Who's Getting the Big Bucks for Data Science? And Why?

Data science is scorching hot right now, in headlines, board rooms, university plans, and yes, philanthropy. At least five schools have scored multi-million-dollar grants for data science initiatives just in the past year. Here’s where the funding is going.

The field of research, often derogatorily described as Big Data, involves putting the staggering sums of information we’re stockpiling as part of our increasingly digital activity to good use. It’s often negatively associated with corporate moneymaking strategy, but harnessing data offers tremendous potential—sort of like reading the genome of, well, anything. Researchers are using data science to analyze demographics, marine biodiversity, human behavior, health records, the financial industry, just to name a handful. 

And foundations large and small are getting on board. Consider a few examples:

  • The Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation launched in November a $37.8 million initiative spread across the University of Washington, the University of California at Berkeley and New York University. Sloan and Moore are two of the biggest backers of data science.
  • Hedge fund investor Jaffray Woodriff gave $10 million to the University of Virginia to for the school’s Data Science Institute, a new interdisciplinary program at the school.
  • The University of Rochester received a grant of $10 million from the Wegman Family Charitable Foundation for its Institute of Data Science, part of a $100 million commitment the university has made. 
  • The University of Washington just received $30 million from the Washington Research Foundation, which uses state school patent earnings to support further research. Data science is one of four fields of research the grant will benefit. The school also receives millions in support from Microsoft, Google and Amazon.

And that’s only non-government support. New York City is contributing $15 million each to data science centers at Columbia and NYU, not to mention NSF and NIH grants.

And while there is a rush of universities trying to get on board with the field itself, with several schools opening up data programs, foundations are also calling on data science to achieve their existing goals.  

Robert Wood Johnson Foundation, for example, has been funding projects that use data to improve health care. The Virginia G. Piper Charitable Trust recently granted Arizona State University $10 million to improve health care through the use of data. And a number of environmental foundations are funding work to analyze biodiversity. 

While this is still very much an emergent field, it seems like there a few categories of related giving these days:

  1. Funding the field itself. This is where the Sloan funding and to some extent Moore comes from. Less about where the money is physically going or the application of the research, and more about advancing the work.
  2. Funding so a university can get ahead. This is definitely the University of Virginia story, and a big opportunity to pursue with alumni donors, although as a data-driven investor, Woodriff appreciates the value in the field. This offers alums a chance to put their school at the forefront of a new, hot discipline.
  3. Funding to build regional expertise. New York City and State funding, and the Seattle-area businesses backing UW are particularly aimed at this. There's even an emerging rivalry between the two cities surrounding data science.
  4. Funding data science to get something done. Think health care funders, enviro and development funders realizing the potential here. I'd also place Moore somewhat in this category, although they have a subprogram devoted to Data-Driven Discovery

There is one common thread that seems to run through all of these new initiatives and funding strategies—collaboration. Data science is an inherently integrated field, meaning it necessarily calls on different research disciplines, researchers, universities, even countries to cooperate.

It’s about taking three steps back and analyzing the sea of numbers, collected in multiple places, and from different angles to gain powerful perspective on larger scales. That's likely one reason funders are so into this—it gives them a change to be facilitators of shared knowledge that might not otherwise happen.

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