DQ Analysts will need to sort out and prioritize findings. The goal of an initial Data Quality Assessment is to learn about the Data in order to define an Actionable Plan for improvement. It is usually best to start with a small, focused effort – a basic proof of concept – to demonstrate how the improvement process works. Steps include:
- Define the Goals of the Assessment; these will drive the work
- Identify the Data to be Assessed; focus should be on a small data set, even a single data element, or a specific data quality problem
- Identify Uses of the Data and the Consumers of the Data
- Identify Known Risks with the Data to be Assessed, including the potential impact of data issues on organizational processes
- Inspect the Data based on Known and Proposed Rules
- Document Levels of Non-Conformance and Types of Issues
- Perform Additional, in-depth Analysis based on Initial Findings in order to
- Quantify Findings
- Prioritize Issues based on Business Impact
- Develop Hypotheses about Root Causes of Data Issues
- Meet with Data Stewards, SMEs, and Data Consumers to Confirm Issues and Priorities
- Use Findings as a Foundation for Planning
- Remediation of Issues, ideally at their Root Causes
- Controls and Process Improvements to Prevent Issues from Recurring
- Ongoing Controls and Reporting
Framework and Methodology
Data Quality Priorities must align with Business Strategy. Framework should include methods to:
- Understand and prioritize business needs
- Identify the data critical to meeting business needs
- Define business rules and data quality standards based on business requirements
- Assess data against expectations
- Share findings and get feedback from stakeholders
Framework should also account for how to organize for data quality and how to leverage data quality tools.
