Data Quality: Why It Matters and How We Ensure It
"Garbage in, garbage out" — data-driven decisions are only as good as the data they are based on. Poor data quality erodes trust in analytics, leads to bad decisions, creates compliance risks, and wastes analyst time on data cleaning rather than insight generation.
Dimensions of Data Quality
- Accuracy: Does the data correctly reflect reality? Are values within expected ranges?
- Completeness: Are required fields populated? Are there unexpected nulls?
- Consistency: Is the same entity represented consistently across systems? (e.g. the same customer has the same ID in your CRM and your analytics platform)
- Timeliness: Is data available when needed? Is it current?
- Validity: Does data conform to defined formats, types, and constraints?
- Uniqueness: Are there duplicate records that should be deduplicated?
Data Quality Controls
- Source system validation: Input validation in the applications that generate data — the cheapest place to prevent quality issues
- Pipeline testing: Automated tests on data pipelines that alert when data quality degrades (e.g. row count drops unexpectedly, null rates increase)
- dbt tests: Schema tests (not null, unique, referential integrity, accepted values) on transformed data models
- Data observability: Tools like Monte Carlo or Great Expectations that continuously monitor data quality across your warehouse
Data Quality Ownership
Data quality is a shared responsibility between the teams that generate data and the teams that consume it. We help clients define data ownership, quality SLAs, and monitoring processes as part of data platform work.