Gartner Research

Build a Data Quality Operating Model to Drive Data Quality Assurance

Published: 29 January 2020


Resolving data quality issues requires a multifaceted approach that involves people, governance, processes and technologies as key factors. Data and analytics leaders should build a comprehensive data quality operating model including these factors to foster data quality assurance.

Included in Full Research

  • Key Challenges
  • Identify Capabilities and Deficits That Affect the Success of Your Enterprise’s Data Quality Initiatives
  • Work With Stakeholders to Build a Data Quality Operating Model to Augment Your Data Quality Practices
    • Scope: Define the Scope of Your Data Quality Program With Clear Business Outcomes in Mind
    • Culture and Governance: Design and Operate Business-Driven Data Governance Focusing on Targeted Data Quality Improvements
    • Process and Practices: Embed Data Quality Tasks in Business Processes and Monitor the Progress Over Time
    • Organization and People: Establish Data-Quality-Related Roles That Are Critical to the Success of Data Quality Initiatives
    • Technology and Patterns: Use Data Quality Tools to Automate Manual Data Quality Tasks
    • Metrics: Identify Concrete and Measurable Data Quality Metrics, Link Them to D&A Outcomes and Monitor Progress

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