AI engineering glossary · Data engineering
What is data quality?
Data quality: How well data fits its intended use, commonly judged on accuracy, completeness, consistency, uniqueness, validity and timeliness. Poor quality quietly corrupts dashboards and machine learning models, so it is measured and monitored.
Data quality: an example
In a customer table, 4 percent of phone numbers are blank and 1 percent are duplicated, so the completeness and uniqueness checks flag it.
Related terms
More Data engineering terms
- Data retention policy
- Data serialization
- Data skew
- Data SLA
- Data tiering
- Data validation
- Data vault
- Data warehouse
Where you learn it
Data quality is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.