Establishing the connection between crowd-sourced data and decision makers

Wednesday, 17 December 2014
Larry J Paxton1, William Swartz2, Shadrian B Strong3, Maegan g Nix3, Robert K Schaefer1 and Michele Weiss4, (1)The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (2)Johns Hopkins Univ, Laurel, MD, United States, (3)Applied Physics Laboratory Johns Hopkins, Laurel, MD, United States, (4)JHU/APL, Laurel, MD, United States
There are many challenges in using, developing, and ensuring the viability of crowd-sourced data. Establishing and maintaining relevance is one of them but each participant in the challenge has different criteria for relevance. Consider, for example, the collection of data using smart phones. Some participants just like to contribute to something they consider good for the community. How do you engender that commitment? This becomes especially problematic when an additional sensor may need to be added to the smart phone. Certainly the humanitarian-egalitarian may be willing to “buy-in” but what value does it hold for the entrepreneurial-individualist? Another challenge is that of the crowd-sourced data themselves. Most readily available apps collect only one kind of data. The frontier lies in not only aggregating the data from those devices but in fusing the data with other data types (e.g. satellite imagery, installed sensors, radars, etc.). Doing this requires resources and the establishment and negotiation of data rights, how data are valued, how data are used, and the model used for support of the process (e.g. profit-driven, communal, scientific, etc.).

In this talk we will discuss a few problems that we have looked at wherein distributed sensor networks provide potential value, data fusion is a “value multiplier” of those crowd-sourced data and how we make that connection to decision makers.

We have explored active decision making through our Global Assimilation of Information for Action project (see our old website and the use of “serious games” to establish affinities and illuminate opportunities and issues. We assert that the field of dreams approach (“build it and they will come”) is not a sufficiently robust approach; the decision-makers (or paying customers) must be involved in the process of defining the data system products and quantifying the value proposition for their clients.