Which concept refers to ensuring accuracy, consistency and integrity of data?

Which concept refers to ensuring accuracy, consistency and integrity of data?

• A. Data Quality
• B. Data Governance
• C. Data Provenance
• D. Metadata Management

Answer: (A)

Ensuring accuracy, consistency, and integrity of data is a matter of data quality. Data quality focuses on how trustworthy data is for decision-making, capturing how close data is to real-world values (accuracy), how uniform data remains across systems and over time (consistency), and how complete and uncorrupted data is (integrity). When data quality is high, analyses and reports can be trusted and used confidently. Data governance, while related, is about the overarching policies, roles, and processes that organize how data is managed across the organization. It sets the framework within which quality can be maintained but isn’t the definition of quality itself. Data provenance refers to the origin and history of data, which supports trust and traceability but doesn’t define data quality. Metadata management deals with data about data—descriptions, definitions, and context—that help users understand and use data correctly, yet it’s not the measure of quality itself.

Ensuring accuracy, consistency, and integrity of data is a matter of data quality. Data quality focuses on how trustworthy data is for decision-making, capturing how close data is to real-world values (accuracy), how uniform data remains across systems and over time (consistency), and how complete and uncorrupted data is (integrity). When data quality is high, analyses and reports can be trusted and used confidently.

Data governance, while related, is about the overarching policies, roles, and processes that organize how data is managed across the organization. It sets the framework within which quality can be maintained but isn’t the definition of quality itself. Data provenance refers to the origin and history of data, which supports trust and traceability but doesn’t define data quality. Metadata management deals with data about data—descriptions, definitions, and context—that help users understand and use data correctly, yet it’s not the measure of quality itself.