Which term focuses on data's completeness, consistency, and reliability throughout its lifecycle, including protection against tampering?

Which term focuses on data's completeness, consistency, and reliability throughout its lifecycle, including protection against tampering?

• A. Data Integrity
• B. Data Accuracy
• C. Data Provenance
• D. Data Quality

Answer: (A)

Data integrity is the state of data being complete, consistent, and trustworthy throughout its life, with safeguards that prevent unauthorized changes. It isn’t just about correct values at a single moment; it encompasses the ongoing accuracy and reliability of data as it is created, stored, transmitted, and updated. Techniques like checksums or hashes, cryptographic signatures, versioning, strict access controls, and audit trails ensure data hasn’t been tampered with and remains trustworthy over time. Data accuracy, while related, focuses only on the correctness of values at a given point. Data provenance tracks where data came from and how it’s processed, which helps assess trust but doesn’t by itself guarantee ongoing integrity. Data quality covers multiple dimensions (completeness, timeliness, consistency, etc.) but the term that most directly centers on maintaining trust and preventing tampering across the lifecycle is data integrity.

Data integrity is the state of data being complete, consistent, and trustworthy throughout its life, with safeguards that prevent unauthorized changes. It isn’t just about correct values at a single moment; it encompasses the ongoing accuracy and reliability of data as it is created, stored, transmitted, and updated. Techniques like checksums or hashes, cryptographic signatures, versioning, strict access controls, and audit trails ensure data hasn’t been tampered with and remains trustworthy over time. Data accuracy, while related, focuses only on the correctness of values at a given point. Data provenance tracks where data came from and how it’s processed, which helps assess trust but doesn’t by itself guarantee ongoing integrity. Data quality covers multiple dimensions (completeness, timeliness, consistency, etc.) but the term that most directly centers on maintaining trust and preventing tampering across the lifecycle is data integrity.