The 6 Dimensions of Data Quality

Data quality has 6 dimensions. Most teams only measure 2. Here’s what you’re missing.

Most teams get data quality wrong the same way: they check if the data is there (completeness) and if the numbers add up (accuracy). Then they call it a day.

But data quality has six dimensions. The other four are where the real damage happens:

Consistency - same customer, different numbers in three systems. Nobody knows which is right.

Timeliness - the data is correct… as of yesterday. The decision was made this morning.

Uniqueness - duplicate records that inflate every metric. Marketing thinks they have 50K leads. They have 35K.

Validity - the data passes type checks but violates business rules. A product quantity of 10,000 is a Tuesday for a wholesaler. For a D2C shop selling handmade furniture, it’s a data entry error that just triggered a warehouse restock.

I’ve changed my mind on where to start, by the way. I used to say accuracy first. Now I’d say consistency. Because inconsistent data erodes trust faster than inaccurate data. People can tolerate a wrong number. They can’t tolerate three different “right” numbers.

Pick the dimension that’s hurting your team most. Fix that one first.

Which quality dimension causes the most incidents on your team?