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Also known as: Data Quality Principle, Fit for Purpose Principle
Commitment to continuously measure, monitor, and improve data’s fitness for intended purposes.
DESCRIPTION
The Quality Principle prioritizes data accuracy, completeness, consistency, timeliness, and validity. It requires explicit rules, metrics, and SLAs aligned to business use cases. Profiling, testing, and observability automate detection of defects. Remediation workflows and root-cause analysis prevent recurrence. Standards like ISO/IEC 25012 and practices like data contracts reinforce expectations. A quality culture ensures analytics and AI outcomes remain reliable.
ALTERNATIVE DEFINITIONS
Wikipedia
Data quality refers to the state of qualitative or quantitative pieces of information.
Provides a broad framing of data quality as a state or condition.
Open Data Handbook
Data quality is a measure of the condition of data based on factors such as accuracy, completeness, reliability, and relevance.
Emphasizes key quality dimensions used in practice.
RELATED TERMS
Data Quality Data Profiling Data Observability Data Validation Data SLA Quality Metrics ISO 8000 ISO-IEC 25012 Master Data Management Data Cleaning
COMPETING TERMS
KNOWLEDGE AREAS
Data Quality Data Management Data Governance Testing & Validation
TARGET AUDIENCE
Data Steward Data Governance Manager Data Engineer Data Analyst Chief Data Officer
EXTERNAL SOURCES
- Wikipedia - Data quality
- Great Expectations - Open-source data quality framework
- Google Cloud - Managed data quality in Dataplex
TOP QUESTIONS
What is the Quality Principle in data management?
It is the commitment to continuously measure and improve data’s fitness for purpose using defined rules, metrics, and controls.
Category: Definition
Which dimensions does the Quality Principle emphasize?
Accuracy, completeness, consistency, timeliness, validity, and sometimes uniqueness and integrity.
Category: Concepts
How is the Quality Principle enforced?
Through data profiling, automated tests, observability, SLAs, and remediation workflows tied to ownership.
Category: HowTo
Why is the Quality Principle still relevant?
AI and analytics depend on trustworthy data; poor quality undermines outcomes and compliance.
Category: Relevancy
What’s a common mistake with the Quality Principle?
Defining metrics without business context, leading to optimizing the wrong indicators.
Category: Mistakes
What risks occur when ignoring the Quality Principle?
Bad decisions, model drift, regulatory breaches, and customer dissatisfaction may result.
Category: Risks
What standards support the Quality Principle?
ISO/IEC 25012 and the ISO 8000 series offer data quality models and guidance.
Category: Standards
How does the Quality Principle relate to eventual consistency?
Eventual consistency may tolerate temporary discrepancies; the principle ensures reconciliation and fit-for-purpose quality.
Category: Relation
Which tools implement the Quality Principle?
Great Expectations, Deequ, Dataplex Data Quality, and custom tests in pipelines.
Category: Tooling