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Quality Control (QC) - Detecting and Repairing Data Issues: Recommended Practices

Quality control (QC) of data refers to the application of methods or processes that determine whether data meet overall quality goals and defined quality criteria for individual values. In order to determine whether data are 'good' or 'bad' - or to what degree they are so - one must have a set of quality goals and specific criteria against which data are evaluated.

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  • Evaluate and tag data for known issues and quality status
  • Verify data integrity when values are transformed using
  • scripted or automated processes
  • Validate your data products against your original quality criteria
  • Provide feedback to QA processes
  • Build a process for reporting data errors and tracking repairs

Quality Control (QC) - Detecting and Repairing Data Issues: Examples

Generic: 5 Examples of Quality Control 

  • Peer review - catch data issues prior to release or publication
  • Map coordinate data to validate location information
  • Longitude in the U.S. should be a negative value!
  • Use scripts to automate evaluation of data against accepted ranges or domains
  • Collect QC samples (replicates, trip blanks, spiked samples, etc.) to detect hidden issues affecting measurements, such as contamination or poor equipment calibration
  • Request laboratory reruns for questionable data results

Published Quality Control Standards and Methods