Gartner Research

Agile Data Quality to Maximize Your Business Results

Published: 03 October 2017

ID: G00327537

Analyst(s): Thornton Craig


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.

Table Of Contents

Problem Statement

  • 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
    • Identifying Changes
  • Step 5: Implement Data Quality Changes
    • Implementing 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

  • Related Guidance

Gartner Recommended Reading

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