Gartner Research

Overcome Barriers to Improving Data Quality: Lessons From the EMEA MDM Summit Workshop

Published: 29 March 2011

ID: G00211687

Analyst(s): Roxane Edjlali, Ted Friedman


At a workshop at Gartner's master data management summit in London, participants discussed the most common challenges with data quality programs and brainstormed ways to address these challenges.

Table Of Contents
  • Overview

What You Need to Know


  • Barrier: Lack of Solid Business Case
    • Solution: Measure Data Quality Business Value From a Foundation of Metrics
  • Barrier: Lack of Executive Sponsorship and Business-Side Buy-In
    • Solution: Demonstrate Business Impact of Poor Data Quality
  • Barrier: No Formal Definition/Understanding of Data Quality
    • Solution: Set Up a Data Governance Group With Contributions From Both IT and the Business to Formalize and Define What Data Quality Means
  • Barrier: Not Being Proactive in Approach to Data Quality Issues
    • Solution: Think of Data Quality Improvement As a Program Rather Than a Project
  • Barrier: Who Has Accountability?
    • Solution: Assign People to Dedicated Data Steward Roles and Measure Their Performance on an Ongoing Basis
  • Barrier: Lack of a Single Source of Truth; Many Information Silos Create Inconsistencies
    • Solution: Establish Incremental Approach Based on Enterprise Priorities
  • Tactical Guidelines

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