There is a significant distance (and many steps) between raw, granular data gathered in business and industrial processes, and information which is meaningful to human decision-makers. The pathway from high quality data to high quality expression of information has many potential points of failure. Some warning signs of business problems may be detected in the granular data, however the first derivative (moving towards “information”) may reveal more meaningful insight. Also, data quality testing of raw, granular data may not be sufficient to evaluate the quality and efficacy of summary information or strategic information.
Decision-makers often function in a cognitive bubble. The culture inside that bubble includes paradigms and vocabulary which the knowledge worker must understand when packaging strategic information to support executive decision-making.
Michael Scofield, M.B.A. is an Assistant Professor at Loma Linda University in Southern California. He is a frequent speaker and author in topics of data management, data quality, data visualization, and data warehousing. He has spoken over 365 times to professional audiences in over 27 states, Canada, Australia, and the U.K.
Audiences have included 24 DAMA chapters, 5 TDWI chapters, 14 ASQ sections and many accounting professional organizations. He also guest-lectures at several universities.
His career experience includes auditing with a CPA firm, designing an accounting and general ledger system for a major California bank, as well as experience in government, manufacturing, finance, and software development. Now semi-retired, he still does pro bono data mining and data quality analysis for non-profit organizations. His greatest interests currently are data visualization, data quality assessment, and using graphic techniques to reveal business and economic behavior. He also has humor published in the Los Angeles Times, and other journals.