Data Management

Data Quality Assessment and Review: Recommended Practices

Project staff should perform periodic data-assessments during the project cycle to discover errors prior to project completion. These reviews do not need to be overly complicated, but instead serve as an opportunity to keep your data management plan, quality goals and metrics, and metadata up to date, and to generate documentation about adherence to your quality plan.

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  • Develop a data assessment strategy (test plan, specific goals)
  • Schedule data-quality reviews at important points in your workflow
  • Maintain data-quality metadata and documentation
  • Track data changes and implement a versioning scheme for your data
  • Periodically run test data through all processing scripts to verify expected functionality
  • Compare new data to historical values
  • Plot spatial data on a map to verify locations
  • Calculate summary statistics for data or display data using common graphs such as box plots to evaluate for possible anomalies.
  • Review field notes for unusual occurrences or events that may help explain data anomalies
  • Use comment fields and data quality indicators to qualify data anomalies

Data Quality Assessment and Review: References

USGS References

Non-USGS References