workflow automation, and WatsonsX Orchestrate excels in this domain. By automating intricate AI and data science workflows, the platform significantly reduces manual effort, streamlining processes and enhancing operational efficiency. This allows organizations to accomplish more with less, freeing up human resources for high-value tasks.
Collaboration for Enhanced Productivity: Digital labor is not about replacing human workers but augmenting their capabilities. WatsonsX Orchestrate fosters collaboration among cross-functional teams, bringing together data scientists, developers, and domain experts. This collaborative environment accelerates problem-solving, decision-making, and innovation, creating a synergistic relationship between digital labor and human expertise.
Scalability to Meet Growing Demands: As organizations scale their digital labor initiatives, scalability becomes a critical factor. WatsonsX Orchestrate provides the flexibility to scale horizontally and vertically, ensuring that the platform can seamlessly adapt to the growing demands of AI and data science projects. This scalability is essential for organizations aiming to expand their digital labor capabilities without compromising performance.
Model Monitoring and Management for Continuous Improvement: In the era of digital labor, continuous improvement is paramount. WatsonsX Orchestrate includes robust tools for monitoring and managing AI models in production. This ensures that digital labor processes based on AI models deliver consistent and reliable results over time. The platform's capabilities contribute to the iterative refinement of digital labor processes, optimizing outcomes and enhancing overall performance.
AI Explainability and Ethical Digital Labor: Transparent digital labor practices are crucial for building trust and ensuring ethical use of AI. WatsonsX Orchestrate provides tools for explaining AI model decisions, addressing the interpretability challenge often associated with complex AI systems. Additionally, the platform includes features for detecting biases, aligning digital labor practices with ethical standards and promoting fairness in decision-making.

There are more than 2,000 activities that make up 800 full-time occupations that are part of knowledge work. However, only 5% of these full-time occupations could be fully automated using existing technology. That means that the 95% of remaining occupations require cognitive abilities.

Benefits for Businesses:

watsons business overview

Accelerated Time-to-Value: By automating and streamlining AI and data science workflows, organizations can significantly reduce the time it takes to move from ideation to deployment, ultimately accelerating their time-to-value for AI initiatives.
Improved Collaboration: The collaborative features of WatsonsX Orchestrate facilitate better communication and knowledge sharing among teams, leading to more effective and impactful AI solutions.
Enhanced Governance and Compliance: The platform provides robust governance and compliance features, ensuring that organizations can meet regulatory requirements and maintain a high standard of data ethics.
Cost-Efficiency: With the ability to scale and the flexibility of deployment options, WatsonsX Orchestrate helps organizations optimize costs by aligning infrastructure with project requirements.

In the era of AI and data-driven decision-making, IBM WatsonsX Orchestrate stands out as a powerful solution for organizations looking to harness the full potential of their AI and data science initiatives. With its automation capabilities, collaborative environment, and emphasis on ethical AI, WatsonsX Orchestrate is poised to become a key player in the journey towards building intelligent, transparent, and scalable AI solutions. As businesses continue to navigate the complexities of the digital age, platforms like WatsonsX Orchestrate provide the tools needed to turn data into a strategic asset and drive innovation in the ever-evolving landscape of AI.

Business Intelligence IBM WatsonX Orchestrate

Saying Goodbye to Cognos TM1 10.2.x: Changes in support effective April 30, 2024

13 February 2024, 9:00:00 am AEDT

2 min

In a recent announcement, IBM unveiled changes to the Continuing Support program for Cognos TM1, impacting users of version 10.2.x. Effective April 30, 2024, Continuing Support for this version will cease to be provided. Let's delve into the details.

blog (1)

What is Continuing Support?

Continuing Support is a lifeline for users of older software versions, offering non-defect support for known issues even after the End of Support (EOS) date. It's akin to an extended warranty, ensuring users can navigate any hiccups they encounter post-EOS. However, for Cognos TM1 version 10.2.x, this safety net will be lifted come April 30, 2024.

What Does This Mean for Users?

Existing customers can continue using their current version of Cognos TM1, but they're encouraged to consider migrating to a newer iteration, specifically Planning Analytics, to maintain support coverage. While users won't be coerced into upgrading, it's essential to recognize the benefits of embracing newer versions, including enhanced performance, streamlined administration, bolstered security, and diverse deployment options like containerization.

How Can Octane Assist in the Transition?

Octane offers a myriad of services to facilitate the transition to Planning Analytics. From assessments and strategic planning to seamless execution, Octane support spans the entire spectrum of the upgrade process. Additionally, for those seeking long-term guidance, Octane Expertise provides invaluable Support Packages on both the Development and support facets of your TM1 application.

FAQs:

Will I be forced to upgrade?

No, upgrading is not mandatory. Changes are limited to the Continuing Support program, and your entitlements to Cognos TM1 remain unaffected.
How much does it cost to upgrade?

As long as you have active Software Subscription and Support (S&S), there's no additional license cost for migrating to newer versions of Cognos TM1. However, this may be a good time to consider moving to the cloud.
Why should I upgrade?

Newer versions of Planning Analytics offer many advantages, from improved performance to heightened security, ensuring you stay ahead in today's dynamic business environment. This brings about unnecessary risk to your application.
How can Octane help me upgrade?

Octane’s suite of services caters to every aspect of the upgrade journey, from planning to execution. Whether you need guidance on strategic decision-making or hands-on support during implementation, Octane is here to ensure a seamless transition. Plus we are currently offering a fixed-price option for you to move to the cloud. Find out more here

In conclusion, while bidding farewell to Cognos TM1 10.2.x may seem daunting, it's also an opportunity to embrace the future with Planning Analytics. Octane stands ready to support users throughout this transition, ensuring continuity, efficiency, and security in their analytics endeavours.

Analytics TM1 tech tips

Mastering Calculations in Planning Analytics: Adapt to Changing Months with Ease

8 June 2023, 8:30:00 am AEST

6 min

One of the standout features of Planning Analytics Workspace (PAW) is its ability to create calculations in the Exploration view. This feature empowers users to perform advanced calculations without the need for technical expertise. Whether you're using PAW or PAfE (Planning Analytics for Excel), the Exploration view offers a range of powerful capabilities. The Exploration view supports a variety of functions, such as aggregations, mathematical operations, conditional logic, and custom calculations. This means you have the flexibility to perform complex calculations tailored to your specific needs.

This enables users to create complex financial calculations and business rules within the views, providing more accurate and tailored results for analysis and planning. All this can be done by the business users themselves without relying on IT or development teams, enabling faster and more agile reporting processes. This enables creating ad hoc reports and performing self-service analysis on the fly with a few simple clicks. This self-service capability puts the control in the hands of the users, eliminating the need for lengthy communication processes or waiting for IT teams to fulfill reporting requests.

In this blog post, we will focus on an exciting aspect of the Exploration view: creating MDX-based views that are dynamic and automatically update as your data changes. The beauty of these dynamic views is that users no longer need to manually select members of dimensions to keep their formulas up to date.

Similar to the functionality of dynamic subsets in dimensions, where each click in the set editor automatically generates MDX statements that can be modified, copied, and pasted, the exploration views in Planning Analytics Workspace also generate MDX statements. These MDX statements are created behind the scenes as you interact with the cube view. Just like MDX subsets, these statements can be easily customized, allowing you to fine-tune and adapt them to your specific requirements.

By being able to tweak, copy, and paste these MDX statements, you can easily build upon previous work or share your calculations with others.

Currently, the calculations are not inherently dynamic, however, there are techniques that can be employed to make the calculations adapt to changing time periods.

A classic example we can look at is performing variance analysis on P&L cube where we wish to add a variance formula to show the variance of current month from the previous month. There are many more calculations that we can consider from but we will focus on this analysis in this blog.

If we take our example, the current month and previous month keep changing every month as we roll forward and they are not static. When dealing with changing months or any member in your calculation, it's important to ensure that your calculations remain dynamic and adaptable to those changes.

To ensure dynamic calculations that reflect changes in months, you have several options to consider:

Manual Approach: You can manually update the column dimensions with the changing months and recreate the calculations each time. However, this method is time-consuming, prone to errors, and not ideal for regular use.

Custom MDX Approach: Another option is to write custom MDX code or modify existing code to reference the months dynamically from a Control cube. While this approach offers flexibility, it can be too technical for end users.

Consolidations Approach: Create consolidations named "Current Month" and "Prior Month" and add the respective months to them as children. Then, use these consolidations in your view and calculations. This approach provides dynamic functionality, but you may need to expand the consolidations to see the specific months, which can be cumbersome.

Alias Attributes Approach: Leverage alias attributes in your MDX calculations. By assigning aliases to the members representing the current and previous months, you can dynamically reference them in your calculations. This approach combines the benefits of the previous methods, providing dynamic calculations, visibility of months, and ease of use without excessive manual adjustments.

In this blog post, we will focus on the alias attributes approach as a recommended method for achieving dynamic calculations in PAW or PAfE. We will guide you step-by-step through the process of utilizing alias attributes to ensure your calculations automatically adapt to changing months. By following this approach, you can simplify your calculations, improve efficiency, and enable non-technical users to perform dynamic variance analysis effortlessly.

To create dynamic calculations for variances between the current and prior month, you can follow these steps:

Step 1: Ensure you have an alias attribute available in your Month dimension. If not, create a new alias attribute specifically for this purpose.
Step 2: Update the alias with the values "Curr Month" and "Prior Month" for the respective months.
Step 3: Open the exploration view in PAW and select the two months (current and prior) on your column or row dimension.
Step 4: Create your variance calculation using the exploration view's calculation capabilities. This could involve subtracting the P&L figures of the prior month from the current month, for example.
Step 5: Open the MDX code editor and replace the actual month names in the MDX code with the corresponding alias values you updated in Step 2. You can copy the code in Notepad and use the "Find and Replace" function to make this process faster and more efficient.

ezgif.com-video-to-gif (3)

By replacing the month names with the alias values, you ensure that the calculation remains dynamic and adapts to the changing months without manual intervention. When you update the alias values in the Month dimension, it will reflect in the exploration view. As a result, the months displayed in the view will be dynamically updated based on the alias values. This ensures that your calculations remain synchronized with the changing months without the need for manual adjustments.

Important Note: When selecting the months in set editor, it is crucial to explicitly select and move the individual months from the Available members' pane (left pane) to the Current set pane (right pane). This step is necessary to ensure that unnecessary actions, such as expanding a quarter to select a specific month, are not recorded in the MDX code generated in the exploration view which can potentially lead to issues while replacing the member names with alias values.

This approach of using alias attributes to make calculations dynamic can be extended to various other calculations in Planning Analytics Workspace. It provides a flexible and user-friendly method to ensure that your calculations automatically adapt to changing dimensions or members.

That being said, it's important to note that there may be certain scenarios where alternative approaches, such as writing custom MDX code or utilizing a control cube, are necessary. Each situation is unique, and the chosen approach should align with the specific requirements and constraints of the calculation, however the proposed approach should still work for a wide variety of calculations in IBM Planning Analytics.

Unlocking the Power of Hierarchies in IBM Planning Analytics

11 April 2023, 8:00:00 am AEST

4 min

With the introduction of hierarchies in IBM Planning Analytics, a new level of data analysis capability has been unlocked. This is by far one of the most significant enhancements to the Planning Analytics suite as far as the flexibility and usability of the application is concerned.

IBM Planning Analytics

Benefits of LEAVES Hierarchy

One particular useful hierarchy is the LEAVES hierarchy. It offers several benefits beyond data analysis.

One that stands out is that it is a “zero-maintenance” hierarchy as it automatically adds leaf level members as they are added in other hierarchies. It can also be used as a master hierarchy to validate and compare nLevel members in all the other hierarchies. Additionally, deleting the member from this hierarchy will delete it from the rest of the hierarchies.

While all hierarchies must be either manually created or automated through the TI process, contrary to the general perception within the PA community where it is maintained that LEAVES hierarchy only gets added when you create a new hierarchy in a dimension, there is, however, a quick and easy way to create the LEAVES hierarchy without creating any other hierarchy in few simple steps.

}DimensionProperties cube

This is where, where I would like to expose you to a control cube - }DimensionProperties. In this cube you will find quite a few properties that you can play around with. Two properties to focus in this blog are “ALLLEAVESHIERARCHYNAME” and “VISIBILITY”.

Creating LEAVES hierarchy

By default, the value for ALLLEAVESHIERARCHYNAME in the control cube is blank, however, entering any name in that cell against a corresponding dimension will automatically create a LEAVES hierarchy with that name.

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Once done, the Database Tree must be refreshed to see the leaves hierarchy reflecting under the dimension.

This way you can quite easily create the LEAVES hierarchy for any number of dimensions by updating the values in }DimensionProperties cube.

Caution: If you overwrite the name in the control cube, the LEAVES hierarchy name is updated with the new name in the Database Tree and if your old LEAVES hierarchy is referenced in any rules, process or views and subsets, they will no longer work. However, once you restore the original name in the control cube, it will start working. This risk can be mitigated by using a consistent naming convention across the model.

Note that the old hierarchy will still remain in the ‘}Dimensions’ dimension and changing the name does not automatically delete the old hierarchy member.

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Toggling Hierarchies

In addition to creating LEAVES hierarchy using a few simple steps, you can also use the }DimensionProperties cube to hide or unhide any hierarchy you have created. This capability is useful if there are many hierarchies that have been created but only a selected few needs to be exposed to the users. If any of the hierarchy is not yet updated and is still in WIP state, it can be hidden until the changes are finalized. This gives more control to the administrators or power users to hide or unhide whichever hierarchy they like to show.

To hide any hierarchy, enter the value NO against the “Visibility” property in the control cube. Once the Database Tree is refreshed, that hierarchy will no longer be visible under the dimension. This property is also blank by default.

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If a view contains a hierarchy and the VISIBILITY property of that hierarchy is set to NO, while the view still opens, opening the subset editor will throw an error.

Note, to unhide the hierarchy, delete the value or enter YES and refresh the Database Tree.

In conclusion, once you understand the benefits and take into account the potential pitfalls of updating the properties, using this capability would greatly enhance the overall usability and maintainability of the application.

DYNAMIZING DYNAMIC REPORTS: A Hack to Make Columns as Dynamic as Rows

22 March 2023, 9:00:00 am AEDT

4 min

If you’re tired of manually updating your reports every time you need to add a new column in your Dynamic Reports, you're not alone. It can be time-consuming and tedious - not to mention frustrating - to have to constantly tweak and adjust your reports as your data changes. Luckily, there’s a way to make your life easier: Dynamizing Dynamic Reports. By using a hack to make your reports’ columns as dynamic as the rows, you can free up time and energy for other tasks - and make sure your reports are always up-to-date. Read on to learn how to make your reports more dynamic and efficient!

The Good

Dynamic Reports in PAfE is highly popular and primarily used due to its intrinsic characteristic of being dynamic. The great thing about this report and one of the big reasons for its wide adoption is that the row content in this report updates dynamically, either depending on the subset used or the mdx expression declared within the TM1RptRow function and also because the formulas in the entire TM1RPTDATARNG are dictated by simply updating them in the master row (first row of data range) and it cascades down automatically, including how the formats of the reports are dynamically applied in the report.

The Bad

That being said, with all those amazing capabilities, there is however one big limitation of this report and that is that, unlike rows, the columns are still static and require the report builder to manually insert the elements and the formulas across the columns, thereby making it “not so dynamic” as you would otherwise expect, in that context.

Purpose of this blog

And it is precisely this limitation that this blog aims to address and provide you with a workaround to this problem and make the columns as dynamic as the rows, thus substantiating the title of the blog “Dynamizing the Dynamic Report”.

Method

In order to achieve the dynamism, I have primarily used a combination of 4 functions; 3, Excel 365 and 1, PAfE Worksheet function and they are as follows:

BYCOL - processes data in an array or range and leverages LAMBDA function as an argument to each column in the array to return one result per column as a single array
LAMBDA - a UDF (user defined function) helps to create generic custom functions in Excel that can reused by either embedding it as an argument in another LAMBDA supported function (such as BYCOL) or a function of its own when ported as a named range
TRANSPOSE - Dynamic Array function to transpose the row or column array
TM1ELLIST - Only PAfE worksheet function that returns an array of values from a dimension subset, static list or MDX expression

Instructions

Let's have a look now at how we have utilized these functions within the Dynamic Report.

The above image is a Dynamic Report showing the data from the Benefits Assumptions cube having 3 dimensions; Year, Version, and Benefit.

The Benefit dimension is across rows, Year across columns, and Version on the title.

In cell C17, I used the TM1Ellist function to get the Year members (Y1, Y2, Y3) from a subset named “Custom Years” returning it as a range and then wrapping it inside the TRANSPOSE function to transpose the resultant range.

Cell C17 formula:

In cell C18, instead of DBRW, I used the BYCOL function where I used the range in cell C17 by prefixing it with spilled reference (#) as the first argument of it.

I then used the LAMBDA function to create a custom function as its second argument where I declared a variable x and passed it inside the DBRW formula in the position of the Year dimension.

So the way the formula would work is, it would take the output from TM1ELLIST function and pass each member of it in LAMBDA function as variable x which is then passed within DBRW formula, making it a dynamic range that automatically resizes based on the output of TM1ELLIST function.

Cell C18 formula:

Note that the formula is only entered in one cell (C18) and it spills across both rows and columns.

Caveats

This is only supported in PAfE which means it won’t work in PAW or TM1Web
Works in Excel that supports Dynamic Array and LAMBDA functions
The formatting is not spilled

Exploring the Latest Enhancements of IBM Planning Analytics Components

28 February 2023, 9:33:19 pm AEDT

3 min

As the world moves towards more data-driven decision-making, businesses are increasingly looking for effective planning and budgeting solutions. IBM Planning Analytics is the go-to for businesses looking for a comprehensive set of tools to help them manage their budgeting and planning process.

With Planning Analytics, businesses can access powerful analytics to make more informed decisions, leverage advanced features to create complex models, and gain better insights into their financial data.

IBM is constantly improving the functionalities and features of the IBM Planning Analytics components. This includes Planning Analytics Workspace (PAW), Planning Analytics for Excel (PAfX), and Planning Analytics with Watson. With these updates, businesses can take advantage of new features to help them manage their budgeting and planning process more effectively.

In the last 12 months, IBM has released several updates to its Planning Analytics components.

In PAW, users can now access advanced analytics such as forecast simulations, predictive models, and scenario analysis. They can also perform in-depth analysis on their data with the new Visual Explorer feature. In addition, users can now access a library of planning and budgeting models, which can be customized to fit the needs of their organization. (download PDF file to get the full details)

download PDF file to get the full details

In PAfX, users can now access advanced features such as SmartViews and SmartCharts. SmartViews allows users to visualize their data in various ways, while SmartCharts allows users to create interactive charts and graphs. Users can also take advantage of the new custom formatting options to make their reports look more professional.

download PDF file to get the full details

Finally, with Planning Analytics with Watson, users can access powerful AI-driven insights. This includes AI-driven forecasting, which allows users to create more accurate forecasts. In addition, Watson can provide insights into the drivers of their business, allowing users to make more informed decisions.

download PDF file to get the full details

Overall, IBM’s updates to the Planning Analytics components provide businesses with powerful tools to help them manage their budgeting and planning process. With these updates, businesses can take advantage of the latest features to quickly access data-driven insights, create more accurate forecasts, and gain better insights into their financial data.

Download the PDF file below to get the full version of each IBM Planning Analytics components.

CFO’s love-hate relationship with Excel spreadsheets

20 September 2022, 9:00:00 am AEST

5 min

Warning: long term use of Excel is lethal for CFOs trying to make data-driven decision-making. Discover how Excel can’t excel when it comes to managing data and what can be done about it. IMG_20220726_111029

The troubles with Excel are only beginning. Excel not only caps out at a million records, but it also rounds off large numbers using imprecise calculations which compromises the accuracy of your data.

Excel is a single-user system. That means it only allows one person to input data at a time. There’s a high chance that someone entering the data does it without regard for storage, backup, and processing best practices.

Excel kills collaboration

They lack IT expertise, so they hyper-personalise it for themselves leaving no room for collaboration and control.

Working with real-time data in such an environment where there is no room for collaboration and control becomes cumbersome and time-consuming. Your employees are spending their valuable time putting together different versions of data that are scattered over different workstations in different departments.

No integration with other finance systems

Excel cannot be integrated with data from other departments to get insights and make decisions. This creates a situation where finance managers don’t have consistent visibility of data throughout the operations.

When you operate in different locations throughout Australia, the only way to share such files is through emails. Different versions of your data are flying around back and forth with different versions of truth in them. Anyone who tries to alter logic can do so without any accountability.

Excel inaccuracies go undetected

Missing data and incorrect formulas introduced by human errors can easily be propagated throughout your organisation because of the links in the worksheets. The links tend to break creating a cyclic version control nightmare that is very difficult to troubleshoot. Even if someone can get all the files together, it becomes impossible to trace the logic behind the personalised formulas that only the creator could understand. If the creator of the file is not available or has left your organisation, everything comes to a halt.

Anthony, head of Client Relations at Octane, tells this story about a CFO from the construction industry, who is moving away from Excel to a modern budgeting and forecasting tool:

“They wrote their excel sheets over a period of 15 years. No one knows where the person who started writing it is today. The logic is buried so deep inside the excel spreadsheets that it breaks very easily. When other people make calls to such links it breaks everything and now nobody knows what to do next.

Worse, people often have to input their numbers in a sequence. If Jack doesn’t enter X, then Jill cannot enter Y. It’s a joke that is played out in finance offices all over Australia.”

Excel attracts malicious behavior

Intentional and fraudulent manipulations are very easy to do with Excel. The company files with financial logic into it should never fall into the wrong hands. But with Excel, your data can be easily copied and updated with faulty values and dependencies without being detected.

Further, in case of any mishap or disaster, there is no way for full data recovery with Excel. Without the company’s financial data, customer records, inventory data, and sub-contractors' accounts it becomes very hard for the company to make a quick turnaround after such events.

Missing regulatory deadlines

Consolidation of data to prepare reports while cross-referencing it with different versions from different departments takes a lot of time and effort from your employees. A lot of times your executives can even miss the timelines for a proper course of action.

Meeting regulatory compliance from the Australian government is a very good example of how things can go downhill with Excel. The Australian government is tightening regulations and you would have a timeline to meet while submitting your worksheets. There’s a high chance that your spreadsheets will take a lot more time and effort to put together missing the deadlines and exposing you to official scrutiny.

CFOs still love Excel

Excel is not going away. Despite the pitfalls, Excel remains best friends with CFOs. According to NetSuite Brainyard’s whitepaper, CFOs spend an average of 2 hours in spreadsheets every day. That’s more time in spreadsheets than any other software in their toolkit. ^[1]

TM1: a modern budgeting and forecasting tool that uses Excel

Most finance people will keep using Excel because they have grown with excel sheets. Even when they face all these problems with Excel, they simply can’t get over the user interface and the flexibility that Excel provides.

That’s why TM1 was developed by IBM. It is a modern budgeting and forecasting tool that still uses an Excel interface.

TM1 allows the finance office to pull and manipulate millions of records in less than a second using a familiar Microsoft Excel interface.
TM1 has all the flexibility of Excel like writing formulas on the fly, without the inaccuracies of Excel.

Amendra Pratap, Managing Director at Octane, says:

“In spreadsheets, you cannot form a repeatable and reliable process. Every time you do something, you must do it over and over again. You can’t perform to your fullest potential when your employees are doing process work. Platforms like IBM Planning Analytics then eliminate the repetition of effort, freeing your managers to do the job that needs their actual attention.”

Driver-based forecasting unlock big-time opportunities in construction

6 September 2022, 9:00:00 am AEST

5 min

Instead of using historical sales data, CFOs making financial transformations are shifting to driver-based forecasting. In the Australian construction industry alone, market conditions have moved so quickly in the last 24 months, that history can no longer be trusted to tell the future.

In the Australian construction industry alone, market conditions have moved so quickly in the last 24 months, that history can no longer be trusted to tell the future.

Forecasting P+L and balance sheets based on actuals and historical data is proving less reliable. The historical data can’t capture the market dynamics like real-time data can and forecasts based on historical data don’t give good indicators for the decision-making processes. Driver-based forecasting models are going to replace the models based on historical trends for budgeting and forecasting. Anthony Coundouris, Head of Client Relations are Octane says:

“Historical data can’t be a good determinant for the forecasting because the pace of change from one increment to the next is so great, that by the time the data is recorded, the opportunity or the threat is gone. With driver-based forecasting, you can build, and export different forecasts based on what-if scenarios.”

What is driver-based forecasting?

Anthony says:

“There is a huge opportunity for these guys to pick up projects from failing construction companies. However, the question remains: which ones are good and which ones are duds?”

Driver-based forecasting uses the key levers or drivers in your business to help you evaluate the impact of internal or external changes. Let’s take an example of acquisitions of construction projects. The predictive models can break down costs for each job and determine the profitability to help you discern which projects to acquire.

Billables are another important driver. Billing data on market rates help you forecast your payables and receivables. Billing analytics can help establish efficient accounting that is free from duplicate invoices, disputes, and gaps in price margins. A very good example would be equipment costs. It doesn’t matter if you are buying or renting the equipment, the forecasts based on the traditional approach miss by as much as 40%. Leveraging forecasts from real-time data can easily mitigate such over or underestimation.

Amendra warns:

“Companies need to have the tools and data at their fingertips to allow themselves to be able to see the insights by performing what-if scenarios on drivers to optimise their cost base and their funding arrangements with their banks and financial institutions.”

Driver-based forecasting needs a lot of data

The sources of data will be varied. Ranging from the financial data from the suppliers, contractors, and other participants in your network to the data that your ERP platform collects from the operations and forecasts inside your own organisation.

On top of that, non-financial data like weather patterns, market data that captures the shift in costs of materials, interest rates, inflation, labour force, and Government policy along with safety data, waste data, and workflow data that directly or indirectly contribute to the financial decisions and forecasting will increase the total volume of data that your organization needs to capture.

Excel won’t cut it

Some construction companies are still trying to rely on manual methods like spreadsheets. But Excel has limitations. First, it has a hard stop at a million records. We can’t expect Excel to deal with such varied sources of data in real-time with millions of records when it can’t even deal with the company’s own full history of actuals.

Secondly, it’s impossible to collaborate and maintain a single truth across the whole organisation using spreadsheets. Even if you try to maintain huge volumes of real-time data in Excel, it easily breaks when you try to link the logic.

Anthony says:

“CFOs tell the same story of how they came across the forecasts from inefficient spreadsheets that took too long to put together. They say if only they could have known about it 30 days ago, they could have taken evasive actions.”

Don’t sweat it. This is easier than you think.

So there has to be a way to unify data and maintain a single source of truth for analysis eliminating the need to pull it from the disconnected databases and spreadsheets. Such a platform will enable your company to absorb internal and external data increasing the speed of reporting while reducing errors and inefficiencies involved in the manual processes.

Anthony remains optimistic:

“We don’t want you to be afraid of the huge amount of data. Embrace all the data that comes from your internal departments and from external sources. But don’t be restricted by Excel because it simply lacks the agility and dexterity to crunch the big data that’s going to be in your hands soon. TM1 can bring millions and millions of records from your historical and real-time data sources like a snack in a matter of milliseconds.”

Accurate forecasts and reports from IBM TM1 Planning Analytics add agility, speed, and accuracy to thrive during and after disruptions in the industry. Forecasts based on real-time data can mitigate your risk by detecting triggers and optimizing the costs to improve your profitability while you develop your rate models and enter negotiations for competitive proposals.

Amendra concludes:

“In fact, most construction companies can achieve driver-based forecasting in eight weeks and for less than A$80,000. I’m surprised not more of them are shifting.”

CFOs must transform themselves if they are to transform construction

15 August 2022, 9:00:00 am AEST

5 min

Australian construction companies that will thrive during the recent consolidation will blend corporate strategy and digital analytic strategy. CFOs will move away from traditional models to adopt digital strategic planning models that rely on data insights while making decisions.

IMG_20220726_100207

Aspen Medical CFO, Moazam Shah, noted some of the key competencies for finance professionals to thrive in a digital world, including knowledge of digital technologies, ability to articulate their use to a wider audience in the business, an ability to learn and adapt to changes in technologies, and to a motivation to embrace new ways of working.^[1]

“My role has not just been about ramping up our global finance workforce to support COVID projects and facilitate fast-moving projects. Above all, my key goal has been to drive digital transformation. Thank goodness we have the technology, which has supported us in everything we do”^[2]

said Aspen, CFO Moazam Shah.

CFOs are becoming catalysts for growth

A Brainyard survey of leaders across 21 industries showed that most CFOs are responsible for functions outside of finance. Whether by focusing on strategic partnerships, evaluating technology, or working to meet revenue and earnings goals, CFOs are expected to be strategic catalysts of company growth — not just the head of the financial organization.^[3]

Construction companies need to be ready to ramp up their digital initiatives to capture and make sense of the data, both financial and non-financial, that they will collect from varied sources. CFOs can play a vital role in establishing a proper data pipeline for collecting, storing, and analysing data.

Accenture has found that 76% of CFOs believe unifying disparate data is vital to achieving business objectives.^[4] So, CFOs of construction companies will find themselves responsible for removing financial data disparity in the organization to establish the single truth throughout the organisation.

CFOs will also have to be able to introduce agility in their financial planning using tools that will allow them to adapt to the market by creating models which predict the real-time changes in the market. The construction companies can’t become agile by planning 12 months in advance. That means the whole industry will have to start moving away from fixed-price contracts.

CFOs are declaring war on spreadsheets

They have decided they need smarter, quicker, decision-makers if they are going to snatch the right deals. That means the data needs to be current and accurate. Unifying data for analysis eliminates the need to pull it in from disconnected databases and spreadsheets. At the same time, a single source of data also increases the speed of reporting and reduces the inefficiency and errors inherent in manual processes.

“It took so long to build the forecast in Excel that the opportunity has been and gone.”

“If we knew that 30 days ago, we could have done something about it.”

A separate study by DataRails found that 70% of CFOs rely on Excel for financial budgeting and forecasting.^[5]

What you need is a platform that is able to absorb internal and external data, and the forecasts made by your frontline people. It will need to bring this together into neat and tidy reports your executives can read to make decisions at speed.

In other words, this group is solving the data disparity within their organization by reaching for a single platform to become the source of truth.

According to NetSuite Brainyard’s white paper, “State of the CFO Role,” 186 CFOs respondents said they spend an average of 2 hours in spreadsheets every day. That’s more time in spreadsheets than any other software in their toolkit.^[6]

CFOs are removing data disparity

The primary goal for CFOs in the construction industry is to find a solution that can remove data disparity, that can be rapidly deployed in a lean form and that can be scaled to benefit from the investment in such a solution. Amendra says:

“The roles of CFOs are changing from being the numbers people to being more and more of a trusted advisor of which direction a company is going to go in terms of financial transformation. Their advisory needs to be based on data and reality instead of a gut feeling.”

CFOs are adopting driver-based forecasting

Driver-based forecasting uses the key levers or drivers in your business to help you evaluate the impact of internal or external changes. Forecasting P+L and balance sheets based on actuals and historical data is proving less reliable. In the Australian construction industry alone, market conditions have moved so quickly in the last 24 months, that history can no longer be trusted to tell the future.

Financial transformation in the Australian construction industry

2 August 2022, 5:17:27 pm AEST

8 min

Home Innovation Builders, Dyldam Developments, Hotondo Homes franchise, Tasmanian Constructions, ABD Group, BA Murphy, Pindan, and Inside Out Construction have all gone bust. It’s time CFOs from the construction embraced financial transformation.

IMG_20220726_093451

Construction giants like Probuild and Condev have collapsed. Many other construction companies are looking at downturns and the huge debt that’ll push them into a similar path to liquidation. Amendra Pratap, Managing Director at Octane, say:

“The rate of construction companies falling and going bankrupt is at an all-time high. The major reason behind such downfalls is the rise in the cost of operation and construction materials.”

The construction sector inflation will hit 6% over the year till December 2022 according to a report by Macromonitor. Matthew Mackey, executive director of engineering company Arcadis, told Daily Mail Australia: ^[1]

“I don't think a lot of companies are taking the cost increases seriously. It's a perfect storm”,

The market has become highly volatile. Even after the supply chains have become responsive, the material costs and commodity prices are shooting through the roof. Industry bodies have reported soaring timber prices by 50% to 100% in 2021. Steel has gone up between 30% to 60%. Similarly, concrete has gone up by 20% to 40%. Moscow’s war in Ukraine has also affected engineered timber products coming into Australia from Russia which added a further 25% price increase this year. Delays on other manufactured products, such as roof trusses, glass, and steel are also extending the time it takes to deliver projects and pushing up costs for the contractors. ^[2]

While companies try to manage big orders, they are struggling because they have locked themselves into agreements prior to the rise in costs.

Solid_Icon_ Building materials graph

“Fixed price contracts have brought down most of these builders as it makes them have no ability to pass on cost increases on timber and steel accounting”,

said Paul Bidwell, Master Builders Queensland deputy chief executive.^[3] Adam Merlehan at SmartCompany quite appropriately pointed out that the construction industry is besieged by a profitless boom. Amendra says:

“When construction companies signed a contract for 6 months they didn’t take into account the sheer rise in the price of the materials and now it has left them exposed to a high cost and a thin profit margin. But legally they couldn’t raise the price and that’s why the deals automatically collapse.”

Condev-infographic-3 Thin profit graph

CFOs think the glass is half full

According to AFR, Australian CFOs are feeling optimistic about the prospects for their companies in 2022.^[4]Deloitte in 2022 reported that over 80% of the country’s senior finance executives feel optimistic about the prospects for their company over the next 12 months. Deloitte partner, and CFO Program leader, Stephen Gustafson said:

“CFOs have acclimatised to tumultuous times, and with more confidence comes a greater appetite for risk. 41% of CFOs think they are under-geared, and 66% agree that now is a good time to be taking greater risk onto their balance sheets, up from 53% six months ago.”^[5]

Now is the time to capitalise on the change.

Transform or die trying

Andrew Spring, partner at building insolvency specialist Jirsch Sutherland said:

"I wonder whether, off the back of this, we may need to see a reset in the industry as to how they go about pricing for work and assessing how to deal with these types of really rapid, difficult fluctuations." ^[6]

Andrew’s comments could not be further from the truth. Low profits, stagnant growth, and a huge dive into customer satisfaction have created a dire need for financial transformation in the industry.

We expect the consolidation in the construction industry to rapidly transform the whole industry in the next two years. At the end of this intense reshuffling, there’ll only be three types of companies left:

The group who does not survive at all
The group who survives – the majority
The group that thrives by seizing the opportunity

Amendra is quick to make the distinction and says:

“The quicker you evolve and the quicker you transform your department and your company into an entity that is smarter and makes data-based decisions, you’ll have a clear pathway to joining the list of companies that will be thriving.”

The companies that will thrive due to the financial transformation will be able to buy out all the collapsing deals and come out of this stronger than ever. Equipped with the right financial tools, they can separate the gems from the weeds in terms of deals they consider.

Financial transformation will not only help construction companies have greater control over their value chain but also help them take the leap toward data-driven decision-making.

Manufacturing learnt their lesson long ago

The construction industry is not the first industry to encounter such disruption. Lessons can be drawn from other industries with similar attributes that went through similar disruptions. In the manufacturing industry, many players who thrived during and after the disruption were the ones who adopted digital and cloud-based solutions. As we discussed above, the same will happen for construction companies, the winning players will soon adopt cloud-based solutions.

Financial transformation in construction has begun

As of now, it has started from the integrated production and distribution of electronic documents. An increasing number of suppliers and contractors are making heavy use of mobile apps to perform tasks while collecting, storing, and segmenting data to analyse, forecast, and fix productivity gaps in their projects and inefficiencies in their organisational health.

Contractors have realized the benefits of having real-time information on their hands to make critical forecasting and decisions. It not only provides them with a single source of truth for operations in their organization but also peace of mind because they have the backups in their cloud. Improvements in the ability to gather and analyse data will follow with the advancement of mobile and cloud technologies that make it possible to pull data from real-time sources.

A cost-benefit analysis is key to financial transformation

Anthony Coundouris, Head of Client Relations at Octane, recommends that companies not jump too quickly into buying software. He says:

“You need to build a business case first. What it will cost and what it will return. CFOs who can do this stand a much higher chance of getting the organisation to buy in to financial transformation”.

Dexter Clarke, CFO of vehicle servicing company Motorserve, suggests other CFOs spend substantial time upfront understanding and mapping processes. He says:

“You need to do the work upfront to understand existing processes. If you don’t do that, don’t think a different system will give you an improved process. You’ve got to improve the process and understand where the waste is before you start talking to software vendors." ^[7]

Octane Software Solution Partner With QUBEdocs to Deliver Cutting Edge

28 June 2021, 11:03:19 am AEST

3 min

Octane Software Solutions is a cutting edge technology and services provider to the Office of Finance. Octane is partnered with vendors like IBM and BlackLine to provide AI-based solutions to help finance teams automate their processes and increase their ability to provide business value to the enterprise.

Qubedocs is an automated IBM Planning Analytics documenter. It generates automated documentation within minutes and ensures compliance and knowledge management within your organisation. So, we're excited to announce our partnership with QUBEdocs - a solution that takes the resources and headaches out of TM1 modelling. In this article, we discuss common challenges with Planning Analytics and how QUBEdocs transforms this process.

Challenges with Planning Analytics (TM1)

Our experience in the industry has meant we've worked with many enterprises that encounter challenges with Planning Analytics. Common concerns and challenges that our clients face are listed here:

Correct documentation
Over-reliance on developers, which leaves businesses vulnerable.
Unable to visualise the full model, resulting in not understanding the information and misinterpreting the model.
Are business rules working correctly?
Understanding data cubes
Disaster recovery and causation analysis
Managing audit
Compliance with IBM licence rules

Reading through these challenges can paint the picture of a complicated process to manage and support. They cover a broad range of concerns, from first ensuring the documentation is correct, understanding the data and information, and not knowing if they're doing it right. Automating this process can take the guesswork and lack of confidence out of the models.

How QUBEdocs transforms the process

We've partnered with QUBEdocs because of its capabilities to transform the TM1 Models. Through QUBEdocs you can generate custom documentation in minutes (as opposed to months) for your IBM Planning Analytics TM1. You're able to meet your regulatory requirements, capture company-wide knowledge and gain an accurate, updated view of TM1 model dependencies.

Below is a list of benefits that QUBEdocs offers:

Purpose-built

Specifically built for business intelligence, QUBEdocs allows seamless integration with IBM Planning Analytics.

Fully automated documentation

QUBEdocs focuses on driving business value while documenting every single detail. Automating the documentation takes the errors out of the process and ensures your plans are knowledge-driven.

Personalised reporting

QUBEdocs keeps track of all the layers of data that are important to you – choose from standard reporting templates or customise what you want to see.

Compare models

Compare different versions of your model to gain complete visibility and pinpoint changes and potential vulnerabilities.

Cloud-based

QUBEdocs up-to-date features and functionalities need no infrastructure to use and allows collaborative, remote working.

Data with context

Context is critical to data-driven decisions. Every result in QUBEdocs is supported by context, so you understand before you act.

Model analysis

Models offer a way to look at your applications, objects or relationships in-depth. Analysing your models can help you understand your complex models intuitively, so you know each part of your business and what it needs to succeed.

Dashboards

Understand your server environment at a glance with key metrics tailored for different stakeholders in your business.

Summary

This article has outlined the benefits of QUBEdocs, and why we're excited to announce our partnership. Though, when you work with Octane Software Solutions, you get a company in it for the long haul until you've grown into your new wings. If QUBEdocs is right for you, a big part of our processes is implementing it into your organisation so that it's fully enabled to improve your business performance.

Learn more about QUBEdocs or join our upcoming webinar; How to automate your Planning Analytics (TM1) documentation.

Planning Analytics with Watson (TM1) Training made easy

28 May 2021, 4:32:47 pm AEST

4 min

We have made it easier for your users to access Planning Analytics with Watson (TM1) Training.

This week we launched our online training for Planning Analytics with Watson PAW and PAX; available online in instructor-led, 4-hour sessions.

Planning Analytics with Watson (TM1) Training

This training provides the ideal time for you to spend some of your allocated training budgets; often assigned but never utilised on something that you can actually apply in your workplace. We have made it easy for you to book your training online in a few easy steps.

IBM has been consistently improving and adding new features to PAW and PAX. To maximise your training outcome, we will run the training on the latest (or very close to the latest) release of PAW and PAX; which will give you a good insight into what new features are available. Our training will speed up your understanding of the new features and help you decide on your upgrade decisions. The best part of our training offering is that we have priced it at only $99 AUD – this is a great value.

Being interactive instructor-led TM1 training, you would be able to ask questions and get clarifications in real-time. Attending this training will ensure that you and your staff are up-to-date with the latest versions and functionalities.

Training outcomes

Having your users trained up will mean that you can utilise your Planning Analytics with Watson (TM1) application to its full potential. Users would be able to self service their analytics and reporting. They would also be logging a reduced number of tickets as they understand how to use the system. Engagement would go up as they actively participate in providing feedback on your model's evolution. Overall, you should expect to see an increase in productivity from your users.

PAW Training Overview

Introduction of PA and workspace
Welcome page
Creating books
Creating views
Hiding row or columns/rows and columns in views
Snap commands
Selector widget
Synchronising objects in a book or sheet
Adding navigation button to sheet
Dataset export
Visualisations
Creating metric visualisations
Add text box
Work with images
End-user calculations
Using MDX based subsets

PAX Training Overview

Introduction to PAX
Overview and list components
Setup IBM connection, connecting data source, open workbook
Working with data and reports
Clear cell content
Convert dynamic data to snapshots
Exploration views
Lists
Quick report
Dynamic report
Custom report
Publish workbooks
Sets for TM1
IBM TM1 functions
Cube viewer
Action buttons

Training delivery

The training course will be delivered online by Octane senior consultants, who have more than 10-15 years of delivery experience. The class size is limited to only 12 attendees to ensure all attendees get enough focus.

The training sessions are scheduled in multiple, so you should be able to find a slot that is suitable for you.

Have you got any questions?

We have captured most of the questions we've been asked on this FAQ page.

I look forward to seeing you at training.

What's in a name? Watson in the name!

22 April 2021, 3:10:50 pm AEST

2 min

Starting 1 April 2021, "with Watson" will be added to the name of the IBM Planning Analytics solution.

IBM® Planning Analytics with Watson will be the official product name represented on the IBM website, in the product login and documentation, as well as in marketing collateral. However, IBM TM1^® text will be maintained in descriptions of planning analytics' capabilities, differentiators and benefits.

What is the "Watson" in Planning Analytics with Watson?

The cognitive help feature within Planning Analytics with Watson is the help system used in IBM Planning Analytics Workspace (Cloud). This feature uses machine learning and natural language processing to drive clients towards better content that is more tailored to the user's needs. As clients interact with the help system, the system creates a content profile of the content they are viewing and what they are searching for.

Branding benefits of the name

Utilize the IBM Watson® brand, a leader in the technology and enterprise space, to gain a competitive advantage

AI and predictive as differentiators to how we approach planning
Amplify the reach of planning analytics to our target audience and analysts through Watson marketing activities

What do we think?

We are pleased to note that the name TM1 remains with the product. The Planning analytics product has evolved significantly from the early days of Applix. We had initial apprehension when IBM acquiring TM1 via Cognos acquisition (IBM acquired Cognos in January 2008 for USD $4.9 Billion). We naturally assumed that this little gem of a product would be lost in the vast portfolio of IBM software.

However, it's quite pleasing to see TM1 thrive under IBM. It received significant R&D funding and made TM1 into an enterprise planning tool. We saw the development of the workspace, which brought in the modern dashboard and reporting features. Move to Pax saw us get an even better excel interface and, just lately, the workspace feature that manages a complex enterprise workflow.

The biggest gamechanger was making Planning Analytics available as a Software as a Service (you can still get it as an on-premise solution). This meant that the time to deploy was reduced to a couple of days. There is no cost to the business in maintaining the application in doing any patches and upgrades. Gone are the days of IT and Finance at loggerheads over the application. The stability and speed of Planning Analytics as a SaaS product has pleasantly surprised even us believers!

Adding Watson to the name is timely as AI-infused features around predictive forecasting is getting more prevalent. There is no doubt that IBM Planning Analytics with Watson is the most powerful AI-based Planning tool available. It's time to acknowledge the future of where we are going.

What do you think of the name change? Share with us your thoughts.

Data Analysis using Dynamic Array formulas

17 September 2020, 4:30:00 pm AEST

6 min

How to create reports using dynamic array formulas in Planning Analytics TM1

In our previous blog (https://blog.octanesolutions.com.au/what-are-dynamic-array-formulas-and-why-you-should-use-them), we discussed about Dynamic Array formulas and highlighted the key reasons and advantages to start using DA formulas.

In this blog, we will try to create a few intuitive reports based on custom reports built on PAfE. The data set we will be using is shows the employee details in “Employee” cube with the following dimensionality:

Dimension	Measure
Year	Department
Version	Name/Desc
Sr.No	Current Salary
Organisation	Joining Date
Measure

Below is the screenshot of my PA data that I will be using for this blog:

For ease of formula entry, I’ve created a named range for column B to F.

Now that we’ve set the base, lets start off with generating some useful insights with our dataset.

Get the employees with top/bottom 3 salaries
Sum data based on date range
Create searchable drop down list

Formula in cell J22 is as below:

I will try to breakdown the formula to explain in simple language:

We used Filter function which is a DA formula. The Excel FILTER function filters a range of data based on supplied criteria, and extracts matching records. It works in a similar way to VLOOKUP except that VLOOKUP returns a single value, whereas Filter returns one or more values that qualify a criteria. Filter takes three arguments; Array, Include and If_Empty. We passed the employee and salary list as the array in our formula and for inclusion we used a LARGE function (that returns the x largest value in an array where x is a number) and compared it with all the salaries using greater than or equal to operator.

With this criteria, the array is filtered to those employees whose salary is greater than or equal to the 3rd most largest salary.

Similarly, if you wish to filter the employees by 3 lowest salaries. Use the below formula to achieve the same:

A very common analysis that is done based on date range is summarising or calculating the average of data between start and end date. So lets see how we can achieve this using the DA formula. The scenario is, the analyst wants to see what is the sum of the salaries paid for all the periods between Jan 2019 till Dec 2019.

Lets first get the list using the Filter function and once we’ve the data, it is very easy to just summarise it.

Formula in cell H22 is as below:

The concept is similar to the previous one where we’re getting a list of employees with their salaries and joining dates, based on a set condition. Here we’re using AND condition to filter the data based on two date ranges, where joining date of employees is greater than or equal to Date From and less than or equal to To Date. We had to use the NUMBERVALUE function to convert the date that is stored as string data in Planning Analytics to numeric value for doing logical comparison.

Now that we know we can apply the same condition within the Filter function that only returns the Salary and wrap it up inside the SUM function to summarise the salaries.

Formula in cell L19:

In PAfE, a SUBNM is used to search and select the elements in a dimension. However, there is currently no provision to filter the list of elements in SUBNM list to only show selected elements basis that matches the text, let alone wild card search. One of the cool things we can do with DA formulas is to be able to create a searchable drop down list.

Lets create a searchable drop down list for the Department now and see how it works.

In the screenshot above, I’ve entered letter i in cell H7 which is a Data Validation list in Excel and the drop down lists all the departments which have letter i in it. The actual formula is written in cell I1 and that cell is referenced in the Source field of Data Validation.

I’ve used a Hash(#) character in the source to refer to an entire spill range that the formula in I1 returns.

Formula in cell I1:

I’ve wrapped a Filter function within a UNIQUE function that is another DA function that returns a unique list of values within an array. The Filter function uses SEARCH function to return a value if a match is found which is then wrapped inside ISNUMBER to return a Boolean value.

Note:: While the example uses custom report, the same named ranges can very well be created in Dynamic report using OFFSET function to do the same so this analysis is not just restricted to sliced report but also Dynamic aka Active Form report.

So these are just a few of the super easy and on the fly analysis we can do using DA functions to start with that can take the reporting capabilities of PAfE to a whole new level.

https://support.microsoft.com/en-us/office/implicit-intersection-operator-ce3be07b-0101-4450-a24e-c1c999be2b34?ui=en-us&rs=en-us&ad=us

Dynamic Array formulas in IBM PA TM1 - Supercharge your Excel report

11 September 2020, 12:36:58 pm AEST

4 min

Dynamic Array Formulas in IBM PA TM1 (1)

What are Dynamic Array formulas and why you should use them?

In this blog article (and few other upcoming blogs), I am going to write about the capabilities of Dynamic Array (DA) functions with examples and demonstrate to you some great features it has that I believe can empower the PA Analysts to do all differently sorts of data analysis in a simpler and much more intuitive way, thereby enhancing their productivity.

To start off, lets first understand what Dynamic Array functions actually are?

To put it simply, the DA functions are those functions that leverages Excel’s latest DA calculation behavior where you no more have to enter CSE(Control+Shift+Enter) to spill the formulas or in fact copy pasting the formula for each value you wanted returned to the grid.

With DA you simply enter the formula in one cell and hit enter and it will result in an array of values returned to the gird, also known as spilling.

The Array functions are currently only supported in Office 365 but according to Microsoft, it will be extended other versions soon.

Typically when you enter a formula that may return an Array in Older version of Excel and then open it in DA version of Excel, you would get an @ sign – also know as implicit intersection - before the formula. This is added by Excel automatically for all the formulas that it considers might potentially return an multi-cell ranges. By having this sign, Excel ensures formulas that can return multiple values in DA compatible version would always return just one value and it does not spill.

Following is the information on implicit intersection available on the Microsoft website:

With the advent of dynamic arrays, Excel is no longer limited to returning single values from formulas, so invisible implicit intersection is no longer needed. Where an old Excel formula could invisibly trigger implicit intersection, dynamic array enabled Excel shows where it would have occurred. With the initial release of dynamic arrays, Excel indicated where this occurred by using the SINGLE function. However, based on user feedback, we’ve moved to a more succinct notation: the @ operator.

Note: According to Microsoft, this shouldn’t impact the current formulas, however few Planning Analytics clients have already complained of issues having @ in DBRW formulas in PAfE where it no more works. The @ sign that had to be manually removed from all DBRW formulas to make it work. This is a bit of bummer because depending on the number of reports, it may lead to significant amount of work to do this task, a VBA might be a of relief here otherwise it a bit of tedious task.

More on Implicit Intersection can be found in below link:

Additionally, there is another key update that must be made in Excel setting to address another bizarre side effect of implicit intersection observed in Dynamic reports. See below link for details:

https://www.ibm.com/support/pages/unable-expandcollapse-tm1rptrow-dynamic-report-shows-mail-icon

Below are the list of DA formulas currently available in Excel 365.

• FILTER

• RANDARRAY

• SEQUENCE

• SORT

• SORTBY

• UNIQUE

I will be covering off a bit more in detail on these functions in my subsequent blogs to showcase real power of these functions so hang in there till then.

As for why you need to use them, below are some of the reasons I’ve listed to bring on the table:

1. It compliments the PAfE capabilities and fills the gaps where PAfE could not due to its limitations

2. Can open up the world of new data analysis capabilities

3. Once you understand the formulas and the Boolean concept (which is not complicated by any means), It’s true potential could be realised

4. It is simple yet very powerful and a big time-saver

5. With formula sitting only in one cell, it is less error prone

6. The calculation performance is super-fast

7. CSE no more!

8. It is backward compatible, meaning you need not worry how your DA results appear in legacy excel as long as you’re not using the DA fun

9. Updates are easy to make – need to only update in once cell as opposed to all cells

This is my value proposition for why you should use DA formulas. I’ve so far not yet demonstrated what I proposed which I intend to do in my later blogs, till then thanks for reading folks and stay safe.

Octane Celebrates 4th Anniversary

18 August 2020, 8:08:33 pm AEST

6 min

2020 has been an interesting year for us !

Celebrations

This month Octane celebrates our 4th anniversary. Never imagined that we would be celebrating with our team members across different geographies via online gifts and Teams meetings. Normally we fly in all our team members to one location for the weekend and have a great time bonding. However with the global pandemic we had to adapt as the rest of the world is.

The journey so far

On the whole, reflecting back on the journey so far on our anniversary 2020 certainly has thrown in an riveting challenge. Having started from a small shared office in North of Sydney, Octane today has 7 offices and operates in multiple countries. We were helping some of the largest and diverse enterprises around the world get greater value out of their Planning Analytics applications. Travel to client sites in different cities was always been my favourite job perks. As we were getting into the grips of pandemic in Feb/march we were on a trip to Dubai and Mumbai meeting clients and staff. There was a bit of concern in the air but none of us had any idea that travel would come to a standstill. We suddenly found ourselves coordinating with our staff, arranging for their travel safely back to their homes; Navigating the multiple quarantine regimes of different countries and fighting for seats on limited flights.

Octane Team at client site in Dubai

Dubai Mall - One of our clients

One of the team outings in Delhi

We had a team of consultants assisting Fiji Airways with their Finance Transformation. The travel restrictions and a volatile economy meant that Fiji Airways had to swiftly change gears and make use of our team to assist in business scenario planning and modelling. This was a exemplary example of how a organisation reacted quickly and adopted a distinct business model to face the challenges. Thankfully their platform supported Scenario Modelling and could handle What-if Analysis handling at scale with ease (same cannot be said for some of the other enterprises that had to resort to excel or burn midnight oil to provide the right insights to the business - This is why we love Planning Analytics TM1!)

Fiji Airways (our client) on Tarmac at Nadi Airport

Sameer Syed at the Welcoming ceremony of Fiji Airways A350 aircraft

The Silver Lining for Octane

With Pandemic came a rapid rethink of the business model for most organisations. We at Octane were already geared up to provide remote development and support of TM1 applications. With our offshore centres and built-in economies of scale, we were in a position to reduce the overall cost of providing development and support. This gained a lot more traction as organisations started evaluating their costs and realised we are able to provide better quality of services at a lower cost without a hitch. We internally tweaked our model to reduce barriers of entry for companies wishing to take up our Managed Service options. We already had a 24/7 Support structure in place which meant that we could provide uninterrupted service to any client anywhere in the world in their time zones.

Within Octane we were also operating in a crisis mode with daily Management calls. Ensuring safety and well-being of staff was our first priority as different countries and cities brought in the lockdowns. We remained agile and forged tactical plans with clients to ensure there were minimal disruptions to their business. Working from home was the new normal. We already had all the technology to support this and specialise in remote support so this was an fairly easy exercise for us.From the lows in May, Slowly but steadily our business model started to gain traction as we focused on client service and not profits.

Growth Plans and Announcements

In the chaos of 2020 it was also important to us to continue with our growth plans. We had to tweak our strategy and put opening new offices on hold in some countries.Travel restrictions and move to a new business model by clients meant we did not need to be present in their offices.

One major announcement is that Octane has signed up as a business partner Blackline. Blackline is a fast growing Financial Close and Finance Automation System. It fits in well with our current offering to the office of Finance and operations.

The other significant milestone was the launch of DataFusion. This is a connector developed in-house to connect Planning Analytics TM1 to PowerBI, Tableau or Qlik seamlessly. These are some of the common reporting tools and typically require manual data uploads. This leads to reconciliation issues and untimeliness of reporting data. This has resonated very well with the TM1 community.

We also have a number of vendors who are discussing partnership opportunities with us and we will be making these announcements as they get finalised. This is largely a manifestation or realisation that in the current climate our business model of onshore/offshore hybrid model provides the best cost benefit equation for clients.

Octane Community

We at Octane have always been part of the community and have been hosting User Groups in all the cities we operate in. With the onset of Covid we have stressing our efforts in hosting a monthly User Group meetup. . Our meetups are generally focused on providing tips/tricks and “how to “ sessions for existing user base of Planning Analytics. The registration of the User groups have been increasing steadily.

As a part of our corporate social responsibility undertaking, we also try and support different community groups.. Octane sponsored a Drive in a Lamborghini in the NSW WRX clubs Annual North rally which raises funds for Cystic Fibrosis of NSW. One of friends who I used to race with, Liam Wild, succumbed to the disease in 2012.

This year my kids also started competing in the Motorkhana series with me and this has been great fun and a welcome distraction during the pandemic as we bonded (fought) during the long hours in the garage and practice runs.

Looking back, I would like to express my sincere gratitude for the trust and support Octane has received. With the pandemic here to stay at least until the end of this year, wishing for a blessed and successful 2021 for all.

Race Days - trick to beating them is to give them a slower car

Mud Bath - Social Distancing done right

20200801_130657-2

Clean and ready for next high Octane adventure

Session Timeout for TM1Web, PAW and PAX

6 August 2020, 12:00:00 am AEST

3 min

We might often get a request from users that TM1 session has logged. As per the client requirement and standards, need to increase or decrease the session timeout. Changing the session timeout is a trade-off, it should not be too big or too small. If it’s too big then many inactive sessions can lead to server performance issues. If it is too small, then the user experience might be affected.

Each application of TM1 has its separate session timeout parameter. Jump into the respective section, depending upon your need.

TM1Web

1. Go to <Installation Folder>\IBM\cognos\tm1_64\webapps\tm1web\WEB-INF\configuration and Open tm1web_config.xml file.

2. Change the HttpSessionTimeout to desired value.

a. Please note timeout value to be mentioned in minutes

3. Save and close the tm1web_config.xml file.

4. Restart the IBM TM1 Application Server service.

PAX

Go to http://localhost:9510/pmhub/pm/admin. Below screen appears.

2. Sign-in using your credential at top right corner.

3. Expand Configurations and go to PMHub Session.

4. Change MaxInactivity Timeout value. Default value is 3600 sec.

PAW

1. Go to <PAW Installation Folder>\paw\config and open paw.env and defaults.env file.

2. Copy “export SessionTimeout” parameter from defaults.env file and add it to paw.env file with desired value and save.

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Adding images in PAW

21 April 2020, 8:31:49 am AEST

4 min

In this article I would like to share a very useful tip on how we can use different methods to adding images in Planning Analytics Workspace; one that is very well known, one that is lesser-known and one that is relatively unknown. I intend to touch base on the first two methods while focusing more on the latter one.

But before I begin, as I write this blog article, there has been more than 2 million confirmed cases of COVID-19 worldwide with over 130,00 deaths and I wish to take a moment on behalf of Octane Software Solutions and express our deepest condolences with all those and their family members who have directly or indirectly suffered and had been affected by the pandemic and our thoughts go to them.

And at the same breath a special shout out and our gratitude to the entire medical fraternity, the law enforcement, various NGOs and numerous other individuals, agencies and groups both locally and globally who has been putting their lives at stake to combat this pandemic and help the needy around. Thank you to those on the frontline and the unsung heroes of the COVID-19. It is my firm belief that together we will succeed in the fight.

Back to the topic, one of the most used methods for adding images in PAW is to upload it in some content management and file sharing sites like BOX or SharePoint and paste the web link in the PAW Image Url field. Refer the link below where Paul Young demonstrates how to add an image using this method.

The other method is to upload your image to an encoding website like https://www.base64-image.de.

This provides a string which can then be pasted as Image url to display the image. Note that it only works on limited file formats and on small sized images.

Also note that albeit the above two methods achieves the purpose of adding images in PAW none of them provides the capability to store the images in a physical drive in order to keep a repository of the images used in PAW easily saved and accessible in your organizations’ shared drive.

The third approach addresses this limitation as it allows us to create a shared drive, store our images in it and then reference it in PAW.

This can be done by creating a website in IIS manager using few simple steps as listed below.

First off, before you can begin, ensure IIS is enabled in your data server as a prerequisite step. This can be done by simply searching IIS in your Windows menu.

Incase no results are displayed, it means it has not been enabled yet.

To enable, go to Control Panel à Programs à Turn Windows feature on or off

A wizard opens, click Next. Select Role-based or feature-based installation and click Next.

Select the server if its not already selected (typically data server where you’re enabling the IIS)

Select the Web Server check box and click Next

Select IIS Hostable Web Core and and click Install.

This installs the required IIS components on the server so we can now proceed to add the website in IIS Manager.

Before adding a website, navigate to C:\inetpub\wwwroot\ and create a folder in this directory. This will be the folder where we will store our images.

Once IIS is enabled follow the below steps:

1. Under Sites right click and select Add Website.

2. Update the following configuration settings

a. Site name: Enter the name of the site

b. Physical path: Enter the folder path we created in earlier step

c. Port: Enter any unreserved port number

d. Host name: Enter the machine name

Now go to PAW and enter the image URL.

Where ibmpa.jpg is the image saved within PAWImage folder.

Note: This only works in Planning Analytics Local.

Octane Software Solutions is a IBM Gold Business Partner, specialising in TM1, Planning Analytics, Planning Analytics Workspace and Cognos Analytics, Descriptive, Predictive and Prescriptive Analytics.

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Predictive & Prescriptive-Analytics

Automation in TM1 using AutoHotkey

2 March 2020, 2:00:36 pm AEDT

3 min

This blog explains a few TM1 tasks which can be automated using AutoHotKey. For those who don't already know, AutoHotKey is open-source scripting language used for automation.

1. Run TM1 Process history from TM1 Serverlog :

With the help of AutoHotKey, we can read each line of a text file by using loop function and content is stored automatically in an in-built variable of function. We can also read filenames inside a folder using same function and again filenames will be stored in an in-built variable. Therefore, by making use of this we can extract Tm1 process information from Tm1 Serverlog and display the extracted information in a GUI. Let’s go through the output of an AutoHotKey script which gives details of process run.1.

Below is the Screenshot of output when script is executed. Here we need to give log folder and data folder a path.
After giving the details and clicking OK, list of processes in the data folder of Server is displayed in GUI.
Once list of processes are displayed, double-click on process to get process run history. In below screenshot we can see Status, Date, Time, Average Time of process, error message and username who has executed the process. Thereby showing TM1 process history in TM1 server log.

2. Opening TM1Top after updating tm1top.ini file and killing a process thread

With the help of same loop function which we had used earlier, we can read tm1top.ini file and update it using fileappend function in AutoHotKey. Let’s again go through the output of an AutoHotKey script which will open Tm1top.

When script is executed, below screen comes up which will ask whether to update adminhost parameter of tm1top.ini file or not.
Clicking “Yes”, new screen comes up where new adminhost is required to be entered.
After entering value, new screen will ask whether to update servername parameter of tm1top.ini file or not.
Clicking “Yes”, new screen comes up where new servername is required to be entered.
After entering a value, Tm1Top is displayed. For verifying access, username and password is required
Once access is verified, just enter the thread id which needs to be cancelled or killed.

PA Local PA Cloud TM1 tech tips

Potential Data Loss: Quick Fix a must : PA Cloud and PA Local

20 December 2019, 2:18:00 pm AEDT

5 min

IBM has identified a defect within the code introduced in TM1 10.2.2 Fixpack 7, part of all other releases before 2.0.9. This defect causes data loss within the cubes even after performing SaveDataAll activity with in TM1 server. Let us get into the details.

What is the defect :

Possibility of losing data even after SaveDataAll activity is performed. This defect (APAR PH19984) has been identified recently by IBM. This will only trigger when below conditions are met. srini1

No-SaveDataAll : If SavedataAll not performed since TM1 Server was rebooted.
Lock Contention : Lock contention specific to public subset, TI process or chore.
Rollback : SavedDataAll thread rollback due to lock contention.
ServerRestart : TM1 server restarts following above mentioned points.

How to Find:

To find if TM1 Server might encounter this issue, pls follow below steps.

If not already enabled, enabled debug options in tm1s-log.properties.
TM1.Lock.Exception=DEBUG
TM1.SaveDataAll=DEBUG
Identify SaveDataAll thread, look for “Starting SaveDataAll” in tm1server.log.
Check if lock contention rollback on SaveDataAll has been triggered in tm1server.log. Look for “CommitActionLogRollback: Called for thread ‘xxxxx’”, check if xxxxx is SaveDataAll thread.
If “CommitActionLogRollback: Called for thread ‘xxxxx’” is found before ‘Leaving SaveDataAll critical section’ – there is high change you are prone to this defect and might cause data loss.

Impacted Users :

All Clients using Planning Analytics On-Cloud and On-Premise (Local) TM1 Server versions 10.2.2 Fix pack 7 and PA version 2.0 till 2.0.9.

How to avoid :

This can be avoided in two ways.

Automate SaveDataAll ( Best practice) to happen at regular intervals, else do this manually.
For PA Local users, Apply fix released by IBM on 17th December 2019, click here for more details.

Octane Software Solutions is a IBM Gold Business Partner, specialising in TM1, Planning Analytics, Planning Analytics Workspace and Cognos Analytics, Descriptive, Predictive and Prescriptive Analytics.

PA Local PA Cloud

PA+ PAW+ PAX (Version Conformance)

27 November 2019, 10:42:11 pm AEDT

5 min

IBM, with the intention of adding new features to Planning Analytics ( TM1) and to its Visualization and reporting tools Planning Analytics Workspace and Planning Analytics for Excel new release are happening at regular intervals.

New version for Planning Analytics Workspace, Planning Analytics for Excel comes out every 15-40 days, for Planning analytics new version comes out every 3-6 months.

In this blog, I will be discussing about the version combinations to be used between these tools to get better/optimum results in terms of utilization, performance, compatibility and bugs fixes.

Planning Analytics for Excel ‘+’ Planning Analytics Workspace:

There are many versions of Planning analytics for Excel (PAX) from 2.0.1 till 2.0.48 latest version released, similarly we have many versions of planning Analytics Workspace(PAW) starting from 2.0.0 to 2.0.47.

Planning Analytics for Excel though installed, can only be used if Planning Analytics Workspace(PAW) is installed and running. So, the question to be answered is - will all versions of PAX work with all versions of PAW ? – Answer is NO. Yes, you read it right – not all versions of PAX are supported by every version of PAW. We have some versions which are supported and some versions which are Optimal, these are covered below.

Supported Versions :

Planning analytics for Excel (PAX) version will be supported by three versions of Planning Analytics Workspace(PAW), matching version, previous version and next version.

Here is an example, current PAX version is 2.0.45 and current PAW version being used is 2.0.45. PAX current version will be supported by PAW version 2.0.45 (matching), 2.0.44 ( previous version) and 2.0.46 (next “version”). I have considered two scenarios to explain this better.

Scenario (PAX upgrade):

Say, a decision has been taken to upgrade PAX version to latest version 2.0.48 from 2.0.45, with above explanation, new PAX will only be supported by PAW ( 2.0.47, 2.0.46, 2.0.48). As existing PAW (being used) is 2.0.45, new PAX is not supported. This upgrade activity PAX (2.048), must include PAW upgrade as well. Planning analytics Workspace (PAW )has to be upgraded from 2.0.45 to PAW (2.0.48, 2.0.48, 2.0.49).

Scenario (PAW upgrade):

Say, a decision has been taken to upgrade PAW version(2.0.45) to version 2.0.47 but PAX existing version is 2.0.45 being used by Users.

If the PAW is upgraded to 2.0.47, it will support PAX versions (2.0.47, 2.0.46, 2.0.48) only. If there is PAW upgrade then PAX must be upgraded to either 2.0.46/ 2.0.47/ 2.0.48 versions as part of PAW upgrade activity.

Best suited/ optimal version :

Planning analytics for Excel (PAX) version though supported by three versions( matching, previous, next) of PAW, optimal results are achieved with matching and next version of Planning Analytics Workspace(PAW).

Here is an example, current PAX version is 2.0.45 and current PAW version being used is 2.0.45. PAX current version, though supported by PAW (2.0.45, 2.0.44 and 2.0.46), optimal are (current and next versions) in this case optimal version is 2.0.45 and 2.0.46.

table 1

Planning Analytics ‘+’ Planning Analytics for Excel:

To check which PAX versions suit Planning Analytics version, we should always consider the bundled PAX/PAW package version as reference to PA.

For example, 2.0.43 PAX version is bundled with 2.0.7 PA version, 2.0.36 PAX is packaged with PA 2.0.6 version.

Supported and Optimal Versions :

Planning Analytics for Microsoft Excel will support three different long cadence versions of Planning Analytics.

Planning Analytics version that was bundled with version of Planning Analytics for Microsoft Excel or the most recent Planning Analytics version that was previously bundled with version of Planning Analytics for Microsoft Excel.
The two previous Planning Analytics versions before the bundled version.

Here is an example, PAX version 2.0.43 is bundled with 2.0.7 PA. PAX 2.0.43 will be supported by PA (2.0.7(bundled version), 2.0.6 (previous) and 2.0.5 (previous)). PAX 2.0.43 will not work well with older version, also note that PAA has been introduced in PA 2.0.5 version.

Below table might help with PAX and PA supported/optimal versions.

table

For more details click here.

Read some of my other blogs :

Business-intelligence vs Business-Analytics

What is IBM Planning Analytics Local

IBM TM1 10.2 vs IBM Planning Analytics

Little known TM1 Feature - Ad hoc Consolidations

IBM PA Workspace Installation & Benefits for Windows 2016

Octane Software Solutions Pty Ltd is an IBM Registered Business Partner specialising in Corporate Performance Management and Business Intelligence. We provide our clients advice on best practices and help scale up applications to optimise their return on investment. Our key services include Consulting, Delivery, Support and Training. Octane has its head office in Sydney, Australia as well as offices in Canberra, Bangalore, Gurgaon, Mumbai, and Hyderabad.

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Comparison of Linux vs Windows for IBM Planning Analytics (TM1)

14 November 2019, 12:28:17 pm AEDT

10 min

Linux vs windows

If you are thinking of moving to Planning Analytics, then this document can help you in selecting the best Operating System (OS) for your PA installation. Planning Analytics currently supports operating systems listed below:

Windows
Linux

At Octane, we have the expertise of working on both Windows and Linux environments and number of clients asked which one is the best-fit for their organisation?

Although its looks like a simple question but hard to answer, we would like to highlight some aids of both to help you to select the best-fit for your organization.

WINDOWS

Versions Supported for Planning Analytics:

Windows Server 2008

Windows Server 2012
Windows Server 2016

Advantages

Graphical User Interface (GUI)

Windows makes everything easier. Use a pre-install browser to download the software and drivers. Use the install wizard and file explorer to install the software. The Cognos Configuration tool defaults to a graphical interface that's easy to configure.

Single Sign On (SSO)

If your organisation uses Active Directory for user authentication, since it’s a Microsoft product so it’s easy to connect and setup Single Sign On (SSO) within Windows OS.

Easy to Support

It’s easy to do the Admin and maintenance related tasks due to its graphical interface.

Hard to avoid GUI interface completely

Even if you think to get rid of Windows, as Planning Analytics is GUI based product so it’s difficult to avoid windows environment completely. It’s easy to install and configure Planning Analytics in windows environment.

IBM Support

Almost all IBM support VMs are running on Windows, so it’s easy for IBM support team to replicate the issue. when they are trying to replicate an issue you might have discovered, it's quick and easy to test.

LINUX

Versions Supported for Planning Analytics:

Red Hat Enterprise Linux (RHEL) 8
Red Hat Enterprise Linux (RHEL) Server 6.8, 7.x

Advantages

Cost effective Linux servers are cost effective i.e. available at low price than Windows. If your organisation is running a large distributed environment than it can add up some costs.

Security While all servers have susceptibilities that can be locked down, Linux is typically less susceptible and more secure than Windows.

Scripting If you like to automate processes such as startup/shutdowns and server maintenance, the Linux command line and scripting tools make this easy.

There are number of Linux OS versions available in the market and due to this it would be difficult to find the information for a specific version.

CONCLUSION

When it comes to selecting an operating system, there is no right or wrong choice. It’s totally depends on the usage and how much comfortable you are with the selected OS. At Octane we do prefer to suggest Windows OS because of its simple UI for install and config as well as the support base. However, if you run a Linux-based shop and have server administrators who are comfortable and prefer Linux, then go with Linux we are here to help you.

https://www.fonts.com/font/microsoft-corporation/webdings

Planning Analytics Secure Gateway: Token Expiry

11 November 2019, 2:55:24 pm AEDT

3 min

Before you read further, please note, this blog details secure Gateway connection used for Planning Analytics deployed “on-cloud” Software as a Service (SaaS) offering.

This blog details steps on how to renew secure gateway Token, either before or after the Token has expired.

What is IBM Secure Gateway:

IBM Secure Gateway for IBM Cloud service provides a quick, easy and secure solution for establishing link between Planning Analytics on cloud and a data source; Typically, an RDBMS source for example IBM DB2, Oracle database, SQL server, Teradata etc. Data source/s can reside either “on-premise” or “on-cloud”.

Secure and Persistent Connection:

By deploying this light-weight and natively installed Secure Gateway Client, a secure, persistent connection can be established between your environment and cloud. This allows your Planning Analytics modules to interact seamlessly and securely with on-premises data sources.

Picture1-22

How to Create IBM Secure Gateway:

Click on Create-Secure-Gateway and follow steps to create connection.

Secure Gateway Token Expiry:

If the Token has expired, Planning Analytics Models on cloud cannot connect to source systems.

How to Renew Token:

Follow below steps to renew secure gateway token.

Navigate to the Secure Gateway
Click on the Secure Gateway connection for which the token has expired.
Go to Details as shown below and enter number 365 (max limit) beside Expiration days. Here 365 or a year is the maximum time after which the token will expire again. Once done click update.

Picture2-7

This should reactivate your token, TIs should now interact with source system.

PA Local PA Cloud TM1 tech tips

Adding customizations to Planning Analytics Workspace

8 November 2019, 11:40:16 am AEDT

4 min

IBM, in Planning Analytics Workspace’s 2.0.45 release however has addressed some limitations by extending the flexibility to users to upload the fonts and color themes of their choice in Workspace and apply it in their visualisations.

One of the common complaints that I constantly hear from users and have myself put up with when using Planning Analytics Workspace is its lack of available fonts and color pallets for its visualisations.

The lack of this flexibility put a hard restriction on designing intuitive interfaces and dashboards as we’re limited by only a certain fonts or color combinations provided by the platform. This becomes even more challenging when we had to follow the corporate color scheme and font type but this is no more!

Users can now add new themes by exporting the json file from Administration page in PAW and uploading the file with updated code for new color themes back.

http://colorbrewer2.org/# website offers some sample color pallets to quickly get started with ready to use customised color codes that you can paste in the json file.

Similarly, you may choose any free color picker extension of your choice available in Chrome Web Store to get the hexa code from anywhere within a webpage.

As for fonts, you can either download free Fonts from Google directly (https://www.fonts.com/web-fonts/google ) or you can go to https://www.fonts.com/web-fonts to purchase a desired font from its wide range of fancy fonts.

Tip: My all time favourite is Webdings font as that allows me to use the fonts as images so it enhances the performance of my dashboard by substituting the images with fonts displayed as icons/image thereby considerably reducing the dashboard refresh time and rendering of the data.

See the full list of graphics that it this font can display from the below link. http://www.911fonts.com/font/download_WebdingsRegular_10963.htm

Because this is a paid font, it would be highly desired if IBM can incorporate it in its existing list of fonts in PAW, until then it can be downloaded from Microsoft from below link.

Refer the below IBM link to get more info on how to add the fonts and color palettes to PAW.

https://www.ibm.com/support/knowledgecenter/SSD29G_2.0.0/com.ibm.swg.ba.cognos.tm1_prism_gs.2.0.0.doc/c_paw_corp_identity_overview.html

To identify where the color pallete code is within the json file, search for keyword “ColorPalette” in Notepad++ and it should list the results which consists of the root branch called ColorPalette and ids that has a unique number suffixed against ColorPalette (see screenshot below)

Note: It is not easy to correlate the color palette you see in PAW with its corresponding json code (id) and the only way you can do so is by manually converting the hex code of a color of all ids into a color and then visually inspecting it in PAW, so it’s a bit of a manual process and it becomes even more tedious process when you start to add more color palettes.

And given this complexity, below are some of the key points that you’ve to be vary of when working with palettes in PAW.

The color palettes displayed in PAW under Visualisation details corresponds to the placement order of the code within “ColorPalette” section of the json theme file. So if you have ColorPalette3 code placed above ColorPalette2 code, the second palette you see in PAW correlates to ColorPalette3 code.
The heat mapping color, however, corresponds to the numeric value of the id within the json file and not the placement order which is quiet weird. So if we take the same scenario from above, the 2rd^nd palette in PAW(which correlates to ColorPalette3) will still apply the heat mapping color of the ColorPalette2. Therefore, it is important to keep the numeric order consistent to easily correlate the code with the palette in PAW.

Incase same id is being repeated twice with different color codes, the first one that appears in the json file takes precedence and second one is ignored.

TM1 object extensions

8 November 2019, 11:32:33 am AEDT

13 min

This article talks about different extensions/files seen in Data directory and how they are co-related to front-end objects that we see in architect. Since, TM1 is an in-memory tool so all objects seen like cube, dimension, TI process etc seen in Architect are saved in data directory with a specific extension and in a specific way to differentiate them from the other objects.

By understanding these extensions/files, it comes easier in finding objects and deciding which objects are needed to be considered to backup or moving a specific set of changes. Consider the case of taking an entire data folder backup which might take up a large amount of space; instead of only a few objects had under gone changes. It would be more efficient to take backup of these changes then the complete data directory.

Also, by understanding these extensions and knowing what information it holds, developers can efficiently decide the objects that needed to be moved and their impact on the system when these objects are moved. To have a better understanding, lets divide the objects seen in TM1 into 3 sections i.e., dimension, cube and TI process & also what files are created in data directory.

Dimension objects

In this section, we will see what files are created in the data directory and what are they related in front-end interface of architect. To have a better understanding of how the dimension objects seen in architect are stored in data directory, we will take example of a dimensions called "Month" with 2 subsets "All Members" and "Months Only"

*.dim File

The dimensions that are seen in Architect are saved in data folder with <DimensionName>.dim extension. The file holds the hierarchy and elements details of that dimension. If the dimension needs to be backup or migrated then we can just take this file. In this example, "Month" dimension seen in architect is saved in "Month.dim" file in the data directory and by reading this file, the architect shows the "Month" dimension and its elements with hierarchy.

In Architect	In Data Directory

*}Subs Folder

All the Public Subsets that are seen under the dimension in Architect are placed in <DimensionName>}Subs folder respective to that dimension in data folder. In this case, Subsets created for month dimension i.e., "All Members" and "Months Only" are placed in Month}subs Folder

In Architect	In Data Directory

*.Sub File

The Subsets are created to make it easier to access the set of elements of a dimension and all the subsets of a any dimension are placed in <DimensionName>}Subs folder with <SubsetName>.Sub extension. The Subsets of Month Dimension i.e., "All Members" and "Months Only" are saved in the "Month}subs" Folder as All Members.sub and Months Only.sub

In Architect	In Data Directory -> Month}subs

Cube Objects

In this section, we will go through the files that are created in data directory for Cube related objects & how are they co-related to the cube objects in the architect. For this case, let’s use the cube "Month_ID" Cube as an example along with its Views "View1" & "View2".

*.Cub File

The cube and data seen in architect of any cube are saved in <CubeName>.cub file in data directory. So, if only the data needs to copied/moved from different environments, we can do this just by replacing this file for that respective cube. Here, Cube "Month_ID" and its data seen in architect are saved in a file Month_ID.cub in data directory of that TM1 server

In Architect	In Data Directory

*}Vues Folder

All the public views seen under the respective cube in architect are saved in <CubeName>}Vues Folder of the data directory. In this case, the views "View1" & "View2" of "Month_ID" Cube are saved in Month_ID}Vues Folder of data directory

In Architect	In Data Directory

*.vue file

All the public views created under a cube are saved in <CubeName>}Vues Folder with a <ViewName>.vue extension. So, the views "View1" & "View2" are saved in Month_ID}Vues Folder as View1.vue and View2.vue

In Architect	In Data Directory->Month_ID}vues

*.RUX file

This is rule file, all rule statements written in Rule Editor for cube can be seen in <CubeName>.RUX file. Here, Rule statements written in rule editor for "Month_ID" cube are saved in Month_ID.rux file of TM1 data directory.

In Architect	In Data Directory

*.blb file

These files are referred as Blob files and they are used to hold the format data, for example if a format is applied inside Rule of a cube then that data is saved in <CubeName>.blb. Similar to this, if a format style is applied to a view then the format details are saved in <CubeName>.<ViewName>.blb file. In this Case, the format style data applied in rule editor for "Month_ID" cube is saved in Month_ID.blb and the Format style applied to the "View1" is saved in Month_ID.View1.blb file which can be found in TM1 Data Directory.

In Architect

In Data Directory

Format Style data Applied in Rules

Format Style applied in View1

*.feeders file

This file gets generated only when the Persistent Feeders is set "True" in TM1 Configuration file. Once the feeders have been computed in the system, they will be saved in <CubeName>.feeders and this file will be updated in the feeders. Here, Feeder statements present in Rule editor for "Month_ID" are calculated and are saved as Month_ID.feeders

In Architect	In Data Directory
Feeders statements in Rule for Month_ID Cube

TI and Chore Objects

Here, we are going to look at files that are created in data directory for TI processes and chores.

*.Pro file

All TI processes in the Architect are saved in data folder with <TIProcessName>.Pro extension. Now, assume that there is TI process "Month_Dimension_Update" seen in architect then this TI process is saved as Month_Dimension_Update.pro file in data directory.

In Architect	In Data Directory

*.Cho file

The chore which is used to schedule the TI process is saved in the data folder with <ChoreName>.cho extension. Say, we have to schedule the TI process "Month_Dimension_Update" so we create a chore, "Month_Dim_Update" and this will create a file Month_Dim_Update.cho

In Architect	In Data Directory

Application objects

Applications provide the functionality to create virtual folders and this helps in accessing and the orderly sorting of TM1 objects like dimensions, TI Process, Views, Excel reports and so on. When any TM1 Object is added in the Application folder/Virtual folder, it creates a shortcut for that object enabling us to access the object from the shortcut and we can also rename these shortcuts as required.

When these objects are added, in turn they create a file in }Applications Folder of datafiles. These files hold the object information like type, name, reference and so on. Let’s take example of test virtual folder below Application

You can find these objects in datafiles Folder > }Applications Folder > Test Folder

You can find table on how the objects are mapped from frontend architect to backend files in the data folder

Objects in TM1 Application	Object Type	Files create in Application Folder of Data Directory
Test	Virtual Folder	Test Folder
App_Cube_Example	Cube	App_Cube_Example.cube
App_Cube_View_Example	View	App_Cube_View_Example.view
App_Dim_Example	Dimension	App_Dim_Example.dimension
App_Dim_Subset_Example	Subset	App_Dim_Subset_Example.subset
App_TI_Process_Example	TI process	App_TI_Process_Example.process
App_Chore_Example	Chore	App_Chore_Example.chore

You can also add files, URLs and Excel files from the system to the TM1 Application Folder. When we add files like text file, excel file in TM1 Application folder, *.blob files are created in backend of }Applications Folder in data directory. Similarly, *.extr file is created for URL and this file is saved in the TM1 Application Folder.

Also, if we had selected “copy the file to the TM1 Server” then the copy of that file gets saved in the }External Folder of Data Directory . Similarly, When the report is created and upload from Perspective client of TM1 it creates *.blob File and places the file in the }External Folder.