Every new material in SAP is a chance for bad data to get in. With DQC, material data is checked against your rules before it is saved, completed from similar existing materials, and corrections from DQC flow back into SAP under your control. No data migration, no change to your data model.
Most data quality problems in SAP do not start in a migration project. They start on an ordinary day, in an ordinary transaction: a material number that already exists, an English short text that describes something else, an empty material group, a missing commodity code. Each gap is small. Downstream, in purchasing, logistics, customs, and reporting, they add up.
DQC addresses this in both directions. It stops bad data at the point of entry, and it writes improvements for existing data back into SAP. The two short videos below show both directions in an SAP S/4HANA system.
Check before saving and auto improve with DQC
Finding errors is half the job. With Improve with DQC, the user starts an improvement flow that builds the new material from the most similar existing one:
Data input: one new material with five findings from the ruleset.
Reference material lookup: the most similar material is 2000-1202 (96 % similar), based on five neighbours.
Data enrichment (AI): seven values are proposed.
Verification against the ruleset: the improved material is validated again through the DQC Validation API.
