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The CDO Cheatsheet:

How data quality degrades and why it matters

The data factory metaphor highlights how every step in data handling—transportation, copying, and manipulation—presents opportunities for error, leading to data corruption despite intentions of enhancement.

Issues arise from various factors including poor quality inputs, inadequate descriptions, software bugs, and improper sequencing, all of which can turn good data bad.

Human factors also contribute significantly, as changes in data handling by people, whether through new features, bug fixes, refactors, or optimizations, can inadvertently introduce errors that degrade data quality.

  • Poor Quality Inputs: Errors from initial data creation, like sensor malfunctions or typos, affect the base quality.
  • Improper Handling: Faulty metadata or unexpected API changes can mislead systems, mishandling otherwise valid data.
  • Software and Operational Failures: Bugs, outages, and misconfigurations in software or sequence errors in processes can lead to data loss or corruption.
  • Human Factors: Changes in team structure, undocumented modifications, or engineering adjustments can introduce new errors or amplify existing ones.

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