How to measure CRM data quality
Measuring data quality turns a vague worry into a number you can manage, target, and prove you are improving.
The five dimensions give a complete picture, and the trend over time matters more than any single reading.
Short answer
Measure CRM data quality by scoring five dimensions - completeness, accuracy, consistency, uniqueness, and timeliness - across your key objects and fields, tracking each as a percentage, weighting the fields that drive automation most, and watching the trend over time. Measure continuously if you can, so quality is a live metric you can prove is improving rather than a one-off audit.
Step by step
Score the five dimensions
Measure completeness, accuracy, consistency, uniqueness, and timeliness for your key objects and fields, each as a percentage.
Weight the fields that matter
Emphasize the fields that drive routing, scoring, and outreach, so the score reflects quality where it counts most.
Track the trend
Watch the scores over time, since the trajectory - improving, flat, or decaying - matters more than a single snapshot.
Measure continuously
Automate the measurement where possible, so quality is a live metric rather than a periodic manual audit that goes stale.
The trend matters more than the number
A single data-quality reading tells you the state today; the trend tells you whether prevention is working or quality is decaying. Measure continuously and watch the trajectory, so you catch regressions early and can prove that cleanup and prevention efforts are actually moving the number.
How Ardovo helps
Ardovo measures data quality live per object and field across the five dimensions, so the score is always current. Rook works to raise it and you watch the trend climb, so quality is a managed, provable metric rather than a number from an audit that is stale the next day.
Frequently asked questions
What dimensions measure data quality?
Five: completeness (are key fields filled), accuracy (are values correct), consistency (are formats standardized), uniqueness (duplicate rate), and timeliness (is the data current). Scoring these as percentages across your key objects gives a complete, trackable picture of data health.
How often should you measure data quality?
Continuously if you can, so it is a live metric rather than a one-off audit that goes stale. If measuring manually, do it before and after cleanups and periodically between. The trend over time matters more than any single reading, which is why continuous measurement is ideal.
What is a good data quality score?
There is no universal number - it depends on your data and needs. Set targets for the fields that drive automation, such as high completeness on email and company, and focus on the trend. Whether the score is climbing or decaying tells you more than comparing it to an arbitrary benchmark.