In this article I explore some of the key concepts of data quality management and how to build a strategy for continuous improvement. I won’t be covering every possible scenario, process, method or problem; only those that are common across most industries and those that have proved useful on my own personal journey.
Hopefully, we already agree that good data quality is an essential part of business intelligence and a foundation on which you build your systems, processes and reporting. Unfortunately, it’s often an afterthought and/or under-resourced. If you’re lucky enough to have multiple database technologies, mixed hosted systems, personal and/or sensitive data, a geographically wide user base or perhaps just a lack of resources and time to devote to this – you’re not alone! Many businesses struggle to manage and improve their data quality.
One possible solution is having a good Data Quality Strategy. This is a combination of similar concepts like data governance, master data management, balanced scorecards and a variety of other processes. Whatever method or name we use, the goals are basically the same; to identify, manage and continuously improve data.
Key elements that your strategy should include are:
Understanding your data
- Field identification, documentation and classification
- Data completeness & data quality scoring
Data ownership
- Establishing KPIs
- Data stewardship
- Master data management
Continuous improvement
- Root cause analysis
- Data enrichment
- Data quality committee
As it is unlikely you will ever attain 100% perfect data you are, in reality, working in a permanent “find – fix – improve” loop. To maximise the potential value locked in your data (and keep it), a simplified loop looks something like this:
Those five simple elements will touch almost every part of your business!…
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