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J mc callumbig datapsp2013

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Preso on relevance of big data analytics to scholarly publishers, given at annual AAP/PSP conference. Focuses on the "product side" of big data and how advances in new models for evaluating medical evidence will affect medical publishers and offers recommendations on how to prepare for new developments in data-driven evidence-based medicine.

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J mc callumbig datapsp2013

  1. 1. BIG DATA: HELPING SCHOLARLY PUBLISHERS CUT THROUGH THE HYPE Janice McCallum Health Content Advisors Association of American Publishers 2013 PSP Annual Conference, Washington, DC February 6-8, 2013
  2. 2. Focus for this talk Is Big Data Overhyped? Let’s start with some definitions Relevance to scholarly publishers What should you be doing to profit from--and avoid being disrupted by--Big Data upstarts?
  3. 3. IS BIG DATA OVERHYPED?
  4. 4. Need I say more?
  5. 5. Other Headline Grabbers from Retail and Financial Services Analyzing customer behavior to predict purchasing or payment patterns: • Target recognizing likely pregnancy of shopper • Credit card companies knowing when you’re likely to be late with payments • Gathering real-time behavior data (EyeSee mannequins)
  6. 6. There is More to Big Data than Watson…Is It All Hype? Comprehensive List of Big Data Statistics “Comprehensive” should be in quotes. Sources and magnitude are constantly changing.
  7. 7. What’s the Big Deal with Big Data? Growth in computing power/reduction in cost: Moore’s Law. • Why Big Data is So Big New Sources of Data • Mobile devices and sensor data hugely expand the amount of data generated. • Social media: Facebook and Twitter • But keep in mind: What Executives Don’t Understand About Big Data • Schrage: Question shouldn’t be how do we get more data, it should be “what marriage of data and algorithms” achieve our desired outcome. Value of Big Data lies in unlocking patterns and insights that would not have been possible without the combination of computing power, tools, and data. Alternative definition for Big Data: Advanced Analytics for Complex Problem Solving
  8. 8. DEFINING BIG DATA
  9. 9. • Volume Large datasets: too big for standard enterprise database applications. • Variety Combining structured and “unstructured” data sources. Think “union” of different sources, not big vs. small or structured vs. unstructured. • Velocity Big data systems can integrate and process near real-time data from mobile devices and sensors. • Veracity Data management best practices still matter. It’s not about trading off size vs. quality; it’s about combining best of both worlds. Big Data is an umbrella term that can encompass infinite use cases. The ability to incorporate large diverse data sources into an analytic model is paramount. The 4 Vs of Big Data‟Controlling Data Volume, Velocity, and Variety….to improve internal and external collaboration.” Doug Laney, 2001
  10. 10. RELEVANCE OF BIG DATA TO SCHOLARLY PUBLISHERS With Examples from Medical Publishing
  11. 11. Expectations of Researchers Have Changed Scientific and Medical Researchers Need: Easier faster access to data sets; Ability to trace data provenance; Central repositories or better discovery options for data sets; Business models for accessing, sharing, and adding value to the base of knowledge. Raw Data, Now! Tim Berners-Lee, 2009 [Biomedicine is] going to have to become more dynamic, more computational. Stephen Wolfram, 2006
  12. 12. Expectations of Clinicians Have Changed “…the problem is no longer getting access to data, whether it's a genome sequence or whether it's a glucose sensor, but how do you process that data in an efficient way…” --Eric Topol, 2012
  13. 13. Partial Set of Data Sources in Medical Research Clinical Research Patient registries/ Outcomes DataRx data Sensor data/Exercise tracking OTC & food purchases Disease registriesGenomic data Almost all medical research currently occurs on data types displayed above the fold.
  14. 14. Big Data Uses in Healthcare Are you prepared to play in this fast- growing fast-changing segment?
  15. 15. Getting Started in Big Data  First: recognize that you are all data publishers. If the content is digital, it’s data.  Create standard formats for data sets that are submitted with articles.  Plan for collaborating with Big Data analytics companies.  Develop expertise in new more complex models of medical evidence.
  16. 16. Accelerated Pace of Data Flows Evidence Base Software, models Analysis, insights Data sets, registries, directories Curated news, textual content, summaries Big Data isn’t about structured vs. unstructured data. It’s about building upon the existing base of knowledge with the ability to constantly update the evidence base with new data that either reinforce or replace currently accepted knowledge. A strong foundation remains essential and requires multi- directional data flows.
  17. 17. What Role Will Your Organization Play in Big Data Era? Some possibilities: Evidence Base Software, models Analysis, insights Data sets, registries, directories Curated news, textual content, summaries ← Disseminate latest evidence-based guidelines ← Provide software platform that incorporates latest algorithms and integrates data ← Employ analysts, data scientists, researchers to conduct studies and report results ← Provide master data management services; become clearinghouse for data exchange ← Publish curated scientific research results
  18. 18. THANK YOU! Janice McCallum Managing Director Health Content Advisors Janice@HealthContentAdvisors.com @janicemccallum

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