IBM Planning Analytics

How to Handle Outliers in IBM Planning Analytics Predictive Modeling

March 24, 2026 by Lee Lazarow

Have you used Planning Analytics to generate a forecast and realized that your data might not be as clean as you wanted? Maybe the data is legitimately skewed, such as the massive swings that occurred during COVID. Or maybe the data has not yet been fully scrubbed, such as data inherited from a recent merger or a new implementation.

These situations may cause your use of predictive modeling to give you awkward, or even invalid, results due to data that was not expected. But did you know that Planning Analytics’ predictive modeling allows you to adjust for these outliers?

When generating a prediction, outliers will appear as red squares within your resulting charts. You have the option to adjust for outliers by simply flipping a trigger.

Here is an example of a data set with an outlier.

This outlier caused the trends in this historical data to become more irrational in the forecast. We can ignore the outlier by simply clicking on the option at the top, right corner of the chart.

Here is the same data set with an adjustment to the outlier. Notice how the future values have smoother predicted values with a more rounded confidence interval.

By ignoring outliers, you can have more control of your data, which in turn gives you more control of your predictions within your Planning Analytics environment.

Revelwood is an IBM Gold Business Partner with more than 30 years of experience designing, developing, implementing and maintaining IBM Planning Analytics environments. We focus on solutions for the Office of Finance and have partnered with clients of all sizes across all industries to optimize, enhance and expand their use of Planning Analytics. Revelwood’s Planning Analytics team consists of PA experts with decades of experience, and we have been recognized via awards including IBM Champion status.

Stay up to date with PA – sign up for our weekly Planning Analytics Tips & Tricks newsletter, subscribe to our YouTube channel, and join our IBM Planning Analytics All-Stars group on LinkedIn.

Revelwood’s Lee Lazarow and Ivan Cepero Named IBM Champions for 2026

March 20, 2026 by Revelwood

Two members of the Revelwood team have been named IBM Champions for 2026: Lee Lazarow, our IBM Planning Analytics Practice Leader, and Ivan Cepero, our Senior Planning Analytics Consultant.

The IBM Champions program recognizes non-IBM IT professionals, business leaders, developers, and educators who go beyond the scope of their day-to-day work to make a meaningful impact on the IBM community. Champions are selected based on their commitment to sharing knowledge, mentoring others, advocating for IBM technologies, and helping grow the broader user community through activities like speaking at conferences, authoring articles, moderating forums, and leading user groups.

Earning the designation requires more than technical expertise — nominees must demonstrate consistent, ongoing contributions that help others get more value from IBM software and solutions. It’s a recognition that reflects both deep product knowledge and a genuine commitment to the community.

For Lee, this marks his fifth year earning the IBM Champion distinction — a milestone that reflects years of sustained contribution to the IBM Planning Analytics ecosystem. His ongoing work as a thought leader, educator, and advocate for the platform has earned him recognition year after year as one of the most influential voices in the community.

Ivan joins the 2026 class as a recognized contributor in his own right, bringing his expertise as a Senior Planning Analytics Consultant to the IBM community in ways that extend well beyond client work. His inclusion in this year’s cohort speaks to the breadth and quality of his contributions to the platform and its users.

Together, Lee and Ivan represent what makes Revelwood’s IBM Planning Analytics practice distinctive: a team that doesn’t just implement the platform but invests in the community around it. Their recognition as IBM Champions is a reflection of that commitment.

Congratulations to Lee, Ivan, and all members of the IBM Champions class of 2026.

Interested in learning more about what our IBM Planning Analytics team can do for your organization? Explore our IBM Planning Analytics services.

Celebrating Pi Day with Excel’s =PI() Function

March 17, 2026 by Lee Lazarow

As you know from reading my previous blogs (The Hidden Structure of 1/7 and Exploring Consecutive Squares with 9/16/25), I am a big fan of combining math with dates. This week allowed us to celebrate another math date! Pi Day (March 14th) was just this past weekend, so it felt like the perfect time to highlight a formula hiding in plain sight.

Most of you know that Pi is rounded to 3.14 and that the value is used for various calculations associated with a circle. But did you know that Excel has a function associated with Pi?

The =PI() formula returns Pi accurate to 15 digits. It only goes 15 digits deep due to how Excel handles floating-point arithmetic (Excel converts anything larger than 15 digits into scientific notation).

There’s not much more to say about this formula other than using it to read this …

We all know that MATH IS AWESOME, but Math times Pi makes it three times more awesome!

Stay up to date with PA – sign up for our weekly Planning Analytics Tips & Tricks newsletter, subscribe to our YouTube channel, and join our IBM Planning Analytics All-Stars group on LinkedIn.

Use the Planning Analytics AI Assistant for Smarter Data Analysis

March 10, 2026 by Lee Lazarow

Have you used Planning Analytics’ new AI functionality to analyze your existing models? Maybe you have a chart that you want to get insights on, or maybe you have a set of data where you want to define outliers. All this functionality can be performed by simply clicking on the AI button within PAW:

For more details on these items, please watch our most recent webinar about Planning Analytics’ AI functionality.

But did you know that you can also use the Planning Analytics assistant to “speak” to your model? You can access the assistance by clicking on the icon in the top ribbon:

Once clicked, a window will appear that will allow you to interact with the model using natural language functionality. You can use statements such as:

Tell me about this chart
Show me outliers in this dataset

This approach gives you a great deal of flexibility when using PAW. You can use your mouse, your standard keyboard, or even voice commands to reference various functions in your Planning Analytics environment.

Stay up to date with PA – sign up for our weekly Planning Analytics Tips & Tricks newsletter, subscribe to our YouTube channel, and join our IBM Planning Analytics All-Stars group on LinkedIn.

IBM Named a Leader in the 2026 IDC MarketScape for Enterprise Planning, Budgeting & Forecasting

February 20, 2026 by Revelwood

Independent analyst firm IDC has placed IBM in the Leaders category of its 2026 MarketScape for Worldwide Enterprise Planning, Budgeting, and Forecasting Applications — and it’s worth understanding what that means for finance teams evaluating their options.

What Is the IDC MarketScape?

The IDC MarketScape is one of the most respected vendor assessments in enterprise technology. To be included, vendors must exceed $85 million in enterprise planning software revenue — which filters the analysis to platforms with real market scale. Vendors are scored on two axes: current capabilities and future strategy. Leaders must demonstrate strength on both. IBM Planning Analytics did.

The Market Context: Why This Moment Matters

Finance teams are under pressure to do more than produce an annual budget. Rising business volatility, expanding data requirements, and the rapid introduction of generative AI are reshaping expectations. The new standard is continuous planning — higher frequency, greater accuracy, deeper cross-functional collaboration. IDC’s 2026 assessment was conducted against that backdrop, and the platforms in the Leaders category are the ones built to meet it.

“Enterprise planning, forecasting, and budgeting applications are becoming the backbone of financial resilience. The ability to pair GenAI and agentic workflows with trusted, governed data will help drive accuracy, consistency, and continuous insights.” — Megha Kumar, Research VP, Analytics & AI, IDC

What IDC Said About IBM Planning Analytics

The IDC report describes IBM’s Planning Analytics platform as delivering an AI-powered, agentic, and integrated planning solution that automates budgeting, forecasting, and financial planning. According to IDC, the platform enables real-time analysis and collaboration across enterprise functions, combining strong back-end performance with modern user interfaces, and supports both hybrid deployments and integrated planning scenarios.

IDC specifically called out three strengths:

STRENGTH 01

The platform offers a choice between the web-based Planning Analytics Workspace and an Excel add-in environment — addressing the reality that different users in the same organization have different workflow preferences.

STRENGTH 02

IBM has GenAI features in production today — including chart insights, forecast insights, outlier analysis, and natural language summarization — integrated directly into the planning workflow so users don’t need data science expertise to benefit from them.

STRENGTH 03

The platform includes full system-wide auditability, tracking user actions and allowing rollback to any historical point — a capability that directly supports compliance requirements and builds trust in AI-generated insights.

AI STRATEGY

IBM’s AI strategy combines out-of-the-box AI experiences with composable tools for custom agentic workflows — built on watsonx foundations with a natural language Planning Analytics Assistant for insight extraction.

IDC’s “Consider IBM When” Guidance

DIRECTLY FROM THE IDC REPORT

IDC advises organizations to consider IBM Planning Analytics when they are seeking an AI-enabled planning platform with:

Flexible deployment options (cloud, hybrid, or on-premises)
Integrated planning capabilities across business functions
Support for data residency requirements across regions

This is meaningful guidance. It tells you what kind of organization IBM Planning Analytics is best suited for — one that needs enterprise-grade flexibility, not a one-size-fits-all SaaS setup, and one that takes data governance and compliance seriously.

What This Means If You’re Evaluating Planning Platforms

IDC’s 2026 assessment also included practical guidance for technology buyers — eight specific considerations for organizations going through a platform evaluation this year. The themes are worth understanding even if you’re not actively in a buying process:

IDC’S ADVICE FOR TECHNOLOGY BUYERS

Evaluate what processes you want to automate and what you can standardize — finance teams are often short-staffed and standardization drives adoption
Assess vendors not just on AI accuracy but on AI governance and management capabilities within the platform
Prioritize low-code/no-code capabilities and ease of building financial models
Ensure vendors offer transparency around how data is managed by AI and the ability to “bring your own model”
Build an AI-ready data strategy internally before deploying AI-generated insights into decision-making workflows

The Revelwood Perspective

We’ve been implementing IBM Planning Analytics for finance teams for years. We’ve seen what it does in practice — not just what it promises in a demo. The IDC recognition validates what our clients tell us regularly: IBM Planning Analytics is a serious platform built for serious planning challenges.

The GenAI and agentic capabilities IBM is building are not vaporware. They are in production. The Excel-native and web interface choice matters enormously to real finance users who were trained on spreadsheets. And the auditability features — the ability to roll back to any historical state — are exactly the kind of trust-building infrastructure that gets finance leaders comfortable moving beyond the spreadsheet.

If you’re in the early stages of an FP&A platform evaluation, or you’re already on IBM Planning Analytics and wondering how it compares to the competition, this IDC excerpt is worth reading in full. It gives you independent language to use internally when building your business case — and it shows you exactly how IBM stacks up against Oracle, Anaplan, SAP, Workday, OneStream, and others in the Leaders category.

Read the IDC MarketScape Report

Get your complimentary copy of the 2026 IDC MarketScape excerpt featuring IBM as a Leader in enterprise planning, budgeting, and forecasting.

Download the report here

The Fastest Way to Copy a Worksheet in Excel

February 17, 2026 by Revelwood

Many Excel users copy worksheets by right-clicking on the tab, choosing Move or Copy, and then checking Create a copy. While that works, there’s a much faster way to duplicate a worksheet in seconds.

Here’s the shortcut:

1. Hold down the Control (Ctrl) key on your keyboard..
2. Click and drag the worksheet tab to the right (or to the spot you want the new copy).
3. Watch for the icon to change to a page with a plus sign.
4. Release your mouse, then release Ctrl.

That’s it — you’ve instantly duplicated your worksheet.

Why This Matters

If you work in IBM Planning Analytics and regularly export data to Excel, build scenario comparisons, create versioned reports, or prepare client-ready outputs, this shortcut can save meaningful time. Instead of navigating through menus, you can quickly:

Create a new version of a report before making edits
Duplicate a template tab for a new department or cost center
Copy a formatted sheet while preserving formulas, formatting, and structure
Build side-by-side scenario comparisons (e.g., Budget vs. Forecast)

Small efficiency gains like this add up — especially for Finance teams working under tight reporting deadlines.

We’re always looking for ways to make your Finance team more efficient and productive. Want more time-saving tricks? Check out our full library of Planning Analytics Tips & Tricks.

Stay up to date with PA – sign up for our weekly Planning Analytics Tips & Tricks newsletter, subscribe to our YouTube channel, and join our IBM Planning Analytics All-Stars group on LinkedIn.

How to Remove Duplicate Elements from an Existing Subset in Planning Analytics Workspace

February 3, 2026 by Lee Lazarow

Managing large datasets in IBM Planning Analytics Workspace (PAW) often means dealing with complex lists of elements—from thousands of SKUs to extensive customer databases. In this edition of our IBM Planning Analytics Tips & Tricks series, Lee Lazarow, IBM Planning Analytics Practice Leader at Revelwood, demonstrates a simple, one-click solution to clean up your subsets by removing duplicate elements.

Watch the Video

See this tip in action! Watch the full video below to follow along step-by-step as Lee Lazarow demonstrates how to quickly identify and eliminate duplicates in Planning Analytics Workspace.

Whether you are a developer staring at a long list of elements or a modeler managing static subsets created by users, this tip will help you maintain a clean and accurate Planning Analytics environment.

Why Duplicates Happen in PAW

Duplicates often creep into explorations and subsets, especially when dealing with:

Large Dimensions: When viewing thousands of elements like SKUs or customers, it’s easy to miss a repeating entry.
Static Subsets: Unlike dynamic subsets, static lists created manually by users may accidentally include the same member multiple times.
Manual Modeling: Even experienced developers can overlook duplicates when manually building complex views.

How to Remove Duplicate Elements

Lee Lazarow shows the following process in the video:

1. Identify the Duplicate: In your exploration, notice if an account or element (such as “Rent”) appears more than once in your rows or columns.
2. Open the Set Editor: Click on the dimension in your rows (or columns) and select the Set Editor.
3. Find the “Remove Duplicated Members” Button: Within the Set Editor, look for the specific icon designed to clean up your list.
4. Execute with One Click: Simply click the “Remove duplicated members” button. PAW will instantly scan the list and remove any redundant entries.
5. Apply Changes: Click “Apply” to update your view. Your exploration is now modified to show only unique elements.

Why This Feature Matters

Using the built-in duplicate removal tool helps:

Save Time: You don’t have to manually scan thousands of rows to find errors.
Improve Accuracy: Ensure your reports and explorations aren’t double-counting or cluttering your data view.
Simplify Maintenance: Clean up user-created static subsets with a single click.

By leveraging this built-in PAW feature, you can let the system do the heavy lifting, allowing you to focus on analyzing your data rather than cleaning it.

Stay up to date with PA – sign up for our weekly Planning Analytics Tips & Tricks newsletter, subscribe to our YouTube channel, and join our IBM Planning Analytics All-Stars group on LinkedIn.

How to Create Picklists in Planning Analytics Workspace

January 20, 2026 by Lee Lazarow

Creating picklists in IBM Planning Analytics Workspace (PAW) is a simple yet powerful way to control user input and improve data consistency. In this edition of our IBM Planning Analytics Tips & Tricks series, Lee Lazarow, IBM Planning Analytics Practice Leader at Revelwood, walks you through how to create and customize picklists so your users have a clear, guided experience.

Watch the Video

See this tip in action! Watch the full video below to follow along step by step as Lee Lazarow demonstrates how to create and customize picklists in Planning Analytics Workspace.

Whether you prefer to watch the video, read the walkthrough below, or do both, this tip will help you better manage your Planning Analytics environment.

What Are Picklists in Planning Analytics?

Picklists are drop-down menus in Planning Analytics Workspace that allow users to select from a predefined set of options. They help maintain data consistency and make input easier for end users.

PAW allows you to customize these picklists to include or exclude blank or default values, giving you more control over user input.

How to Create and Customize Picklists

Lee Lazarow shows the following process in the video:

1. Access Format Manager: In PAW, open the editor for the cell or data area where you want the picklist.
2. Data Area Settings: Go into the data area settings, where you’ll find the picklist options.
3. Enable or Disable Default Values: The default setting includes a blank or “none” value. You can toggle this on or off depending on whether you want users to have the option to clear the selection.
4. Customize the Default Value: You can also define a custom value instead of “none.” For example, you could use a label like “*** LEE ***” or any other text that makes sense for your workflow.
5. Apply Changes: Click “apply” and “done,” and your picklist will update accordingly.

This approach ensures your users see exactly what you want in the drop-down menu, improving data quality and usability.

Why Picklists Matter

Customizing picklists helps:

Maintain consistent data entry
Reduce errors caused by free-text input
Guide users toward valid choices
Make workflows more intuitive

By leveraging PAW’s Format Manager, you can fine-tune picklists for your organization’s specific needs.

Stay up to date with PA – sign up for our weekly Planning Analytics Tips & Tricks newsletter, subscribe to our YouTube channel, and join our IBM Planning Analytics All-Stars group on LinkedIn.

Performance Monitoring in Planning Analytics Workspace

January 6, 2026 by Lee Lazarow

Keeping your IBM Planning Analytics environment running efficiently is critical as models grow, data volumes increase, and user activity expands. In this edition of our IBM Planning Analytics Tips & Tricks series, Lee Lazarow, IBM Planning Analytics Practice Leader at Revelwood, walks through how to use Performance Monitoring in Planning Analytics Workspace to better understand system usage and identify optimization opportunities.

Watch the Video

Prefer to see this tip in action? Watch the full video below to follow along step by step as Lee Lazarow demonstrates Performance Monitoring in Planning Analytics Workspace.

Whether you prefer to watch the video, read the walkthrough below, or do both, this tip will help you take a more proactive approach to monitoring and improving performance.

What Is Performance Monitoring in IBM Planning Analytics?

IBM Planning Analytics includes a built-in feature called Performance Monitor. When enabled, it collects statistics about your environment, such as memory usage and processing activity, and stores them in system control objects. These statistics provide valuable insight into how your databases and cubes are performing over time.

Performance monitoring is especially useful for:

Conducting system health checks
Identifying large or inefficient cubes
Understanding memory consumption
Spotting opportunities to optimize rules and feeders

How to Enable Performance Monitor

As Lee explains in the video, turning on Performance Monitor is quick and straightforward:

1. In Planning Analytics Workspace, right-click on the server name.

2. Select Performance monitor from the menu.

3. Confirm the prompt asking if you’re sure you want to enable it.

Because Performance Monitor records system statistics, IBM includes this confirmation step to acknowledge that there may be a slight performance impact. Once confirmed, simply save your selection and the Performance Monitor will begin running.

Understanding Control Cubes and Performance Statistics

After Performance Monitor is enabled, IBM Planning Analytics automatically creates a series of control objects, including several control cubes that store performance statistics.

To access them:

Expand your database

Navigate to Control Objects > Control Cubes
Look for cubes that begin with }StatsBy

These cubes capture performance data across different dimensions, such as by chore, client, rule, and cube.

Using }StatsByCube for Performance Analysis

One of Lee’s go-to tools during system health checks is the }StatsByCube control cube. This cube provides a clear view into how much memory each cube is consuming and why.

Within the default view, Lee focuses on three key metrics:

Memory used for input data – how much space is taken up by loaded data
Memory used for feeders – memory consumed by calculations and rule feeding
Total memory used – the combined footprint of each cube

For easier analysis, Lee recommends formatting the view (for example, using commas) to clearly see large values at a glance.

Identifying Optimization Opportunities

To prioritize analysis, Lee sorts the cubes in descending order by size. This quickly highlights the largest cubes in the environment.

From there, you can start asking important questions:

Is a cube large because it contains a lot of data?
Or is it large because of inefficient rules or overfeeding?
Are calculations driving unnecessary memory usage?

By comparing input data memory versus feeder memory, you can determine where optimization efforts will have the greatest impact—whether that’s redesigning rules, improving feeder logic, or adjusting data structures.

Why Performance Monitoring Matters

Performance Monitor isn’t just about visibility—it’s about actionable insight. By understanding where and how resources are being used, organizations can:

Improve system efficiency
Reduce memory consumption
Enhance user experience
Plan more effectively for growth

As Lee notes, Performance Monitor provides an excellent starting point for system optimization and ongoing performance management.

Stay up to date with PA – sign up for our weekly Planning Analytics Tips & Tricks newsletter, subscribe to our YouTube channel, and join our IBM Planning Analytics All-Stars group on LinkedIn.

IBM Planning Analytics

Read more IBM Planning Analytics Tips & Tricks:

Read more IBM Planning Analytics Tips & Tricks:

Read more IBM Planning Analytics Tips & Tricks:

What Is the IDC MarketScape?

The Market Context: Why This Moment Matters

What IDC Said About IBM Planning Analytics

STRENGTH 01

STRENGTH 02

STRENGTH 03

AI STRATEGY

IDC’s “Consider IBM When” Guidance

What This Means If You’re Evaluating Planning Platforms

The Revelwood Perspective

Read the IDC MarketScape Report

Here’s the shortcut:

Why This Matters

Read more IBM Planning Analytics Tips & Tricks:

Watch the Video

Why Duplicates Happen in PAW

How to Remove Duplicate Elements

Why This Feature Matters

Read more IBM Planning Analytics Tips & Tricks:

Watch the Video

What Are Picklists in Planning Analytics?

How to Create and Customize Picklists

Why Picklists Matter

Read more IBM Planning Analytics Tips & Tricks:

Watch the Video

What Is Performance Monitoring in IBM Planning Analytics?

How to Enable Performance Monitor

Understanding Control Cubes and Performance Statistics

Using }StatsByCube for Performance Analysis

Identifying Optimization Opportunities

Why Performance Monitoring Matters

Read more IBM Planning Analytics Tips & Tricks:

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