What is a data quality score?
A quality score turns a fuzzy sense that "our data is bad" into a trackable metric. What gets measured gets managed.
It can be scored per record, per field, or per object, and the real value is the trend: is quality climbing, flat, or decaying?
Short answer
A data quality score is a single measure of how healthy your CRM data is, combining completeness, accuracy, consistency, uniqueness, and timeliness into one number or grade. It lets you track data health over time, compare objects, and prove that cleanup and prevention efforts are actually working.
Key takeaways
- Combines completeness, accuracy, consistency, uniqueness, and timeliness into one measure.
- Can be computed per record, per field, or per object.
- The trend over time matters more than any single reading.
- Makes data health visible and cleanup progress provable.
Why it matters
Without a score, data quality is invisible and improvement is unprovable. A score makes it a managed metric with a target and a trend, so leaders can see whether the database is getting healthier or quietly rotting.
How Ardovo handles it
Ardovo computes a live data quality score per object and field, so health is always visible. Rook works to raise it - deduping, enriching, and standardizing - and you can watch the score climb as prevention takes hold.
Frequently asked questions
How is a data quality score calculated?
By scoring records on completeness, accuracy, consistency, uniqueness, and timeliness, then combining those into one weighted number or grade. Weighting usually favors the fields that drive routing, scoring, and outreach, since their quality matters most.
What is a good data quality score?
There is no universal number - what matters is the trend and hitting your own target. Set a threshold for the fields that drive automation (say 95 percent completeness on email and company), and track whether the score is climbing or decaying.
Why track a data quality score?
Because it makes data health a managed metric instead of a vague worry. You can set targets, prove cleanup worked, catch regressions early, and justify prevention investments with a number leadership can see move.