Dayalan Punniyamoorthy Blog

Thursday, April 24, 2025

Run Data Management rule on Autopilot: Streamlining DM Data Jobs with Groovy

Have you ever wanted to run a series of Data Management integrations in Oracle Cloud EPM automatically for specific forecast periods — all while staying user-friendly with runtime prompts and full-on status tracking?

Well, this Groovy business rule does exactly that. Let’s break it down

 

This Groovy rule is designed to automate and control the execution of Data Management (DM) integration jobs for forecast data. It dynamically interprets user input, validates required conditions, and executes the appropriate integration job for each forecast period in sequence.

The rule orchestrates the execution of multiple forecast data movement jobs in Oracle Cloud EPM. The jobs transfer forecast data from one scenario/version to another (like from mid-forecast to working version) using a Data Management (DM) integration job. The timing and execution logic is governed by runtime prompt inputs and substitution variables.

Monday, April 14, 2025

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

Tuesday, April 8, 2025

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.