Poor data quality is like dirt on the windshield...
..........you may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything.
Ever since there have been databases and applications, there have been data quality problems. Unfortunately all those problems are not created equal and neither are the solutions that address them. Some of the largest differences are driven by the data type, or domain, of the data in question. The most common data domains in data quality are customer (or more generally, party data including suppliers, employees, etc.) and product data. Legislation such as Data Privacy is driving companies to take the subject of Data Quality more serious than ever. This is now a board level subject !!
https://ec.europa.eu/justice/data-protection/
How should you approach Data Quality issues?
Work with a vendor that makes use of a good methodology for addressing data quality issues. Any data quality project should start with the identification and comprehensive understanding of your data quality issues. Uncovering exactly where your data quality issues are is the basis for addressing these issues and a foundation for building data quality rules for defect remediation and prevention.
What should you look for in a solution?
A complete solution should support a methodology as seen here above. It should offer a set of core capabilities that include:
- Profiling, Auditing and Dashboards
- Parsing and Standardization
- Match and Merge
- Case Management
- Address Verification
- Product Data Capabilities
And finally, Data Quality is not a ‘one-off’ project, it is ongoing and it needs to be addressed and managed this way. In the absence of active en effective data quality measures, quality will deteriorate. Furthermore, legislation is driving the requirement for a complete Data Quality & Data Governance strategy.