Data Quality Assessment

  • 4h 56m
  • Arkady Maydanchik
  • Technics Publications
  • 2007

Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies.

Quality data is the key to and advancement, whether it's from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality doesn't have to be such a mammoth task.

Data Quality Assessment is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors - the first step in any data quality program. Master techniques in:

  • Data profiling and gathering meta data
  • Identifying, designing, and implementing data quality rules
  • Organizing rule and error catalogues
  • Ensuring accuracy and completeness of the data quality assessment
  • Constructing the dimensional data quality scorecard
  • Executing a recurrent data quality assessment

In this Book

  • Causes of Data Quality Problems
  • Data Quality Program Overview
  • Data Quality Assessment Overview
  • Attribute Domain Constraints
  • Relational Integrity Rules
  • Rules for Historical Data
  • Rules for State-Dependent Objects
  • Attribute Dependency Rules
  • Implementing Data Quality Rules
  • Fine-Tuning Data Quality Rules
  • Cataloguing Errors
  • Measuring Data Quality Scores
  • Data Quality Meta Data Warehouse
  • Recurrent Data Quality Assessment