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A SWOT Analysis of Data Science @ NIH
Philip E. Bourne, PhD, FACMIAssociate Director for Data Science
PSB, Hawaii January 07, 2016
First a Little Context
BD2K is Implementing the 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 Implementation1. 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
The BD2K Program
FY14 FY15 FY16 FY17 FY18 FY19 FY20 FY21$0
$20,000,000
$40,000,000
$60,000,000
$80,000,000
$100,000,000
$120,000,000
total available
BD2K Budget
Opportunities & Threats - Photography
DigitizationDeception
Disruption
Demonetization
Dematerialization
Democratization
Time
Volu
me,
Vel
ocity
, Var
iety
Digital camera invented byKodak but shelved
Megapixels & quality improve slowly; Kodak slow to react
Film market collapses;Kodak goes bankrupt
Phones replacecameras
Instagram,Flickr become thevalue proposition
Digital media becomes bona fide form of communication
Opportunities & Threats: Biomedical Research
Digitization of Basic & Clinical Research & EHR’s
Deception
We Are Here
Disruption
Demonetization
Dematerialization
Democratization
Open science
Patient centered health care
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
Weaknesses
• Access vs privacy of human subjects data• Gender and race inequality• Valuing scholarship / reward systems• Appropriate review • Sustainability• Insufficient resources
Sustainability
• Revised governance structure
• Inventory of NIH data repositories and costs
• The Commons• Interoperability pilots• Sustainability FOAs• Policy
recommendations
ADDS Team
IC Representatives
Leadership
Insufficient Resources
Strengths
439 participants 167 remote viewers Breakout sessions 133 Posters 16 Demos 3 BOFs
http://www.scgcorp.com/bd2k2015/Default
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
Additional Reading
• Strategic plan for 2016-17
• ADDS 2015 Blog
• The Office of Data Science
