Data quality trends, with expert Larry English

Data quality tools and processes have changed dramatically, so keep up with the latest data quality trends. Renowned expert Larry English provides timely advice for your data quality strategy.

Poor data quality can cost companies 15% to 25% (or more) of their operating budget, according to internationally-recognized expert Larry English, founder of Brentwood, Tenn.-based Information Impact International and developer of the Total Information Quality Management (TIQM) methodology. But the tools and methods that companies use to reduce costs associated with bad data are rapidly evolving.

SearchDataManagement caught up with English to learn how data quality processes are changing and how companies can effectively solve quality problems today. Both technology and business professionals can benefit from learning about current data quality trends in this 35-minute podcast.

  • Learn why English strongly prefers the term "information quality" over "data quality" -- and how this could make or break a business case
  • Hear how information quality processes are evolving and what the future of the discipline may bring
  • Get timely tips and guidance for forming (or updating) an enterprise information quality strategy
  • Listen to English's expert advice about how to evaluate information quality technology tools

 

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Data quality trends, with expert Larry English

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About the featured expert
Larry English is an internationally-recognized speaker, educator, author and consultant in knowledge management and information quality improvement. His company, Information Impact International, provides consulting and education for information quality and a variety of related disciplines. Larry developed the Total Information Quality Management (TIQM) methodology applying Kaizen(r) quality principles to information quality management. He chairs Information Quality Conferences around the world, and conceived of and co-founded the International Association for Information and Data Quality (IAIDQ).


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This was first published in January 2007

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