This presentation provides guidance to organizations considering or preparing for data quality initiatives. We will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality can be engineered provides a useful framework in which to develop an organizational approach. This in turn will allow organizations to more quickly identify data problems caused by structural issues versus practice-oriented defects. Participants will also learn the importance of practicing data quality engineering quantification. Learning Objectives: What is data quality and why is it important? Data quality concepts & activities Data Quality Management (DQM) cycle Data quality awareness & requirements Data quality dimensions Case Study: Data Quality Golden Rules Data quality tools & frameworks Guiding principles & best practices

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Training Executive Benefits Hot topics in Data Management

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