Data Quality Management
Data quality management is the practice of measuring and improving the accuracy, completeness, consistency, and freshness of your business data. It combines initial assessment, cleanup, and ongoing monitoring into a program that treats data quality as a continuous process rather than a one-time project.
You know your data has problems, but you do not know how bad they are or where to start. Is it a 10% duplicate rate or a 40% duplicate rate? Are 5% of emails invalid or 30%? Without measurement, you are guessing at the problem and cannot prove the ROI of fixing it.
Our Data Quality Framework
- Assessment. We audit your data across four dimensions: accuracy (is it correct?), completeness (are fields filled?), consistency (are formats standard?), and freshness (is it current?).
- Scoring. Every record gets a quality score based on how well it performs across these dimensions. Your overall database gets an aggregate health score.
- Remediation. We fix the issues identified in the assessment: cleaning, normalizing, deduplicating, and enriching to bring quality scores up.
- Monitoring. Ongoing scans track quality over time so you can see trends and catch new issues early.
- Reporting. Regular dashboards show data quality KPIs: completeness rates, accuracy trends, duplicate growth, and decay velocity.
Quality Management Outcomes
- A measurable baseline for data quality that you can track and improve over time
- Executive-level reporting on data quality that ties directly to business outcomes
- Prioritized action plans that focus cleanup effort where it has the most impact
- Prevention through monitoring that catches problems at 1% instead of 30%
Common Questions
How do you measure data quality?
We measure four dimensions: accuracy (verified against external sources), completeness (percentage of fields filled), consistency (formatting and value standardization), and freshness (how recently the data was verified). Each record gets a composite score, and your database gets an overall health grade.
What is a good data quality score?
An overall completeness rate above 80% and an accuracy rate above 90% are strong. Most databases we assess fall in the 50-70% completeness range. Duplicates under 5% is excellent. Over 15% is common and problematic. We provide industry benchmarks so you can see how your data compares to peers.
How much does data quality cost my business?
Gartner estimates poor data quality costs organizations an average of $12.9 million per year. For sales teams specifically, Salesforce research shows reps spend 27% of their time on data-related tasks instead of selling. We can calculate your specific cost based on your team size and data volume.
Related: All Data Cleaning | Data Cleaning Services | Email Verification | Data Hygiene