Data & Databases
Data cleaning
Finding and correcting errors, inconsistencies, or unwanted records in data.
Example
Cleaning removes duplicate customers and standardizes inconsistent state names.
Why it matters
It reduces avoidable mistakes caused by inconsistent or unusable records.
You'll hear it
“These two spellings refer to the same customer.”
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 cleaning remove a legitimate unusual case?
- Yes. Treating every outlier as an error can discard important information.
- Does cleaning guarantee the facts are true?
- No. Some errors cannot be recognized from the records alone.
- Should missing values always be filled?
- No. A guessed replacement may mislead unless its purpose and limits are clear.