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Exploring the Latest Enhancements of IBM Planning Analytics Components

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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, ...


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)

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Slide6download 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.

Slide7download PDF file to get the full details


Slide8download 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.


Slide9download 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.

Download PDF for full details

Get you own copy of full features and functionalities of each Version for PAX, PAW and IBM PA Local in PDF format

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Mastering Calculations in Planning Analytics: Adapt to Changing Months with Ease

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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.

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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.

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