What Covid-19 Data Can Teach Us About Data Quality  
Presented by Michael Scofield,
Assistant Professor, Loma Linda University

High quality raw data is of no use if the processes which take it through to decision-able information are flawed.  Good raw data, bad information?   It happens.  The processes of filtering, aggregation, summarization, and others often are based on bad assumptions.  We will use an 8-step model to understand where the flow can go wrong.  Of importance is the profound difference between expression of summarized data, and the understanding in the mind of the audience or decision-maker.  Good graphics often help understanding.  But a common vocabulary and understanding of the weaknesses of the data (thank you W. Edwards Deming!) certainly helps. 

Central to this presentation is a review of the current metrics commonly used to understand the current pandemic.  They reveal many issues such as sampling techniques, volatility and variability in aggregations, and the importance or ratios and comparisons to peer experiences.

Key Point: Bridging the Gap Between Raw Data & Information for Decision-Making.

Speaker: Michael Scofield

Michael Scofield is an Assistant Professor at Loma Linda University. He has spent most of his career in data management for various organization, frequently specializing in data quality. He has given over 350 lectures world-wide to professional groups on data quality assessment and data visualization. He has spoken to the Orange County Section nine times, and to numerous other ASQ sections around the West.

Register here: https://tinyurl.com/IIBA-OC-COVID-19