Data Pipeline and data lifecycle

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Across
  1. 2. Unnecessary duplication of data
  2. 5. The extent to which data measures what it is supposed to
  3. 8. Applying data insights for decision-making or visualization
  4. 11. How different phases of the lifecycle influence each other
  5. 12. Checking data against rules or constraints
  6. 13. Repeating a process to refine or improve results
  7. 14. The overall condition or fitness of data for its intended use
  8. 15. Transforming raw data into usable formats
  9. 17. Coordinated control of data throughout its lifecycle
Down
  1. 1. The degree to which data correctly reflects the real-world values
  2. 2. Consistency of data over time and across systems
  3. 3. Confirming that data is correctly entered and processed
  4. 4. The act of collecting or ingesting data
  5. 6. Ensuring data is accurate, reliable, and trustworthy
  6. 7. Availability of data when it is needed
  7. 9. The sequence of stages data goes through from creation to disposal
  8. 10. Saving data in physical or cloud-based systems
  9. 16. Examining data to extract insights