Quality — Reliable data for analytics and AI
AI cannot run on unqualified data. Duplicates, heterogeneous formats, inconsistent values: every defect costs you wrong results and stalled projects. The Quality pack fixes the raw material.
Do any of these situations sound familiar?
Your data is duplicated, inconsistent or incomplete
Risk: flawed decisions and AI models.
No data is clearly the reference
Risk: constant conflicts between your applications.
You feed AI with unqualified data
Risk: unreliable, unexplainable results.
What you get
Standardization
Formats, reference data and consistency rules applied across all sources.
Qualified AI datasets
Deduplicated, documented and traceable datasets, ready for your ML pipelines.
Cross-system consistency
Detection of divergences between applications on the same business objects.
Observed results
Figures from production projects at our clients — actual results vary by context.
How it works
- Automatic mapping and profiling of sources (MAP)
- Detection of anomalies, duplicates and inconsistencies
- Standardization and quality rules, applied and replayable
- Production of qualified datasets for analytics and AI (REUSE)
What sets us apart
A classic tool
A data-quality tool flags anomalies… and stops at the report.
Data Recycling®
Data Recycling fixes, hardens and re-runs the rules over time, with full traceability.
Proof, not promises
Every action produces a signed, timestamped Evidence Pack, opposable in an audit. Seen on a real project:
See the “System archiving & data quality — Randstad” case →Activate the Quality pack
30 minutes, a Data/AI assessment, first results within 24 hours.
Assess my data quality →