Data & Databases
Data quality
How accurate, complete, consistent, timely, and usable a dataset is.
Example
A quality check reports missing account IDs and impossible transaction dates.
Why it matters
It helps teams judge whether information is fit for the task they want to perform.
You'll hear it
“The records are complete, but they're too old for this forecast.”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Can data be high quality for one task and poor for another?
- Yes. Required detail, freshness and coverage depend on the use.
- Does more data compensate for inaccurate data?
- Not necessarily. More wrong records can strengthen misleading patterns.
- Can quality decline without changing the file?
- Yes. Information can become outdated as the real world changes.