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A SWOT Analysis of Data Science @ NIH
This document discusses a SWOT analysis of data science at the National Institutes of Health (NIH). It outlines opportunities and threats, as well as strengths and weaknesses. Key points include that data science will become more central to biomedical research but the transition is taking longer than needed. Opportunities include open science and patient-centered healthcare, while threats include insufficient resources and issues with data access and privacy. Strengths include large datasets and diverse data types available, as well as collaboration, but weaknesses include gender/race inequality and sustainability challenges. The Big Data to Knowledge (BD2K) program aims to address these through initiatives like the NIH Commons and policy recommendations.
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Introduction to a SWOT analysis of data science at NIH, highlighting the context of the presentation.
Overview of BD2K's implementation steps and budget for supporting data science and informatics initiatives.
Explores the disruption in photography due to digital transformation and its impact on traditional markets.
Identifies digitalization's role in biomedical research and the challenges that need addressing for better efficiency.
Lists weaknesses such as access vs privacy, resource insufficiency, and issues around inequity in data science.
Discusses strategies for sustainability in data science, including governance, interoperability, and policy recommendations.
Highlights the composition and challenges faced by the ADDS team in resource allocation within NIH.
Showcases strengths including large datasets, diverse data types, and strong collaboration within the data science community.
Provides resources for strategic planning and additional information about NIH's data science initiatives.
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A SWOT Analysis of Data Science @ NIH
- 1. A SWOT Analysisof Data Science @ NIH Philip E. Bourne, PhD, FACMI Associate Director for Data Science PSB, Hawaii January 07, 2016
- 2. First a LittleContext
- 3. BD2K is Implementingthe ACD Data & Informatics Recommendations* DIWG Recommendations 1. Sharing data & software through indexes 2. Advance big methods, tools & applications 3. Expand data science training 4. Continued support throughout the data & software lifecycle BD2K Implementation 1. Implement the Commons (indices, standards, etc.) 2. Data science research programs (Centers, U01s, etc.) 3. Training and workforce development programs 4. Addressing sustainability of science, technology, and funding mechanisms * http://acd.od.nih.gov/diwg.htm
- 4. The BD2K Program $0 $20,000,000 $40,000,000 $60,000,000 $80,000,000 $100,000,000 $120,000,000 FY14FY15 FY16 FY17 FY18 FY19 FY20 FY21 total available BD2K Budget
- 5. Opportunities & Threats- Photography Digitization Deception Disruption Demonetization Dematerialization Democratization Time Volume,Velocity,Variety Digital camera invented by Kodak but shelved Megapixels & quality improve slowly; Kodak slow to react Film market collapses; Kodak goes bankrupt Phones replace cameras Instagram, Flickr become the value proposition Digital media becomes bona fide form of communication
- 6. Opportunities & Threats: BiomedicalResearch Digitization of Basic & Clinical Research & EHR’s Deception We Are Here Disruption Demonetization Dematerialization Democratization Open science Patient centered health care
- 7. Opportunities & Threats •O: “Disruption” - data & analytics will become more central to the biomedical enterprise • T: The time to this realization is much longer than it need be • T: The efficiency of the enterprise is not what it should be • T: We do too little to address existing & future pain points
- 8. Weaknesses • Access vsprivacy of human subjects data • Gender and race inequality • Valuing scholarship / reward systems • Appropriate review • Sustainability • Insufficient resources
- 9. Sustainability • Revised governance structure •Inventory of NIH data repositories and costs • The Commons • Interoperability pilots • Sustainability FOAs • Policy recommendations
- 10.
- 11. Strengths 439 participants 167 remote viewers Breakout sessions 133 Posters 16 Demos 3 BOFs http://www.scgcorp.com/bd2k2015/Default
- 12. Strengths • Large datasets,e.g., 46M Aetna EHRs • Data integration, e.g., Mobile health + Yelp • Analysis, e.g., machine learning to predict phenotype from EHRs • Diverse data types, e.g., genomics, proteomics, imaging, clinical trials, EHRs • Collaboration, e.g., joint API development, use and requests for metadata templates, data sharing • Depth of training • International
- 13. Additional Reading • Strategicplan for 2016-17 • ADDS 2015 Blog • The Office of Data Science
- 14. NIH… Turning Discovery IntoHealth philip.bourne@nih.gov https://datascience.nih.gov/