Presenter: Michael Scofield, M.B.A.
Topic: Data Quality in Covid-19 Metrics. A Case Study.
Date/Time: December 15, 2020 6p – 8p
Registration Link / Meeting Instructions:
Michael Scofield, M.B.A. is an Assistant Clinical Professor at Loma Linda University. He is a frequent speaker and author in topics of data management, data quality, data visualization, and data warehousing. He has spoken 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 does guest lectures at several universities.
His career experience includes some time with a CPA firm, developing 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 interest currently is 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.
He will be speaking about “Data Quality in Covid-19 Metrics.” There is often a long path between the reality which raw data describes, and decision-maker understanding of what is going on (“big picture”). This “data-to-understanding supply chain” has many steps, with potential for lapses in the quality of data and information. This gap (with threats to quality) is particularly evident in the flow of Covid-19 data being passed around units of government in the U.S. The state and county totals sometimes suffer from a variety of definitions, inconsistent processes, and various causes of delay in reporting. This slide presentation provides simple explanations of what the virus is, how it replicates, the kinds of tests (antigen vs. antibody), and other issues of false negatives, comorbidity, positivity, and sampling techniques.
Then, there are the vaccines. Designing and testing a vaccine under “Warp speed” has numerous dangers. We will look at efficacy as well as measures of safety. The distribution to the public has numerous potential points of failure. Expectations of efficacy may not be realistic; we will show charts to explain how it works. We shall also consider other new metrics which would be useful to understand this pandemic. We shall also touch on best practices (or ineffective techniques) in data visualization with some excellent examples (of both) from government sources.