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Data Quality Engineering

Match tuning, survivorship logic, and golden record governance

Typical duration
4–8 weeks
Best fit
Organizations post-implementation experiencing data quality issues
Deliverables
6 included
Engagement
Fixed scope

Overview

Bad match configuration silently destroys MDM value — false positives merge unrelated records, while conservative thresholds let duplicates proliferate unchecked. We audit, tune, and rebuild your Reltio match rules, survivorship logic, and data quality rule sets against your actual production data.

What's included

Match rule audit and root cause analysis
Threshold calibration using production data samples
Survivorship rule redesign by attribute and source
Data quality rule library build and documentation
Source system reliability scoring
Ongoing quality reporting and KPI dashboards

We audit, diagnose, and fix — not just document

Data quality engagements often produce reports. Ours produce working configurations. We run match rule calibration against your actual production data, deliver tuned thresholds, and validate the results before we leave.

If your duplicate rate is rising, your match quality has degraded, or your stewardship queue is growing faster than your team can handle — a focused data quality engagement typically delivers measurable improvement within 6–8 weeks.

Engagement details

Typical duration
4–8 weeks
Best for
Organizations post-implementation experiencing data quality issues