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Understanding the Big Data
Enterprise
Philip E. Bourne, PhD, FACMI
Associate Director for Data Science
https://datascience...
My Bias
• University professor - 30+ years
• Associate Vice Chancellor for Innovation – 2
years
• Maintainer of public dat...
None of what I am about to tell you
negates what you have heard thus far
today…
Much of what you have heard is
prerequisit...
My Definition of Big Data
• More than the 4+ “V’s”
• A signal of the coming digital economy
• An economy characterized by ...
What is the Worse that Can Happen?
Digitization
Deception
Disruption
Demonetization
Dematerialization
Democratization
Time...
Enterprises that are not born digital
are at a disadvantage in this new
economy…
Fortunately no university has yet to be
b...
The Writing is on the Wall
(Personal Experiences)
• The story of Meredith
• Increasing number of undergraduates as first
a...
The Writing is on the Wall
(Institutional)
• Changing access models
• Changing funding models
– Less federal and state fun...
Leads to the Notion of the University
as a Digital Enterprise
• The university is defined by its digital assets:
– On-line...
The Most Successful Universities of the
Future Will be Those That Can Best
Leverage Their Digital Assets – How?
“Life Wasn’t Meant to be Easy”
Malcolm Fraser
Former Prime Minister of Australia
How? - Break Down the Silos
Research
Basic Clinical
Education Administration
How? - An Appropriate Organizational
Structure
Chancellor
CIO /CDO
Research
Services
Education
Services
Admin
Services
Med...
Use Cases from the University as a
Digital Enterprise
Research Data
• Prof x drags and drops her research data to the
institutional dropbox. She is asked for a small
amount of ...
Faculty Productivity
• From a single profile a faculty member can, at
the push of a button, generate a world-facing
curren...
The Education – Research Interface
• The UCSD on-line drug commercialization
course which previously had 40 local students...
The Research-Administration Interface
• Researcher x receives a new grant, researchers y
and z are notified since it is ve...
Talk is cheap – What is NIH doing to
address a similar situation?
NIH By Comparison
• 27 silos
• Clinical and basic research
• Intramural + extramural
• Administration
• Education role dif...
Established a Commons
• Supports a digital biomedical ecosystem
• Treats products of research – data, software, methods, p...
Commons Framework Pilots (CFPs)
• Exploring feasibility of the Commons framework
• Facilitating connectivity, interoperabi...
BD2K Centers, MODS
and HMP
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools...
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/...
Commons Credits Model
The Commons
(infrastructure)
Cloud Provider
A
Cloud Provider
B
Cloud Provider
C
Provides credits Ena...
Culture Change
http://mitchjackson.com/white-elephants/
How to Change the Culture?
• Intramural and extramural training programs
• Fostering open science
– e.g. policies, challen...
What is the desired endpoint?
Uber!
Some Thoughts as to Why I am Not
Crazy
• A platform to exchange goods – researchers
produce and consume reagents, data,
kn...
Summary
It was the best of times, it was the worst of
times, it was the age of wisdom, it was the
age of foolishness, it w...
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Understanding the Big Data Enterprise

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SCUP 2016 Mid-Atlantic Symposium: Big Data: Academy Research, Facilities, and Infrastructure Implications and Opportunities. John Hopkins, May 13, 2016

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Understanding the Big Data Enterprise

  1. 1. Understanding the Big Data Enterprise Philip E. Bourne, PhD, FACMI Associate Director for Data Science https://datascience.nih.gov/ philip.bourne@nih.gov
  2. 2. My Bias • University professor - 30+ years • Associate Vice Chancellor for Innovation – 2 years • Maintainer of public data resources (PDB etc. – 15 years) • Open science advocate – 10+ years • Fed – 2 years and counting
  3. 3. None of what I am about to tell you negates what you have heard thus far today… Much of what you have heard is prerequisite to my 30,000 foot view
  4. 4. My Definition of Big Data • More than the 4+ “V’s” • A signal of the coming digital economy • An economy characterized by using data to gain a business advantage (and yes universities are a business)
  5. 5. What is the Worse that Can Happen? 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 [Steven Kotler] http://bigthink.com/think-tank/steven-kotlers-six-ds-of-exponential-entrepreneurship
  6. 6. Enterprises that are not born digital are at a disadvantage in this new economy… Fortunately no university has yet to be born digital … The “Google university” could change that
  7. 7. The Writing is on the Wall (Personal Experiences) • The story of Meredith • Increasing number of undergraduates as first authors on my papers • Talking head lectures • Growing frustration at lack of entrepreneurial support • The Google bus
  8. 8. The Writing is on the Wall (Institutional) • Changing access models • Changing funding models – Less federal and state funds – More sponsored research – Increased tuition – More reliance on philanthropy • Changing pedagogy – MOOCs, SPOCs, DOCCs, flips • Changing student expectations – Expect to be taught in a different way • Changing faculty expectations – Expect more from the institution • Changing staff expectations – Better recognition • Changing employer expectations http://collegeparents.org/2011/01/26/when-your-college-student-unhappy/ Yet demand for a quality higher education has never been higher
  9. 9. Leads to the Notion of the University as a Digital Enterprise • The university is defined by its digital assets: – On-line course materials – All of the research life cycle on-line: grants, data, computational methods, results, conclusions, publications – Faculty, staff and student profiles on-line – All administrative data on-line e.g. grants, policies and procedures, disclosures, contracts, patents, agreements, payroll, academic files
  10. 10. The Most Successful Universities of the Future Will be Those That Can Best Leverage Their Digital Assets – How?
  11. 11. “Life Wasn’t Meant to be Easy” Malcolm Fraser Former Prime Minister of Australia
  12. 12. How? - Break Down the Silos Research Basic Clinical Education Administration
  13. 13. How? - An Appropriate Organizational Structure Chancellor CIO /CDO Research Services Education Services Admin Services Medical Services Library
  14. 14. Use Cases from the University as a Digital Enterprise
  15. 15. Research Data • Prof x drags and drops her research data to the institutional dropbox. She is asked for a small amount of metadata describing the dataset. Part of that request gives permission for the data to be indexed and the index analyzed by the University. That analysis reveals that two other researchers have worked on the same gene in the past two months and they are all alerted as to their common interest and begin collaborating. .
  16. 16. Faculty Productivity • From a single profile a faculty member can, at the push of a button, generate a world-facing current web presence, provide biosketches to the major funding agencies and submit their academic file for review saving countless hours of reformatting which now goes into productive research.
  17. 17. The Education – Research Interface • The UCSD on-line drug commercialization course which previously had 40 local students now has 12,000 several of whom apply to Dr. Bourne’s lab as PhD students based on the material he presented. The course also highlights UCSD’s leadership role and by navigating the on-line curriculum several students apply to UCSD as undergraduates. One high school student applies to Dr. Bourne’s lab as a summer intern.
  18. 18. The Research-Administration Interface • Researcher x receives a new grant, researchers y and z are notified since it is very close to areas in which they work and points of collaboration may be possible. • Researcher x needs to have an assay performed and can immediately locate who on campus and off-campus can perform the work and at what cost. • Experts on and off campus can immediately be identified for the review of a potential patent filing based on a researcher’s technology.
  19. 19. Talk is cheap – What is NIH doing to address a similar situation?
  20. 20. NIH By Comparison • 27 silos • Clinical and basic research • Intramural + extramural • Administration • Education role different https://en.wikipedia.org/wiki/Victory_Soya_Mills_Silos
  21. 21. Established a Commons • Supports a digital biomedical ecosystem • Treats products of research – data, software, methods, papers etc. as digital research objects • Digital research objects exist in a shared virtual space • Digital objects need to conform to FAIR principles: – Findable – Accessible (and usable) – Interoperable – Reusable
  22. 22. Commons Framework Pilots (CFPs) • Exploring feasibility of the Commons framework • Facilitating connectivity, interoperability and access to digital objects • Providing digital research objects to populate the Commons • Enable biomedical science to happen more easily and robustly
  23. 23. BD2K Centers, MODS and HMP Compute Platform: Cloud or HPC Services: APIs, Containers, Indexing, Software: Services & Tools scientific analysis tools/workflows Data “Reference” Data Sets User defined data DigitalObjectCompliance App store/User Interface Mapping Commons PILOTS to the Commons Framework PaaS SaaS BD2K Indexing BioCADDIE, Other, schema.org IaaS [Vivien Bonazzi]
  24. 24. Compute Platform: Cloud or HPC Services: APIs, Containers, Indexing, Software: Services & Tools scientific analysis tools/workflows Data “Reference” Data Sets User defined data DigitalObjectCompliance App store/User Interface Mapping Commons PILOTS to the Commons Framework PaaS SaaS Cloud credits model (CCM) IaaS
  25. 25. Commons Credits Model The Commons (infrastructure) Cloud Provider A Cloud Provider B Cloud Provider C Provides credits Enables Search Uses credits in the Commons IndexesOption: Direct Funding NIH Investigator bioCADDIE [George Komatsoulis]
  26. 26. Culture Change http://mitchjackson.com/white-elephants/
  27. 27. How to Change the Culture? • Intramural and extramural training programs • Fostering open science – e.g. policies, challenges • Fostering changes to the research life cycle – e.g. preprints, data citation, open final reports • Strategic planning with buy-in from major stakeholders • Use cases as exemplars
  28. 28. What is the desired endpoint? Uber!
  29. 29. Some Thoughts as to Why I am Not Crazy • A platform to exchange goods – researchers produce and consume reagents, data, knowledge etc. • A platform built on trust – trust is a key part of the academic enterprise • A platform provides a sustainable business model Sangeet Paul Choudary http://www.wired.com/insights/2013/10/why-business-models-fail-pipes-vs-platforms/
  30. 30. Summary It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair… Charles Dickens

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