Data Copy using DataGridBuilder in Groovy!

 

 

In this blog, we will see how to perform an intercompany data translation with ease,  

 

This rule ensures intercompany data is clean, aligned, and instantly processed when users save a form. It applies standard naming and business logic automatically—no manual cleanup needed. Data flows straight into the target  cube or POV, reducing cycle time and reconciliation effort. It improves auditability and consistency across entities, accounts, and products. The result? Faster closes, fewer errors, and more trust in your numbers

Triggering Pipeline from Groovy Rule

For example, consider running a forecast pipeline in Oracle EPM, we often deal with multiple forecast periods that need to be processed sequentially.

However, ensuring that each period runs correctly, waiting for previous jobs to complete, and handling errors gracefully can be tricky.

 

In this the script ensures that each forecast period is processed properly, one at a time, and prevents overlapping or conflicting jobs.

 

EPCM - Copy Data by Point of View operation via Groovy

If you’ve been working with Oracle EPM Cloud, particularly Profitability and Cost Management (PCM), you might need to copy data between different POVs (Point of View) in your application.

In Oracle's Enterprise Profitability and Cost Management (EPCM), efficiently managing data across different Points of View (POVs) is crucial for accurate financial analysis and reporting. A POV typically represents a specific combination of dimensions such as year, period, scenario, and version. Copying data from one POV to another allows organizations to replicate datasets across different scenarios or time periods, facilitating comparative analysis and forecasting. Oracle provides a REST API endpoint specifically designed for this purpose: the "Copy Data by Point of View" operation.

Fetching Pipeline Execution Details in Oracle EPM Using Groovy!

In Oracle EPM, pipelines are essential for automating data workflows, integrating data sources, and performing transformations. To ensure smooth execution, it's crucial to monitor pipeline runs and detect job failures in real-time.

 

When running automated data processing or calculations in Oracle EPM Cloud, monitoring the execution status of pipelines is critical. You don’t want to just trigger a pipeline and hope for the best—you need real-time insights into whether jobs succeed or fail.

 

Automate version creation and renaming in Oracle EPM with Groovy scripting!

 

Groovy scripting, which enables automation, dynamic validation, and metadata updates.

In this blog, we’ll walk through two Groovy business rules that manage version members in the Version dimension:

1. Creating a new version dynamically
2. Renaming an existing version member

We’ll explain the logic behind each rule, 

A Groovy Script for Dynamic Substitution Variables - Automating Rolling Forecast Periods in Oracle EPM

This blog explores a powerful business rule that dynamically updates forecast substitution variables based on the current fiscal period. Learn how to map months to quarters, process rolling forecasts, and seamlessly update variables for accurate planning. 🏆 Say goodbye to manual adjustments and let Groovy handle the quarterly forecasting logic with ease!

Why Automate Forecast Substitution Variables?

Substitution variables act as dynamic placeholders in Oracle EPM, allowing calculations, rules, and integrations to reference time-sensitive values (e.g., current month, current year, forecast period).

 

Extracting Notes and Attachments from Cells in Oracle Cloud EPM Using Groovy!

Lets explore on how to extract cell notes and attachments from Oracle Cloud EPM using Groovy scripting. 

This blog walks through retrieving Entity and Cost Center descendants, querying financial data, and identifying cells with notes or file attachments.

The script dynamically iterates through each combination, loads a data grid, and logs key findings after finding the cell Notes and Attachment if any. 

Suppression rules ensure efficient execution, while error handling prevents failures. Ideal for auditing financial data and ensuring compliance.