IPM Insights in Oracle EPM Cloud Planning is designed to uncover trends and anomalies in data automatically. Two key types of insights it provides are outlier detection and forecast bias detection. Outlier detection identifies data points that deviate significantly from the rest of the dataset, which could indicate errors or unusual events. Forecast bias detection analyzes historical forecast submissions against actuals to reveal systematic tendencies of planners to overstate or understate their forecasts, helping to improve forecast accuracy.

A. Prebuilt forecasting methods and freeform formulas are tools used for creating predictions and forecasts, not the "insights" derived from analyzing the data.

C. Assigning a range of possible values to uncertain inputs describes Monte Carlo simulation, a feature available in the Strategic Modeling module, not IPM Insights.

1. Oracle® Cloud Enterprise Performance Management Cloud

Administering Planning

Release 24.05

Chapter 16

"Using IPM Insights

" Section: "About IPM Insights." This section details the capabilities: "Insights find and highlight important information for you

such as... data patterns such as forecast bias... and outliers."

2. Oracle® Cloud Enterprise Performance Management Cloud

Working with Planning

Release 24.05

Chapter 14

"Analyzing Data Using IPM Insights

" Section: "Insight Types." This section explicitly lists "Outlier detection" and "Forecast bias detection" as configurable insight types.