In order to improve the Data Quality, DQ Analysts must have the clear picture about Current State of Data Quality as well as Organizational Readiness for Data Quality.
Getting a comprehensive picture of the current state of Data Quality in an organization requires approaching the question from different perspectives:
- An understanding of business strategy and goals
- Interviews with stakeholders to identify pain points, risks, and business drivers
- Direct assessment of data, through profiling and other form of analysis
- Documentation of data dependencies in business processes
- Documentation of technical architecture and systems support for business processes
Ask a set of questions to understand current state and assess organizational readiness for data quality improvement:
- What do Stakeholders mean by ‘High Quality Data’?
- What is the Impact of Low Quality Data on Business Operations and Strategy?
- How will Higher Quality Data Enable Business Strategy?
- What Priorities Drive the Need for Data Quality Improvement?
- What is the Tolerance for Poor Quality Data?
- What Governance is in Place to Support Data Duality Improvement?
- What Additional Governance Structures will be Needed?
When issues are found, determine ROI of fixes based on:
- The criticality (importance ranking) of the data affected
- Amount of data affected
- The age of the data
- Number and type of business processes impacted by the issue
- Number of customers, clients, vendors, or employees impacted by the issue
- Risks associated with the issue
- Costs of remediating root causes
- Costs of potential work-around
