What are the 7 data quality metrics?

The 7 Pillars of Data Quality You Can’t Afford to Ignore

When it comes to making smart business decisions, the quality of your data is everything. Garbage in, garbage out—everyone’s heard it. But what exactly makes data "good"? The answer lies in seven core metrics that professionals rely on to assess and maintain trustworthy information.

Accuracy is the foundation. Data must reflect reality—no point analyzing sales figures if they’re off by thousands. Then comes completeness: missing values or blank fields can skew results just as badly as wrong ones. A customer record without an email or address isn’t just incomplete—it’s practically useless.

Consistency checks whether data matches across systems. If your CRM says "New York" and your warehouse system says "NYC," that mismatch creates confusion. Validity ensures data conforms to the right format and rules—like dates in YYYY-MM-DD or email addresses with proper syntax.

Timeliness is often overlooked. Up-to-date data matters—especially in fast-moving industries. A customer’s old phone number may be accurate yesterday but irrelevant today. Then there’s uniqueness: eliminating duplicates. One customer listed three times inflates metrics and distorts insights.

Finally, integrity ties it all together. It’s about ensuring data relationships remain intact—like keeping order records linked to real customers, not orphans in a database. Break one link, and the chain fails.

Together, these seven dimensions—accuracy, completeness, consistency, validity, timeliness, uniqueness, and integrity—form the backbone of reliable data. Ignore any one of them, and you risk decisions built on sand. In a world driven by data, quality isn’t optional. It’s survival.