Data quality
Numbers you can defend, every month.
Data quality management finds where bad records enter, measures what they cost, and puts rules and owners in place so they stop coming back.
01 / Symptoms
What it looks like.
- A close that needs manual reconciliation.
- Reports that disagree depending on who built them.
- Fields left empty or filled with "N/A".
- Data teams fixing the same errors every quarter.
- Checks that run after the damage is done.
02 / What we do
Rules and owners, not one-off cleanup.
- Profiling of the critical data elements behind a symptom.
- Root cause, traced to the process or system that creates the error.
- Business impact in your own metrics.
- Rules with owners and thresholds.
- Monitoring that alerts before reports go out.
- Remediation, sequenced by cost and value.
03 / How it connects
Why quality starts in master data.
Most quality problems are master data problems in disguise: the same entity recorded differently in two places. We fix the cause, then the rules keep it fixed.
04 / Experience
- Operating models
- Production-grade data quality and stewardship operating models.
- Rule design
- Quality rules built into master data workflows, so errors are caught at entry.
Where it shows up
Questions
We bought a data quality tool and nothing changed.
Tools measure. Somebody has to own the rules and fix the process that creates the errors. That's usually the missing part.
Start with 30 minutes.
Tell us the symptom. We'll tell you plainly whether we can fix it.