Data quality—the degree to which data is accurate, complete, timely, and fit for purpose—determines whether data assets deliver value. Poor quality data leads to bad decisions, operational failures, and regulatory problems.
This guide provides a framework for building strong data quality foundations.
Understanding Data Quality
Data Quality Dimensions
How quality is measured:
Accuracy: Data correctly represents reality.
Completeness: Required data is present.
Timeliness: Data is current enough.
Consistency: Data agrees across sources.
Validity: Data conforms to rules.
Uniqueness: No inappropriate duplication.
Business Impact
Why quality matters:
Decision quality: Accurate decisions require accurate data.
Operational efficiency: Bad data causes rework.
Customer experience: Errors affect customers.
Regulatory compliance: Requirements for data accuracy.
Analytics validity: Garbage in, garbage out.
Assessment Framework
Quality Assessment
Understanding current state:
Profiling: Analyzing data characteristics.
Rule validation: Checking against business rules.
Benchmarking: Comparing against expectations.
User feedback: Understanding user experience.
Quality Metrics
Measuring quality:
Dimension scores: Metrics per dimension.
Composite scores: Aggregate quality measures.
Trend analysis: Quality over time.
Threshold monitoring: Alerts for quality degradation.
Root Cause Analysis
Understanding quality problems:
Source analysis: Where errors originate.
Process analysis: How errors are introduced.
Pattern identification: Systematic vs. random errors.
Priority assessment: Impact-based prioritization.
Improvement Strategies
Prevention
Stopping problems before they occur:
Design for quality: Build quality into processes.
Validation at entry: Check data when created.
Standards enforcement: Apply rules consistently.
Training: Build capability.
Detection
Finding quality problems:
Automated monitoring: Continuous quality checking.
Exception reporting: Surfacing quality issues.
Audit processes: Periodic quality review.
User reporting: Capturing user-identified issues.
Correction
Fixing quality problems:
Data cleansing: Correcting existing data.
Enrichment: Filling gaps with external data.
Deduplication: Resolving duplicates.
Standardization: Normalizing formats.
Ongoing Management
Sustaining quality:
Continuous monitoring: Always checking.
Regular review: Periodic assessment.
Process improvement: Fixing root causes.
Governance integration: Quality in governance.
Operating Model
Roles and Responsibilities
Who does what:
Data owners: Accountable for quality.
Data stewards: Day-to-day quality management.
Technical teams: Quality tool operation.
Business users: Quality feedback.
Quality Processes
How quality is managed:
Assessment cycles: When quality is measured.
Issue management: How problems are addressed.
Reporting: How status is communicated.
Escalation: How serious issues are handled.
Technology Support
Tools for quality:
Profiling tools: Understanding data.
Quality rules engines: Automated validation.
Cleansing tools: Data correction.
Monitoring dashboards: Quality visibility.
Implementation Approach
Starting Point
Getting started:
Priority data: Start with critical data.
Baseline assessment: Understand current state.
Target setting: Define quality goals.
Quick wins: Address visible problems.
Building Capability
Growing quality program:
Tool deployment: Implementing technology.
Process establishment: Building routines.
Skill development: Training teams.
Governance integration: Connecting to governance.
Key Takeaways
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Prevention beats correction: Build quality in.
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Measurement enables management: Can't improve what you don't measure.
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Root cause matters: Fix sources, not just symptoms.
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Ownership is essential: Someone must be accountable.
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Continuous effort required: Quality isn't one-time.
Frequently Asked Questions
Where should we start? Critical business data. High-impact, high-visibility domains.
What tools should we use? Profiling and monitoring tools. Talend, Informatica, Great Expectations, others.
How do we get business engagement? Show impact of quality problems. Connect to their pain.
What's a realistic quality target? Depends on data use. 99%+ for critical operations; lower for analytics may be acceptable.
How do we sustain quality improvement? Governance, continuous monitoring, root cause focus.
How do we measure ROI? Rework reduction, decision improvement, compliance costs, customer impact.