Gartner Research

Build a Data Quality Operating Model to Drive Data Quality Assurance

Summary

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.

Published: 29 January 2020

ID: G00466307

Analyst(s): Melody Chien Saul Judah Ankush Jain

Table Of Contents
  • Key Challenges

Introduction

Analysis

  • 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

Gartner Recommended Reading

Already a Gartner client?

Become a Client

This research is reserved for paying clients. Speak with a Gartner specialist to learn how you can access this research as a client, plus insights, advice and tools to help you achieve your goals.

Contact Information

All fields are required.

By clicking the "Submit" button, you are agreeing to the Gartner Terms of Use and Privacy Policy.

©2021 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. This publication may not be reproduced or distributed in any form without Gartner’s prior written permission. It consists of the opinions of Gartner’s research organization, which should not be construed as statements of fact. While the information contained in this publication has been obtained from sources believed to be reliable, Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartner research may address legal and financial issues, Gartner does not provide legal or investment advice and its research should not be construed or used as such. Your access and use of this publication are governed by Gartner’s Usage Policy. Gartner prides itself on its reputation for independence and objectivity. Its research is produced independently by its research organization without input or influence from any third party. For further information, see Guiding Principles on Independence and Objectivity.