Why Data Quality Matters
Every chart and model inherits the quality of the data underneath. Here is how to think about it practically.
Analytics.sa · · 6 min read
Data quality is rarely the most exciting topic in analytics, but it is one of the most consequential. An elegant model built on inconsistent data produces elegant mistakes.
Dimensions of quality
- Completeness: are the expected records and fields present?
- Accuracy: do values reflect reality?
- Consistency: do the same facts agree across systems?
- Timeliness: is the data fresh enough for its use?
- Validity: do values follow the expected formats and rules?
Quality is a process, not a project
One-off clean-up efforts decay. Sustainable quality comes from automated checks in pipelines, clear ownership of source data, and feedback loops that fix problems where they originate.
Fit for purpose
Perfect data is not the goal. The right standard depends on the decision. A strategic trend analysis tolerates some noise; a financial report does not.