Big Data and How It Is Being Used to Transform the Pharmaceutical Business Model

Transcription

1 Big Data and How It Is Being Used to Transform the Pharmaceutical Business Model A Special Extended Symposium for the 2015 National Biotechnology Conference Brian R Moyer and Atul Butte, Co-Moderators

2 The AAPS NBC and NBC Program Committee Welcomes Everyone to a New Topic of High Value to our Biotechnology Conference: The Role of BIG DATA in Biotechnology

3 WHAT IS BIG DATA? THIS EXAMPLE MAY HELP.. Ants are very simple creatures. They can recognize a dozen or so pheromones and can sense where these scents are more intense. They also can tell the difference between meeting two ants in a minute and 200 ants. That, however, is about the extent of their individual communication abilities. But if we observe 10,000 of them in a colony, we see a "swarm logic" emerge. The colony is continually adjusting the number of ants foraging for food, based on the number of mouths to feed, how much food is stored already in the nest, how much food is available in the vicinity, and whether other colonies are out there competing for resources. Yet, no ant understands any of this. Today, let s explore as ants the Big Data world of Biotech/Pharma

4 The DEFINITION By definition, Big Data refers to electronic health data sets so large and complex that they are difficult (or impossible) to manage with traditional software and/or hardware; nor can they be easily managed with traditional or common data management tools and methods (1). Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed (1) Big Data engages electronic datasets so large and complex that they are difficult (or impossible) to manage with traditional software and hardware. Big Data is overwhelming not only because of its volume, but also because of the diversity of data types and the speed in which it must be managed. Volume, Velocity, and Variety often referred to as the three V s of Big Data capture the true meaning of Big Data. Abstracted from a Review: Wullianallur Raghupathi and Viju Raghupathi Big data analytics in healthcare: promise and potential In, Health Information Science and Systems 2014, 2: Frost & Sullivan: Drowning in Big Data? Reducing Information Technology Complexities and Costs for Healthcare Organizations.

5 BIG DATA AND WHAT IT MEANS TO BIOTECHNOLOGY Recent figures estimate the number of Big Data jobs will grow to 4.4 million, with 1.9 million of these jobs to locate in the United States. Jobs in Biotech: Many of these jobs will be directly related to biotechnology platform development and applications off those platforms. New Structures for Everyday Data: Big Data will require intricate algorithms and the crafting stories out of the massive amounts of data that today s pharmaceutical companies are generating every day. Disjoined Data - Connect the Dots : Data scientists will be challenged to make sense of enormous data arrays and will be challenged to design uniquely structured data using cross-platform experiences

6 Continued BIG DATA AND WHAT IT MEANS TO BIOTECHNOLOGY Optimize the Data: Optimizing platform outcomes will be done to satisfy customer/investor interests, foster new creative product designs, and refine models of disease Make Sense of the Data: Interpretations of statistical analyses will generate new pharmaceutical ventures and products, and ultimately create solutions to many of the deadly diseases and health issues we still face.

7 The Big Data Symposia Agenda 8 AM 8:10 Welcome to the Workshop Brian R. Moyer, NBC Program Committee/ 2016 NBC Chair-Elect 8:10-8:35 Keynote Introduction to the Role of Big Data in Pharma: Tamara Dull, SAS Panel 1: Big Data Platforms (8:40 to 10:45 AM) 8:40 9:10 Personal Genomics: Mike Snyder, Stanford Univ. 9:10 9:40 Public Data: Atul Butte, UCSF 9:40 10:10 High Performance Databases: Enakshi Singh, SAP 10:10 10:40 Cloud Computing for Biomedicine: Ketan Paranjape, Intel 10:40 10:50 BREAK Panel 2: Big Data Applications (10:50 1 PM) 10:50 11:20 Microbiome: David Hanzel, Metabiomics 11:20 11:50 Pharmacogenomics: Gunaretnam Rajagopal, Janssen 11:50-12:20 Circulating DNA: Iwijn De Vlaminck, Cornell Univ. 12:20-12:50 Cloud Genomics: David Shaywitz, DNANexus 12:50-1:00 Closing Remarks: Atul Butte, UCSF and Brian Moyer, 2016 Chair

8 Keynote Introduction to the Role of Big Data in Pharma: Tamara Dull, SAS

9 An applied conceptual architecture of big data analytics. From: Raghupathi and Raghgupathi, 2014