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How to Measure and Monitor the Quality of Master Data


We’ve all heard about the importance of data quality in our IT systems and how the data that flows through our applications is the fuel of the business processes. Yet, surprisingly few organizations have a structured approach to measuring the quality of their data. Some may have a few custom reports or manually compiled Excel sheets that show a few aspects of data quality, but if information is truly an enterprise asset, shouldn’t we be measuring and monitoring it like we do with all the other assets of the organization? Like most other things, data quality can only be managed properly if it is measured and monitored.
The main reasons for implementing a data quality monitoring concept are to ensure that you identify:
  • Trends in data quality,
  • Data quality issues before they impact critical business processes and
  • Areas where process improvements are needed.
  • A structured and methodological approach to measuring and monitoring the quality of data should be part of a larger master data management or information management strategy.

The Dimensions of Data Quality

Looking at the offerings of many data profiling and data quality tool vendors, one could be led to think that data quality is just about checking if all fields have a value and if it’s valid compared to the domain that is defined for that specific field. While these are certainly relevant aspects of data quality, they only represent two out of the six common dimensions of data quality.
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When defining a data quality measuring concept, you should aim to focus on the dimensions that are meaningful and relevant for the business without spending too many resources, i.e., you need to justify the business relevance of what you measure. On the other hand, measuring all the different dimensions of data quality gives you the most complete picture. For instance, what is it worth to have a valid address for a customer if it is not correct because the customer has moved?
A challenge that organizations face as they attempt to define data quality key performance indicators is that completeness, validity and integrity may be relatively easy to measure, but measuring consistency, accuracy and timeliness is a whole other story. In general, we would argue that the relationship illustrated in Figure 2 exists between the difficulty of measurement and the business impact.
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Because accuracy, timeliness and consistency are the more complex dimensions to measure, here are a few hints on how to address them.


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