Bill Shannon, PhD, MBA BioRankings, LLC June 24, 2014 Center for Emerging Technology

Transcription

1 Bill Shannon, PhD, MBA BioRankings, LLC June 24, 2014 Center for Emerging Technology

2 Discussing data Autism and bacteria Price optimization Open Discussion about YOUR data

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4 Autism spectrum disorder (ASD) is a challenging neurological dysfunction of unclear etiology. Recent data suggest the possibility that intestinal microbes and the inflammatory response of infants to these microbes initiate a neurological cascade resulting in autism. If these initiating processes can be identified, then there will be major novel opportunities to halt this progression.

5 Premature Babies in Database with DNA/Blood Stored (N = 977) 777 Eligible SRS (~$1,000) ADOS-2 (~$100,000)

6 Expect high correlation Association between autism screening and preterm birth gut bacteria

7 Expect high correlation Expect high correlation Association between autism screening and preterm birth gut bacteria Association between autism clinical test and preterm birth gut bacteria

8 Expect high correlation Expect high correlation Expect very high correlation Association between autism screening and preterm birth gut bacteria Association between autism clinical test and preterm birth gut bacteria Why spend $100,000 on expensive ADOS-2 when same information found with SRS

9 Drawing simple scatter plots and talking about the data we saw that SRS + ADOS-2 was redundant Reduced ADOS-2 from 100 to 30 subjects Saved $70,000 in this experiment Still answered question about bacteria and autism

10 Retail struggles with pricing SKUs to maximize profits Price-demand curve data is based on historical sales where conditions might be very different from today s environment Data and data analytics can solve this problem streaming point-of-purchase sales data digital pricing labels to allow price changes Experiments to test hypotheses about optimal prices

11 Figure 1: Theortical Demand Curve Price Low High p** p* Low q** q* High Quantity

12 Our solution is based on the application of scientific approaches to Big Data analytics: Access to streaming point-of-sales data in near-real time (i.e., daily) to measure current demand based on price Statistical modeling to fit demand curves for multiple SKUs simultaneously Use of experimental design to propose price changes of SKUs within different stores, regions, seasons, etc., on the fly to test hypotheses about optimal pricing The ability to change prices rapidly using digital price labels to gather experimental data in order to formally test the hypotheses

13 Figure 2: Variable Demand Curves for a Single SKU Price Low High Urban Stores Rural Stores Low High Quantity

14 Point-of-sale data capture in real time by IT vendor Data capture

15 Rethink data access and how to use it to open new revenue generating opportunities Think about the data and data analytics and not just IT, biology, economics, strategy, etc. Draw pictures first!