2026 Aug 26 9:16 AM
What approach have people taken for handling data quality issues when integrating multiple S/4HANA systems into Datasphere?
What approach have people taken for handling data quality issues when integrating multiple S/4HANA systems into Datasphere?
2026 Aug 27 9:57 PM
When integrating multiple S/4HANA systems into Datasphere, the biggest challenge is inconsistent master data across systems. The approach we’ve seen work best is a layered one:
Master Data Governance (MDG) at the source to enforce validation rules and harmonize customer, supplier, and product records.
Semantic modeling in Datasphere to align meaning across systems so analytics don’t break when fields differ.
Data federation instead of bulk ETL, so you avoid duplicating bad data and always read the latest state.
Data quality monitoring dashboards to catch anomalies early and feed corrections back into MDG workflows.
This combination keeps governance tight, avoids data sprawl, and ensures that integrated analytics reflect a single, trusted version of the truth.
4 weeks ago
Thank You Atul for detailed explanation. If the data from different s/4 HANA systems needs a lot of cleaning or matching before we can use it, would federation would still be the better option ? or would you replicate the data first, clean and harmonize it, and then use it for reporting ?