Data quality is the degree to which data is fit for its intended purpose, measured across six dimensions: Accuracy (data correctly represents the real-world entity it describes), Completeness (all required data is present), Consistency (data values are coherent across systems and records), Timeliness (data is available and current when needed), Validity (data conforms to defined formats, types, and business rules), and Uniqueness (no unintended duplicate records exist). Within the ISACA CDPSE framework, data quality is a foundational lifecycle property — not a one-time project — that must be defined relative to how data will be used. A customer record that is adequate for marketing may be critically deficient for regulatory reporting. Data quality is NOT synonymous with data cleaning, NOT a purely technical discipline, and NOT static; it requires continuous governance across the data lifecycle through people, process, and technology.
Where it stops · what it isn't
- —Data quality IS a governance discipline requiring organizational ownership, process standards, and ongoing measurement — not a tool or a one-time cleansing exercise.
- —Data quality IS purpose-relative: the same dataset may be high quality for one use case and unacceptably poor for another (e.g., analytics vs. compliance reporting).
- —Data quality IS NOT the same as data security or data privacy, though poor quality can create compliance gaps that intersect with both.
- —Data quality IS NOT exclusively an IT or data engineering responsibility — business data owners and stewards share accountability.
- —Data quality does NOT guarantee business outcomes; it is a prerequisite condition that enables reliable decisions, compliant reporting, and trustworthy analytics.
- —Data quality frameworks do NOT prescribe specific tools; they define capabilities and governance models implementable across a range of platforms.
Connected concepts in the graph
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PART OFCDPSE Data Lifecycle Knowledge AreaCDPSE Data Purpose Domain
REQUIRESData Governance FrameworkData Stewardship and Ownership Roles
ENABLESData Protection and Privacy EngineeringRegulatory Compliance Reporting (GDPR, CCPA, Basel III)Trustworthy AI/ML and Analytics
RELATED TOData Classification and CatalogingMaster Data Management (MDM)
CONSTRAINSData-Driven Decision Making