Yesterday, my post touched on the importance of data quality within the Business Intelligence architecture. I wanted to bring to your attention a book that recently came out regarding data quality assessments. Data Quality Assessment (a descriptive title, huh?) is an extremely interesting book that deserves your attention. The book lays out a rigourous program for improving enterprise data quality.

Early on in the book, Arkady enumerates 13 categories of processes that can lead to data quality issues:

1. Processes Bringing Data From The Outside
   • Initial Data Conversion
   • System Consolidations
   • Manual Data Entry
   • Batch Feeds
   • Real-Time Interfaces
2. Processes Causing Data Decay
   • Changes Not Captured
   • System Upgrades
   • New Data Uses
   • Loss of Expertise
   • Process Automation
3. Processes Changing Data From Within
   • Data Processing
   • Data Cleansing
   • Data Purging

The second part of the book turns to data quality rules: attribute domain constraints, relational integrity rules, historical data, state dependent objects, and attribute dependency rules. Lastly, the third part discusses the data quality assessment and implementation of rules to improve data quality.

This is a very fine book. Click here to see it on Amazon.com.

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