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Master Data Management in Practice: Achieving True Customer MDM by Mark Allen, Dalton Cervo

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Data Quality Metrics

In Chapter 6, we briefly described the purpose of data quality metrics as part of a self-feeding process for continuous improvement. We also discussed establishing and creating a data quality baseline for better understanding the current state of the data and its proper business alignment and fitness for use.

This chapter expands that concept by defining means for creating a scalable and sustainable process in which data quality metrics become the central point for data quality assessment and consequently a critical source for data quality proactive initiatives.

Data quality metrics falls into two main categories: (1) monitoring and (2) scorecards or dashboards. Monitors are used to detect violations that usually require immediate corrective actions. Scorecards or dashboards allow for numbers to be associated with the quality of the data and are more snapshot-in-time reports as opposed to real-time triggers. Notice that results of monitor reports can be included in the overall calculation of scorecards and dashboards, as well.

Data quality metrics need to be aligned with business key performance indicators (KPI) throughout the company. Each LOB will have a list of KPIs for its particular needs, which need to be collected by the data quality forum and properly implemented into a set of monitors and/or scorecards.

Associating KPIs to metrics is critical for two reasons:

1. As discussed earlier, all data quality activities need to serve a business purpose, and ...

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