Defects show up before the load, not after
IDN pulls reference data from the S/4HANA target, such as units of measure, material groups and material types, and validates every record against it before each load. Each object gets a running quality score, and a side-by-side pre-load report is reviewed and approved before anything is published.
| The project way | Rules checked against assumptions; failures appear at load time. |
| The IDN way | Rules checked against S/4HANA reference data, with a score and approval before every load. |
The problem
Rules are only as good as what they check against. When they are written from assumptions about the target, failures appear at load time, and each one costs another cycle.
How IDN does it
- Target data: reference data pulled from S/4HANA so rules check against the real configuration.
- RulePilot validation: every record checked against the rules, with a score per object.
- Pre-load review: side-by-side report of source and converted data, then approval to publish.
What it means for the program
Fewer failed loads, and a clear view of readiness before each cycle.
Questions
How do you validate data against S/4HANA configuration?
Pull the target's reference (check-table) data into IDN and run the validation rules against it. A value that does not exist in the target is caught before the load instead of failing during it.
What is a pre-load or side-by-side report?
A report that shows the converted records next to their source before they are published, with errors listed. The team reviews it and approves the object for publishing.
How do we measure data readiness for go-live?
Use a running quality score per object, based on the share of records that pass every rule, plus the list of open findings. It shows how close each object is before the next test cycle.