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IT Analyst Shares Insights and Best Practice Perspectives on Data Quality

Published
  • October 28 2004, 1:00am EDT
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At Ascential World 2004, the global data integration conference of Ascential Software Corporation, Ted Friedman, principal analyst, business intelligence infrastructure at Gartner, delivered a keynote address on data quality as the foundation for business success. He explored the impact of data quality on an organization, strategies for measuring and understanding data quality in business terms and best practice approaches to address data quality challenges.

Friedman noted that large enterprises now view data quality as a strategic issue. Their focus has expanded from the quality of customer data in CRM and ERP applications. It now also includes data from other business applications, including product lifecycle management (PLM) and supplier relationship management as well as data from initiatives such as RFID. He identified data quality as a shared business and IT issue primarily driven by government compliance requirements, including Sarbanes-Oxley, enterprise information management initiatives, such as PLM, and the need for more effective integration of business applications across the enterprise.

Friedman told the audience that the right blend of people (both business and IT), best practice processes, and technology is vital in order to effectively measure and anchor the business case behind data quality initiatives. And he counseled IT executives to present data quality issues to senior management in business terms, such as lost productivity and revenue, unnecessary expenses generated and customer churn.

In addressing best practices, Friedman called upon the audience to view data quality as a closed-loop process to measure data quality improvement and, potentially, data quality decay over time. He said business and IT must be responsible for mastery of their data and jointly decide how to protect it. This process includes the right use of technology for data profiling, rule definition and validation, data cleansing and continuous monitoring.

 

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