Published: 29 January 2020
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.
Included in Full Research
- 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