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Procedures ensuring products or data meet defined quality standards through monitoring, inspection, and corrective actions.
DESCRIPTION
Quality control encompasses operational techniques to verify outputs meet requirements through inspection, measurement, and statistical monitoring. In data contexts, it includes profiling, rule checks, and remediation workflows. Control charts and SPC detect process variation for corrective action. QC complements quality assurance, which focuses on process design. Effective QC reduces defects, increases reliability, and improves stakeholder trust.
ALTERNATIVE DEFINITIONS
American Society for Quality
Quality control (QC) is a process by which entities review the quality of all factors involved in production.
Broad definition encompassing factors influencing production quality.
Encyclopaedia Britannica
Quality control, in industry, is a system of maintaining standards in manufactured products by testing a sample of the output against the specification.
Emphasizes sampling and testing against specifications.
NIST
Quality control involves the operational techniques and activities that sustain the product quality to meet requirements.
Focuses on operational techniques sustaining quality.
RELATED TERMS
Statistical Process Control Six Sigma Control Chart ISO 9001 Data Quality Inspection Root Cause Analysis
COMPETING TERMS
KNOWLEDGE AREAS
Data Quality Data Management Testing & Validation Data Governance
TARGET AUDIENCE
Data Steward Data Governance Manager Data Engineer Data Management Consultant Data Analyst
EXTERNAL SOURCES
- Wikipedia - Quality control
- ASQ - QC resource and definitions
- NIST - Process monitoring and control
- ISO 9001 - Quality management standard
TOP QUESTIONS
How does Quality Control apply to data?
QC for data uses profiling, validation rules, and monitoring to ensure completeness, accuracy, and timeliness with remediation workflows.
Category: Overview
What is the difference between Quality Control and Quality Assurance?
QC verifies outputs, while QA designs processes to prevent defects. Both are complementary.
Category: Misunderstanding
What tools support Quality Control in data pipelines?
Great Expectations, dbt tests, and catalog-integrated quality checks are common.
Category: Platform
What are common QC mistakes?
Relying on spot checks, ignoring drift, and lacking root cause analysis leads to persistent quality issues.
Category: Mistakes
What risks arise from insufficient Quality Control?
Defective outputs cause bad decisions, regulatory penalties, and reputational damage.
Category: Risk
How to implement effective Quality Control?
Define quality dimensions, implement automated checks, use SPC charts, and establish triage and remediation processes.
Category: How-To
Why is Quality Control still relevant?
Increasing data volumes and regulatory scrutiny demand robust controls to maintain trust and compliance.
Category: Relevancy
What hot topic relates to Quality Control?
Data contracts and observability integrating QC checks into runtime monitoring are growing trends.
Category: Topic
Where to start learning Quality Control?
Review NIST handbooks and ASQ materials, then apply QC frameworks in your data platform.
Category: Getting-Started