Anomaly Detection in Workday Adaptive Planning helps finance, sales, and workforce planning teams find unusual numbers before they create bigger problems in the planning cycle.
In simple terms, it helps users compare plan numbers against expected values. If a number looks too high, too low, or outside the normal pattern, the system can highlight it for review.
This is useful because planning teams often work with large volumes of data. Budgets, forecasts, workforce plans, sales plans, and operating expense plans can include thousands of intersections across accounts, levels, versions, and time periods. Reviewing every number manually is slow and risky.
Anomaly Detection helps shift the review process from manual checking to exception based review. Instead of looking at everything line by line, teams can focus on the numbers that appear unusual.
What Is Anomaly Detection in Workday Adaptive Planning
Anomaly Detection in Workday Adaptive Planning is part of Intelligent Planning. It uses machine learning to generate predictions from historical actuals data.
The system reviews past actuals, learns from the pattern, and creates predicted values. These predicted values are stored in a prediction version. Users can then compare budgets, forecasts, or other plan versions against those predicted values.
The goal is not to replace finance review. The goal is to make review more focused.
For example, if an expense account usually trends around a certain monthly range and the forecast suddenly jumps much higher, Anomaly Detection can flag that number. A finance user can then review whether the change is valid, a data issue, or a planning mistake.
Why Anomaly Detection Matters in Planning
Planning errors do not always come from major mistakes. Many errors come from small issues that are hard to notice during review.
A number may be typed incorrectly.
A forecast assumption may not be updated.
A hiring plan may create an unexpected cost increase.
A sales forecast may move too far away from the historical trend.
An operating expense line may be copied incorrectly across months.
Without anomaly detection, finance users often find these issues late in the process, sometimes after reports are already shared.
Anomaly Detection helps teams catch unusual values earlier. This improves planning quality and reduces the amount of manual review needed during budget and forecast cycles.
Where Anomaly Detection Can Be Used
Anomaly Detection can support different planning areas in Workday Adaptive Planning.
It can be useful for:
Financial planning
Sales planning
Workforce planning
Expense planning
Budget review
Forecast review
Outlier reporting
Standard sheet review
Scenario comparison
The feature is especially useful when teams have strong actuals history and want a better way to compare plan values against expected values.
How Anomaly Detection Works
The process starts with historical actuals.
Workday Adaptive Planning uses actuals data to generate a prediction line. This prediction line represents what the system expects based on historical patterns.
After the prediction is generated, users can compare plan data against it.
If a plan value is materially higher or lower than the prediction, it may be flagged as an anomaly.
The basic flow is:
Complete the planning model
Load enough actuals history
Set up the correct time structure
Create a prediction version
Generate predictions
Compare plan values against predicted values
Review anomalies in sheets or reports
This makes the review process more structured and easier to manage.
The Planning Model Must Be Ready First
Anomaly Detection is not the first step in a Workday Adaptive Planning implementation.
Before using it, the planning model should already be completed. The model can be financial, sales, or workforce based, depending on the use case.
This matters because Anomaly Detection depends on the structure and data already available in the model.
If the model is poorly designed, the anomaly results may not be useful. If accounts, levels, versions, or assumptions are not structured properly, the system may generate predictions that are difficult to interpret.
A strong model design is the foundation.
Actuals History Is Required
One of the most important requirements is actuals history.
Workday Adaptive Planning requires 24 months of actuals data for Anomaly Detection.
This is important because the predictions are based on historical actuals. The system needs enough data to understand the pattern before it can generate useful predictions.
If there is not enough actuals history, the machine learning process does not have a strong base to work from.
For finance teams, this means actuals data should be loaded, mapped, and validated before setting up anomaly detection.
Why 24 Months of Actuals Matters
Twenty four months of actuals gives the system enough historical pattern to compare against.
This helps with:
Seasonality
Monthly trends
Recurring expense patterns
Revenue movement
Workforce cost trends
Business cycle changes
If the actuals history is too short, the prediction may not reflect normal business movement. That can lead to weak alerts or too much noise.
Good actuals data improves the quality of anomaly detection.
Prediction Versions in Workday Adaptive Planning
A prediction version is a special plan version used to store predicted values.
When Anomaly Detection runs, the system writes the machine learning generated predictions into this version.
This prediction version then becomes the comparison point for budgets, forecasts, or other plan versions.
In simple terms:
Actuals history helps create the prediction.
The prediction is stored in the prediction version.
Plan data is compared against the prediction version.
Anomalies are highlighted based on the comparison.
This setup keeps predicted values separate from regular planning data.
Default Time Strata Should Be Month
For Anomaly Detection, the default time strata should be set to Month.
This is important because anomaly detection depends on time based patterns. Monthly planning data needs a monthly structure so the system can compare values correctly across periods.
If the time setup does not match the review process, the results may not be as helpful.
For most finance planning teams, monthly review is the natural level for budgeting, forecasting, and variance analysis.
Security and Permissions
Security is also important when setting up Anomaly Detection.
Users need the right permissions to work with calendar settings and versions.
Calendar permission is needed because time setup is part of the process.
Version permission is needed because prediction versions must be created and used.
Without the right permissions, users may not be able to complete the setup or manage prediction versions properly.
This is a common area to check when users cannot access or complete anomaly detection setup steps.
Optional Anomaly Thresholds
Workday Adaptive Planning also allows teams to define custom anomaly thresholds.
This is useful because not every account behaves the same way.
Some accounts naturally move a lot from month to month. Others stay stable. A single default rule may work for some accounts but create too much noise for others.
Custom thresholds allow teams to define what should count as unusual for each account, version, and level combination.
This gives finance teams more control over anomaly review.
Why Custom Thresholds Matter
Custom thresholds make anomaly detection more practical.
For example, travel expense may move sharply during some months. A 40 percent movement may not always be unusual.
But rent expense may be stable. Even a smaller movement may need review.
The same threshold should not always apply to both accounts.
Custom thresholds help reduce false alerts and make the review process more useful.
How Custom Thresholds Are Defined
Custom thresholds can be defined through a modeled sheet.
The modeled sheet can hold threshold values by account, version, and level.
This allows different areas of the plan to have different rules.
For example:
Revenue accounts can have one threshold.
Payroll accounts can have another threshold.
Operating expense accounts can have another threshold.
Specific levels or departments can have their own threshold.
This gives more flexibility than relying only on the default anomaly logic.
Account Setup for Anomaly Detection
There are optional account settings that can help with anomaly detection.
For general ledger accounts and custom accounts, the account setup should be reviewed carefully.
Recommended setup includes:
Account Type set to Periodic
Data Entry Sheet Type set to Standard
Start Expanded selected where useful
Start Expanded can help users see anomalies more easily because accounts open by default during review.
This is a small setup choice, but it can improve the user experience.
Standard Sheet Setup
Standard sheets can also be adjusted for anomaly review.
If the goal is to highlight account anomalies in standard sheets, custom dimensions may need to be removed from the sheet.
This makes the sheet simpler and can make anomaly highlighting easier to use.
This is a design decision. The right setup depends on how users review data and how much detail is needed in the sheet.
A simple sheet layout often works better for anomaly review because users can see the flagged numbers more clearly.
Default View of Sheets
Teams can also set the default view of sheets for Anomaly Detection.
This is useful when users review anomalies directly in sheets.
The default view controls what users see first. If the view is well designed, users can quickly identify the flagged values and understand what needs attention.
A poor default view can make anomaly detection harder to use, even if the setup is technically correct.
Generating Predictions
After setup is complete, the next major step is to generate predictions.
This is where the machine learning process runs.
The system reads actuals data, identifies patterns, and writes predicted values into the prediction version.
This step can take time. Machine learning predictions may take a few days to complete, so teams should not treat this as an instant process.
It should be planned as part of the setup and review timeline.
What Happens After Predictions Are Generated
After predictions are generated, the predicted data is stored in the prediction version.
Workday Adaptive Planning can then compare forecast or plan values against the generated prediction.
Anomalies can be highlighted in standard sheets.
This is where the feature becomes useful for everyday planning review.
Instead of asking users to manually scan every number, the system points them toward values that may need attention.
Default Anomaly Logic
By default, anomalies are values that are 40 percent higher or 40 percent lower than the generated prediction.
This default applies unless custom thresholds have been defined.
This is important because users need to understand what the system is flagging.
It is not flagging every variance.
It is flagging values that move materially above or below the prediction line.
That makes anomaly detection more focused and useful for review.
Example of Anomaly Detection
Assume an expense account is usually around 100,000 per month based on actuals history.
The system generates a prediction close to that range.
If the forecast value for a future month is 145,000, the system may flag it because it is more than 40 percent above the prediction.
The finance user can then review the value.
Maybe the increase is valid because of a planned project.
Maybe someone entered the wrong number.
Maybe the account mapping is incorrect.
Maybe the assumption was copied incorrectly.
The anomaly does not automatically mean the number is wrong. It means the number deserves review.
Using Anomaly Detection in Sheets
Anomaly Detection can be used directly in sheets.
This helps users review flagged values where they already work.
For finance users, this is useful because they do not need to move to a separate reporting process just to find unusual values.
They can review anomalies while working through budget or forecast sheets.
This makes the review process easier and more practical.
Using Outlier Reports
Anomaly Detection can also support outlier reports.
Outlier reports help summarize unusual values based on report calculations.
This is useful when teams want to review anomalies across accounts, levels, entities, departments, or versions in one place.
Some teams may prefer report based anomaly review because it gives a cleaner management view.
For example, a finance lead may want a report that shows the top flagged accounts by department for the current forecast version.
This can help focus review meetings on the most important changes.
Anomaly Detection for Budget Review
Budgeting usually includes many assumptions across many teams.
Errors can happen easily.
A department may submit a number much higher than historical activity.
A cost line may be duplicated.
A workforce assumption may not match the hiring plan.
A revenue plan may not follow the expected business pattern.
Anomaly Detection helps budget owners and finance teams find these issues earlier.
It gives users a better way to review the budget before it moves into final approval.
Anomaly Detection for Forecast Review
Forecasting is often done monthly or quarterly.
The faster the cycle, the easier it is to miss unusual values.
Anomaly Detection helps by comparing forecast numbers to expected values based on actuals history.
This can help finance teams catch:
Unexpected expense increases
Unusual revenue movement
Workforce cost changes
Data entry mistakes
Mapping issues
Planning assumptions that need explanation
This supports better forecast quality and faster review.
Anomaly Detection for Workforce Planning
Workforce planning is a strong use case for anomaly detection.
Headcount and compensation costs can move quickly. New hires, terminations, promotions, merit increases, bonuses, and benefit assumptions can all affect the forecast.
Anomaly Detection can help identify unusual workforce cost movement before it flows into financial reporting.
This is helpful when workforce plans are connected to the broader financial model.
Anomaly Detection for Sales Planning
Sales planning can also benefit from anomaly detection.
Sales forecasts may include pipeline assumptions, booking expectations, revenue timing, quota changes, and territory plans.
If a forecast value moves far outside the expected pattern, anomaly detection can flag it for review.
This helps sales finance teams identify areas that may need better explanation or correction.
What Anomaly Detection Does Not Do
Anomaly Detection is helpful, but it does not replace finance judgment.
It does not automatically decide whether a number is right or wrong.
It does not fix data quality issues by itself.
It does not replace a clean planning model.
It does not remove the need for strong actuals integration.
It does not replace business review.
It is a review tool. The value comes from how finance teams use the alerts.
Common Setup Issues
Teams may face issues if setup is incomplete.
Common problems include:
Not enough actuals history
Prediction version not created correctly
Time strata not set to Month
Calendar permission missing
Version permission missing
Accounts not configured correctly
Sheets too complex for clear anomaly review
Custom thresholds not designed properly
Actuals data not validated
Model structure not aligned with business review
These issues can reduce the quality of anomaly detection and make the output harder to trust.
Best Practices for Anomaly Detection in Workday Adaptive Planning
To get better results, teams should follow a clean setup and review process.
Useful best practices include:
Validate actuals before generating predictions
Make sure 24 months of history is available
Set default time strata to Month
Create a clear prediction version
Use custom thresholds where default logic creates too much noise
Keep standard sheets simple for review
Set useful default sheet views
Check calendar and version permissions early
Plan enough time for prediction generation
Use outlier reports for management review
Train users that anomalies are review points, not automatic errors
These practices help make anomaly detection more useful and easier to trust.
Simple Setup Flow
A simple setup flow looks like this:
Complete the model
Load and validate 24 months of actuals
Set default time strata to Month
Confirm security permissions
Create the prediction version
Review account settings
Prepare sheets for anomaly review
Define custom thresholds if needed
Generate predictions
Review anomalies in sheets and reports
This flow gives teams a practical way to move from setup to review.
Why Anomaly Detection Improves Planning Review
The biggest value of Anomaly Detection is focus.
Finance teams do not need more reports for the sake of reporting. They need better signals.
Anomaly Detection gives users a way to focus on numbers that are outside the expected range.
This can help teams:
Catch planning mistakes earlier
Reduce manual review time
Improve forecast quality
Strengthen budget review
Make workforce and sales planning more controlled
Explain unusual movements faster
Improve confidence in planning outputs
The result is a better review process.
Conclusion
Anomaly Detection in Workday Adaptive Planning helps teams identify unusual plan values by comparing them against machine learning generated predictions based on actuals history.
The setup depends on a completed model, 24 months of actuals data, monthly time setup, prediction versions, and the right security permissions.
Teams can also improve the process by defining custom thresholds, preparing accounts, simplifying sheets, and using outlier reports.
The feature does not replace finance review. It makes finance review more focused.
When used properly, Anomaly Detection helps finance, sales, and workforce planning teams move from manual scanning to exception based review. That means less time searching for issues and more time understanding what the numbers are telling the business.
