This blog was submitted by Arnaud Sahuguet, former CTO at the NYU Governance Lab and present lead at The Foundry @ Cornell Tech
After recently participating in Bloomberg’s 2015 Data for Good Exchange, it became even more apparent that making decisions about public problems should be (1) data-driven, (2) collaborative and (3) participatory.
Data-driven approaches are the foundation of any kind of science. While companies, governments and non-profits have embraced this thinking, collaboration is rare. Why?
Issues of funding and attributions often make the incentives misaligned between parties. In the private sector, collaborations exist, but usually on both ends of a legal contract. In the non-profit world, most often each player is convinced his or her solution is better. Inside government, we see little collaboration because of turf-wars, but also because of ignorance.
Collaboration is important, as is participation. Citizen science, scientific research conducted by non-professional scientists, is on the rise, and corporations, governments and non-profits are embracing this type of participation. Science used to be a hobby before it became a profession, and we’re seeing it make its way back to its roots. For example, Twitter, Facebook and others base their value of content generated by their participatory users. Governments and non-profits are crowdfunding and using participatory budgeting.
So, how do we encourage players to be more data-driven, more collaborative and more participatory inside their own sector? And how do we encourage this kind of behavior across sectors?
I see open-data as the glue (or the lingua franca) people can use to make and document better decisions according to these three pillars. The most cited examples of open-data work that have had a major impact on society are GPS, the Human Genome project and weather data. What seems commonplace now would not have been possible without open-data. And we can see more examples every day.
I myself co-wrote a paper on Open Civic Data which focuses on the (re)use of data that has already been collected, often by public agencies, utilities and other civic institutions to help in areas such as economic development, crises management and even things like mortgage fraud.
And there are numerous other examples on the Bloomberg Data for Good Exchange site where you can read paper submissions in the areas of government innovation, environment, public health and education.
Yes, here are lots of challenges and lots of pitfalls including privacy, data-bias, data and data science literacy. We are just at the beginning. I look forward to what the future holds as a result of open-data.