Data Quality

The results of Data And Analytics (DAA) is only as good as the data put into it. The saying "Garbage In Garbage Out (GIGO)" is all too true. Data Quality Management is the discipline of ensuring that data is fit for use by the enterprise.

DQM includes obtaining requirements and rules that specify the dimensions of quality required such as: accuracy, completeness, timeliness, and allowed values. It is part of a good Data Governance program. The following figure shows a step by step approach to improving data quality. DQM is an iterative process - expect to repeat this cycle multiple times to improve the quality of data.

1. Define Goals, Rules and Standards: Set the overall approach to DQM.
2. Identify Systems and Data Elements: Determine which systems and data elements should be in scope - start with the highest priority first - avoid "boiling the ocean".
3. Identify Data Quality Issues: Find and prioritize problems to be solved - such as: missing data, inaccurate data, poorly formatted data and mismatched data.
4. Determine Remediation Approach: Decide on the methods to be used to correct existing data and to prevent future DQ issues.
5. Remediate Data: Carry out the remediation approach.
6. Monitor and Control: Make sure that DQ is maintained in a sustainable, on going basis. Loop back to earlier steps to continually improve DQ based on enterprise priorities.