Good data quality is critical to processing high-velocity, high-volume data, including machine learning technologies that are highly susceptible to poor data quality. Identifying data quality hot spots provides a systematic method for technical professionals to continually improve data quality.
- Business and IT Are Jointly Responsible for Data Quality
- Quality Data Is Data That Is "Fit for Purpose"
The Gartner Approach
The Guidance Framework
- Prework: Business Engagement
- Identify Maturity of Data Quality
- Capture Existing Data Quality Roles
- Collect Business KPIs and Known Data Quality KPIs
- Five Steps to Creating a Business Case for Data Quality (Optional)
- Step 1: Determine Data Quality Hot Spots
- Use Data Management Reference Architecture to Identify Hot Spots
- Step 2: Analyze Architecture and Create Data Quality Hot-Spot Rankings
- Creating Data Quality Hot-Spot Rankings
- Data Quality Hot-Spot Ranking Guidelines
- How to Use the Data Quality Hot-Spot Rankings
- Step 3: Select a Data Quality Implementation
- Data Quality Implementation Methods
- Implementation Selection Guidance
- Step 4: Identify Proposed Data Quality Changes
- Step 5: Implement Data Quality Changes
- Step 6: Continual Audit and Repeat
- Follow-Up: Expand the Tools and Integrate Data Quality Onboarding
- Dedicated Data Quality Tools
- Data Quality as Part of Data Onboarding
Risks and Pitfalls
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