0
18 March 2026 9:29:59 AM
10 min
Why most TM1 environments underperform — and the four-step blueprint to fix it. 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 ...
Why most TM1 environments underperform — and the four-step blueprint to fix it.
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.
Most organisations fall into one of four support models. The first step to improving your environment is recognising which one you are.
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.
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.
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.
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.
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.
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.
The following outcomes come from actual client engagements — three different environments, three different sizes and industries, but strikingly consistent results.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
0
27 March 2026 9:00:00 AM
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.
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.
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.
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.
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.
Modernisation does not need to be an eighteen-month programme. Here is what a practical timeline looks like:
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.
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.
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.
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.
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.
Contact us: media@octanesolutions.com.au
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.
0
25 February 2026 2:04:37 PM
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.
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.
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.
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.
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.
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
0
31 July 2024 8:30:00 AM
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.
Here are some essential tips to help you optimize your Planning Analytics (TM1) processes:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
%20(1)%201.png)
print
Leave a comment