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.

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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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

• 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

1. 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.

2. 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.

3. 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! 

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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.

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. 

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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.

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:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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:

1. 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.

2. Improved Collaboration: The collaborative features of WatsonsX Orchestrate facilitate better communication and knowledge sharing among teams, leading to more effective and impactful AI solutions.

3. 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.

4. 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.

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Gone days, where we had no control/alerts mechanism on the TM1 database, CPU/memory it consumes, react at the nick of the moment before TM1 server crashes.

I am sure all TM1 lovers, administrators and business users who had these experiences in the past would connect to what I am referring to. For all new Planning Analytics users, in earlier versions of TM1/ Planning Analytics, we had little overview on how much on RAM/ memory can a TM1 instance use/utilize or have an inbuilt alter mechanism. 

TM1 Database Alert Mechanism: 

Issue: 

Most of you know, TM1 Server loves memory/RAM, more memory available the better performance/processing you get. Due to the trade-off between the cost and memory availability, there has always been a cap on upper limit on RAM available to TM1 Server.   

What is new: 

We now have an inbuilt mechanism in Planning Analytics Workspace, wherein we can set certain configuration and look for alters at a different level.  

Administrators can now set, database threshold and alter configurations in a single tab on the Database settings page for the individual database in Planning Analytics Administration. 

Isn’t that the good news! To use this, Planning Analytics Workspace version must be 2.0.46 or higher. In the previous version of Planning Analytics Administration, it was not possible to apply unique settings for each database, thresholds and alerts were set on separate tabs of a configuration page, but settings were applied to all databases in the environment. 

Navigation: 

For database settings, go to the Administration page, click Database as shown below. 

Click on settings (highlighted), Database Setting, move to Thresholds and alerts. 

The administrator can enter values for Warning threshold and Critical threshold and enable alert as different resource usages, as shown below. 

The administrator can also set thread auto-refresh time interval. 

Wonderful, now you can implement these in your environment, any doubts – we are here to help you for sure! Contact us today to find out how we can help you leverage your data for true business intelligence. 

You may also like reading “ Predictive & Prescriptive-Analytics ” , “ 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 with 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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IBM Planning Analytics (TM1) vs Anaplan

There has been a lot of chatter lately around IBM Planning Analytics (powered by TM1) vs Anaplan. Anaplan is a relatively new player in the market and has recently listed on NYSE. Reported Revenue in 2019 of USD 240.6M (interestingly also reported an operating loss of USD 128.3M). Compared to IBM which has a 2018 revenue of USD 79.5 Billion (there is no clear information on how much of this was from the Analytics area) with a net profit of 8.7 b). The size of global Enterprise Performance Management (EPM) is around 3.9 Billion and expected to grow to 6.0Billion by 2022. The size of spreadsheet based processes is a whopping 60 Billion (Source: IDC)

Anaplan has been borne out of the old Adaytum Planning application that was acquired by Cognos and Cognos was acquired by IBM in 2007. Anaplan also spent 176M on Sales and Marketing so most people in the industry would have heard of it or come across some form of its marketing. (Source: Anaplan.com)

I’ve decided to have a closer look at some of the crucial features and functionalities and assess how it really stacks up.

There are some issues around scaling up the Anaplan cubes where large datasets are under consideration (8 billion cell limit? While this sounds big, most of our clients reach this scale fairly quickly with medium complexity). With IBM Planning Analytics (TM1) there is no need to break up a cube into smaller cubes to meet data limits. Also, there is no demand to combine dimensions to a single dimension. Cubes are generally developed with business requirements in mind and not system limitations. Thereby offering superior degrees of freedom to business analyst.

For example, if enterprise wide reporting was the requirement, then the cubes may be need to be broken via a logical dimension like region of divisions. This in turn would make consolidated reporting laborious, making data slicing and dicing difficult, almost impossible.

Love it or hate it – Excel is the tool of choice for most analyst and finance professionals. I reckon it is unwise to offer a BI tool in today’s world without a proper excel integration.  I find Planning Analytics (TM1) users love the ability to use excel interface to slice and dice, drill up and down hierarchies and drill to data source. The ability to create interactive excel reports with ability to have cell by cell control of data and formatting is a sure-shot deal clincher.

On the other hand, on exploration realized Anaplan offers very limited Excel support.

In today’s world users have come to expect drag and drop analysis. Ability to drill down, build and analyze alternate view of the hierarchy etc “real-time”. However, if each of this query requires data to be moved around cubes and/or requires building separate cubes then it’s counterproductive. This would also increase the maintenance and data storage overheads. You also lose sight of single source of truth as your start developing multiple cubes with same data just stored in different form. This is the case with Anaplan due to the software’s intrinsic limitations.

Anaplan also requires users to invest on separate reporting layer as it lacks native reporting, dashboards and data visualizations.

This in turn results in,

1. Increase Cost
2. Increase Risk
3. Increase Complexity
4. Limited planning due to data limitations

IBM Planning Analytics, on the contrary offers out of the box ability to view & analyze all your product attributes and the ability to slice and dice via any of the attributes. 

It also comes with a rich reporting, dashboard and data visualization layer called Workspace. Planning Analytics Workspace delivers a self-service web authoring to all users. Through the Planning Analytics Workspace interface, authors have access to many visual options designed to help improve financial input templates and reports. Planning Analytics Workspace benefits include:

1. Free-form canvas dashboard design
2. Data entry and analysis efficiency and convenience features
3. Capability to combine cube views, web sheets, text, images, videos, and charts
4. Synchronised navigation for guiding consumers through an analytical story
5. Browser and mobile operation
6. Capability to export to PowerPoint or PDF

Source : Planning Analytics (TM1) cube

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Predictive analytics:

Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. A way to predict the future using data from the past.

Predictive analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modelling, data mining, text analytics, optimization, real-time scoring and machine learning. These are tools that help organizations discover patterns within the data and go beyond knowing what has happened to anticipate what is probable to happen next.

• Use historical information to determine patterns.
• Once equipped with these patterns, predictive models are built and are used to forecast possible trends and outcomes.
• Predictive analytics highlights approaching opportunities and potentials for risk to improve the quality of decision-making around upcoming events.

Prescriptive Analytics:

Prescriptive analytics is the use of technology to help businesses make better decisions in handling specific events by factoring in the knowledge of possible constraints, available resources, past performance and current situation.

Prescriptive analytics involves mathematical and computational algorithms and goes beyond predicting future outcomes by also suggesting actions and to benefit from the predictions and showing the implications of each decision option.

Prescriptive analytics seeks to determine the optimized solution or best outcome among different choices depending on current constraints, resources and priorities. Prescriptive analytics uses both descriptive and predictive data to determine a specific action to take.

Prescriptive Analytics capabilities :

Prescriptive Analytics hold below features

• Prescriptive Modelling
• Uses mathematical and computations models
• Optimized solutions
• Continually take in new data to re-predict and re-prescribe
• Automatically improve prediction accuracy and prescribing better decision options
• Visualization
• Prescriptive analytics incorporates both structured and unstructured data

IBM Decision Optimisation is one such solution from IBM.

IBM Decision Optimisation:

IBM Decision Optimisation is a prescriptive analytics solution that enables organisations in commerce, manufacturing, financial services, healthcare, telco, government and other highly data-intensive industries to make better decisions and achieve business goals by solving complex optimisation problems.

IBM Decision Optimisation solves business problems using Mathematical and Constraint programming.

IBM Decision Optimisation solutions provide features that help create optimization models, either using general programming language APIs, like Python, Java or OPL to solve the breadth of optimization models, using proven and powerful optimization engines.

IBM Decision Optimisation is an integral part of IBM Watson® Studio, so users can benefit from all data science features of IBM Watson Studio, like access to machine-learning models, the ability to pass output from predictive analytics to the Decision Optimisation engine, access to open notebook features, visualization features and data connectivity options from IBM Watson Studio.

Key benefits

• Powerful optimization engines for faster performance
• Quickly build optimization models using your preferred language
• Access to more data science features

Register for our webinar 'Business Decisions and Resource Allocation' to learn how IBM Planning Analytics with Watson and IBMLOG CPLEX work seamlessly together. 

IBM Case Study:

Client:

Leading bulk tanker transportation company.

Business challenge:

To transport bulk products safely and profitably, this carrier needs to manage hundreds of constraints on tankers, drivers and cargos. How can it help its planners make optimal routing decisions?

Transformation:

This leading bulk carrier embedded IBM optimization software into its operational systems and developed a sophisticated solution that provides insight to optimize driver and route planning every 10 minutes

Results:

Millions of dollars saved annually by eliminating miles of unnecessary driving.

Millions more dollars saved annually by improving driver retention.

Million-dollar revenue boost achieved by increasing driver productivity.

Click here for more details.

Products

• IBM ILOG® CPLEX® Optimization Studio
• IBM Decision Optimization for Watson Studio
• IBM Decision Optimization Center
• IBM Decision Optimization on Cloud
• IBM ILOG CPLEX Optimizer for z/OS

Organizational Benefits:

• Optimized solutions to solve business problems.
• Greater ease-of-use.
• Comprehensive analytics capabilities
• Movement to the cloud
• Increased adoption beyond financial services
• Overall market growth.
• Open source integration
• A flexible and scalable platform for one-to-many analytics.

Hope you have enjoyed reading this blog as much as I had testing this cool feature; stay tuned for upcoming blogs.

 You may also like reading “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”.

For more information: To check on your existing Planning Analytics (TM1) entitlements and understand how to upgrade to Planning Analytics Workspace (PAW) reach out to us at info@octanesolutions.com.au for further assistance.

Octane Software Solutions Pty Ltd is an IBM Registered Business Partner specialising in Corporate Performance Management and Business Intelligence. We provide our clients with 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.

To know more about us visit, OctaneSoftwareSolutions.

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This article talks about Business Intelligence and Business Analytics, things in common and about differences between one another. This blog also talks about the paybacks for an organization once these are adopted.

Let’s  start ... Though Business Intelligence and Business Analytics sound similar and are being used interchangeably by many, they do have differences.

Business Intelligence ( BI ) :-

Term Business Intelligence, though exist for long, have been used by wider audience from late 90s .

Intelligence with in Business comes from the data being captured. Business Intelligence has been considered as a process to collect, store, maintain, retrieve and interpret data and purpose is to optimize, streamline and smoothen current operations within the Organization. BI helps in making better-informed decisions, improve performance, helps in creating new strategic opportunities for growth, eventually helps to better understand how the business is doing, make better-informed decisions.

In bullet points, BI is:

• A process deals with collecting data, querying, reporting, online analytical processing and alerting.
• The purpose of business intelligence is to support data-driven business decision making.
• BI solutions collect and analyse current and historical, actionable data with the purpose of providing insights into improving business operations.
• Improves and maintains operational efficiency and helps companies increase organizational productivity.
• BI is more concerned with the WHATs and the HOWs(Performance).
• BI Technologies are efficient enough to give insight on what happened in the past / is happening right now in business –If input data to BI systems is real and granular, Organisations would have a much better insight.
• BI refers to set of technologies(DSS) supporting decision-making process by executives, middle management.  
• A dashboard gives all required insight needed, also displays data trend – thus helps management in taking right decision at right time to run the businesses effectively.
• BI tools have evolved to become much more intuitive and user-friendly.

Business Intelligence gives information about the data itself and also provides insights rather than making extra transformations or conversions.

Business Analytics :-

Word, Business Analytics was also in existence for long but became a buzz word in last 10-15 years. Business Analytics is process of exploring data and interpreting data.

• Uses Statistical analysis and predictive modelling
• Business Analytics involves multiple technologies to transforming raw form of data into a meaningful way to convey the solution in best way possible.
• Analyses past data to drive current business and predict future business. Supports management in decision making to change existing business operations and improve productivity.
• Helps management in improving future business operations using current and history data, thus boosting future performance.
• Uses past data to extract insight, drive customer needs and increasing productivity.
• Establishes trends and helps analyse WHY things are happening and provide optimized solutions to solve problem.
• Applies to companies where future growth and productivity is one of their goal.
• Helps in answering WHAT and WHY and also HOW to achieve.

Business Intelligence and Business Analytics:-

Mutual:

• Collects raw data, current and historical data. Transforms raw data into meaningful data.
• Analyses data, helps in identifying pain points, provides alternatives, suggests optimized solutions.
• Data mining helps in finding insights from existing data .
• Rich visualization provides Dashboards.
• Dashboards can be a single point to know how Organization is performing, Areas to focus, historical data visualization and many more.
• Multiple technologies involved.

Focus:

• Business Intelligence focuses on past and current data, Business Analytics also uses past and current data but helps in predicting future trend using existing data.

• Business Intelligence Visualization helps with past and present trend to some extent predict future for the Business Model.
• Business Intelligence uses traditional approach, Business Analytics uses Statistical methods and models.
• Business Analytics though have reporting capability but primarily makes predictions using collected data and offers optimized solutions.

• Business Intelligence focuses on Descriptive and Diagnostic Analytics, Business Analytics is more about Predictive Analytics and Prescriptive Analytics.

Both Business Intelligence and Business Analytics share activities with in above figure.

Organizational Benefits:

Business Intelligence and Business Analytics helps organization in meeting their  Strategic Goals , near-term , long-term goals. These systems provide insights from raw data feed initially to a digestible and understandable information to the Executive management.

Business Intelligence helps giving insight with in Business Models, Analytics focuses on Business process and gives optimized solutions and thus helps decision making.

These systems provide details on business performance, help them answer many questions, some listed below.

• How business is performing.
• Current state
• When compared to last year, last quarter, last five years etc .
• Where is it performing well?
• Where is it not performing well?
• What happened?
• Why is it not performing well ?
• Why not ?
• What happened?
• What now?
• Are we missing Goals, what/How and who, what change needed etc
• What needs to be done ?
• What should change ?

Business Users like, Executive Team, Middle management, Business Analyst can always do ad hoc analysis, ad hoc reporting and predict the impact of change to expected outcomes. Thus, helps management in taking decisions to meet Organizational objects, goals and targets.

Hope you would have enjoyed reading this blog as much as I had testing this cool feature; stay tuned for upcoming blogs.

You may also like reading “ 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”. 

For more Information: To check on your existing Planning Analytics (TM1) entitlements and understand how to upgrade to Planning Analytics Workspace (PAW) reach out to us at info@octanesolutions.com.au for further assistance.

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.

To know more about us visit, OctaneSoftwareSolutions.

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Forward-thinking enterprises are using business intelligence and analytics to navigate through their digital transformation – one that could see them expand both physically and fiscally at a rapid pace.

The ability to harness this data and use it to make business decisions, however, poses challenges. This is particularly true for organisations that haven’t previously had the technology nor the manpower to sift through all the historical data they’ve accumulated in their daily activities.

Think about it – by 2020 it’s predicted there will be 5,200GB of data for every individual on the planet. More importantly, 90% of our current data is ‘unstructured’, drawn from mediums like social media and Internet of Things (IoT) devices.

Harnessing the power of business intelligence and analytics

So how can enterprises take advantage of data analytics? It starts with the three ‘I’s:

• Investment: Collecting and analysing your company data for future activities. You might be surprised what sort of data can be used for predictive modelling purposes to forecast future trends and outcomes.
• Innovation: Harnessing unexplored or raw data to see if your business can create new products or services.
• Improvisation: Parsing business data to find new meaning in it. This can lead to actionable insights that feed into the analytics cycle.

Using data to generate value

More and more businesses are recognising the usefulness of their data, but there’s still a way to go. While more than 80% see AI as a strategic opportunity, most aren’t using data to its full advantage.

These organisations may be using AI to reduce their operational costs or modernise their systems for better business intelligence and data warehousing, but to derive the very best value from your data, you need to be an insights-driven and transformative company.

What is the analytics cycle?

This all feeds into the analytics cycle, of which there is no one-size-fits-all design. IBM, for example, defines the cycle as taking a Planning, Descriptive, Diagnostic, Predictive and Prescriptive approach.

But the bottom line is that every business’s analytics cycle should feed into one central goal: gaining a competitive advantage.

Your cycle might start with identifying a business problem, preparing and analysing the data, A/B testing different solutions and then monitoring the results.

And that model will likely change according to the data you are analysing or the business problem you need to solve. So long as the target is building a more sustainable and competitive business, you’ll be able to use your business intelligence and analytics to generate greater value for your company.

Are you looking to harness the power of data and rise above the competition? Contact Octane Software Solutions today to find out how we can help you derive real business value from your data.

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