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    Data Integrity — AI transformation lever in the Future Positive Atlas

    PREPARE YOUR DATA / DA-11

    Data Integrity

    0.28Adaptability average

    Unreliable data quietly poisons AI outputs over time, so data integrity aims to prevent the corruption, manipulation and quality drift that erode reliability. KPMG Trusted AI defines integrity as the rigorous maintenance of accuracy, completeness and compliance across the full data lifecycle, specifying both technical controls — validation rules at ingestion, automated quality checks, version control — and procedural controls covering handling, access, modification and retirement. Implementation typically involves dedicated integrity functions, standardised lifecycle stages, audit processes that detect and remediate issues, and the infrastructure that enforces compliance across distributed data environments.

    Potential across the 5 Future Positive Principles

    Self-Directed
    Agency-Centered
    Impact-Led
    System-Focused
    Evolution-Driven
    Industry Standard baseline
    Future Positive potential

    Source Frameworks