Standardizing Your Approach to Monitoring the Quality of Data

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By David Loshin

In my last post, I suggested three techniques for maturing your organizational approach to data quality management. The first recommendation was defining processes for evaluating errors when they are identified. These types of processes actually involve a few key techniques:

1) An approach to specifying data validity rules that can be
used to determine whether a data instance or record has an error.

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Case Study: Northern Ontario School of Medicine (NOSM)

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In this case study, the Northern Ontario School of Medicine (NOSM) was able to cleanse and incorporate their data from about 30 distinct source systems, by integrating Melissa Data’s Total Data Quality Integration Toolkit (TDQ-IT). TDQ-IT works within the SSIS data flow to deliver a wide range of data integration, transformation, and cleansing functionality including: profiling, parsing, cleansing, matching, and monitoring.Read More