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                               The SouthWest Ohio Chapter (SWOC) 

of the

                                       Data Management Association Int'l


“Good Data, Bad Info: What went wrong?”

  • 11-Apr-2018
  • 5:00 PM - 8:30 PM
  • 4350 Aicholtz Road, Cincinnati, Ohio 45245


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“Good Data, Bad Info: What went wrong?

                        Michael Scofield, M.B.A. Assistant Clinical Professor,

                        Loma Linda University, Loma Linda, CA

                                              Visit his page at   Michael Scofield

                                       4350 Aicholtz Road, Cincinnati, Ohio 45245


           5:00 pm – Arrival of attendees
             5:15 pm – Dinner & Networking
             5:45 pm – 8:15 pm Presentation
                            Q & A Discussions may continue until 8:30 pm

          $25 per Attendee includes dinner

          After 2 quarterly meetings at $25 each, attendee is a Chapter member

          Chapter Members attend all subsequent events free for rest of year



Producing decision-able information from lots of raw data often requires complex processes which must be carefully designed and architected.
This presentation looks at the gap between raw data and decision-able information. Most of the chatter about data quality focuses upon raw, granular data describing discrete events and entities inside the enterprise and around it. However, a single fact (or “cell”) of data without context is not very meaningful. Hence, we assemble many facts, aggregate them, calculate ratios and trends, often in the context of a data warehouse. It is information (data assembled and placed in proper context) which is more meaningful to the decision-maker.
We will look at numerous examples between raw data and derived data (“information”) which show us trends, patterns, etc. Examples come from astronomy, military intelligence, and criminology. For example, in image analysis (in the intelligence community) we see a succession of “derived” data (sometimes facts, occasionally assumptions) as we move from pixels to clues about the enemy’s strategic intent.
As an enterprise is inundated with more data (raw, intermediate, and advanced information) the designing and population of good metadata becomes even more important. We will look at nine distinct kinds of metadata. Not all kinds are of equal importance, but we will show the purpose and value of each. The larger the organization, and the more diverse the “institutional memory”, the more important it is to properly document the data asset for broader exploitation of its value. Ignorance of institutional memory can be disastrous.
We will also look at how incorrect conclusions may be drawn because of mis-handling of the process to derive “higher level” information from the raw data.

Speaker’s Profile

Professor Scofield is a frequent speaker & author in topics of data management, data quality, data visualization, & data warehousing. He has spoken in: over 27 states, Canada, Australia, the U.K.
Audiences have included 24 DAMA chapters, 5 TDWI chapters, 14 ASQ (American Society for Quality) chapters, & many accounting professional organizations. He also guest lectures at several universities.
Career experience includes government, manufacturing, finance, & software development. Now semi-retired, he still does pro bono data mining & data quality analysis for non-profit organizations. Interests include: data visualization, data quality assessment, using graphic techniques to reveal business & economic behavior, data management for the “internet of things”, publishing humor in the Los Angeles Times, and other journals.

SWOC Seminar Development Team

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