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Data Quality

Make Data Trustworthy for Decisions

💡 Hover over any tip or practice to see practical examples
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Data Quality Dimensions

  • Define accuracy, completeness, consistency, timeliness, validity
  • Agree on what “good enough” means for each use case
  • Prioritize the dimensions that affect decisions the most
  • Document rules and thresholds for each dimension
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Profiling & Baselines

  • Profile distributions, null rates, duplicates, and outliers
  • Create a baseline so changes are measurable over time
  • Segment profiling by key slices (region, channel, product)
  • Track drift and anomalies, not just one-time checks
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Cleaning & Standardization

  • Standardize formats (dates, currency, casing, categories)
  • Deduplicate using stable business keys and matching rules
  • Handle missing values with clear business logic
  • Fix common issues upstream where possible
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Validation & Testing

  • Add row count, totals, and reconciliation checks
  • Validate relationships and referential integrity
  • Use reasonableness checks (bounds, ranges, allowed values)
  • Test refresh scenarios and failure paths
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Governance & Ownership

  • Assign data owners and escalation paths
  • Define SLA expectations for freshness and issue response
  • Maintain a data dictionary and business definitions
  • Log issues and track resolution with root cause
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Power BI Quality Practices

  • Use a dedicated Date table and consistent keys
  • Hide unused columns and use clear naming conventions
  • Build quality KPIs (null %, duplicate %, refresh success)
  • Surface warnings when data is stale or incomplete

Best Practices for Data Quality

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Define Fitness for Use
Agree on what “good enough” means for the decision. Set thresholds for the dimensions that matter most.
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Profile Before You Fix
Baseline nulls, duplicates, ranges, and category values so you can measure improvement and detect drift.
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Standardize at the Source
Normalize formats and categories upstream where possible to avoid repeating the same cleanup in every report.
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Validate With Tests
Use reconciliation, referential integrity checks, and reasonableness tests. Refresh success is not correctness.
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Assign Ownership
Define data owners, SLAs, and an issue log. Quality improves when accountability is clear.
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Make Quality Visible
Add a Data Health page with key quality KPIs and stale-data warnings so users can trust what they see.