In technosolutionist circles, the belief is that given the right algorithm we can make use of the massive amounts of information on the web to predict and solve problems. To the technosolutionist, the recent failure of advanced epidemic detecting tools to spot an ebola outbreak until a day after it had been announced by Guinea’s government through broadcast media should be a wake-up call. Foreign Policy gets it right:

On a panel I served on last week, we were asked to name what we thought was the greatest challenge to better understanding the world.  A representative of a government-funded agency stated that, in his program’s view, it was a need for better computer science tools to better extract patterns from data. That’s a worthwhile goal, but not if the data set is incomplete. While there is certainly great need for better data tools, even if one could perfectly extract every piece of information from theNew York Times each day, it would likely not yield a picture of the emerging Ebola outbreak any more detailed than what American government officials already have. Instead, what we truly need is better, more local data (and expanded tools that can translate and process that material) to allow us to more closely listen to and understand local communities.

You see this all over the place. There’s belief that the information is out there, we only need the tools to parse it. If you’re a twenty-something Silicon Valley native at a tech startup I’m sure it can feel like there’s more than enough information in some database or another to answer any question of importance to you.

For most of the world this is not the case, and you don’t even need to go to Guinea to find examples of this (I could show you this problem in my own organization, or the classroom of your choice).

I know Big Data is all so very exciting, but it would be great if we also took the collaborative/cooperative tools that have been stagnating and made them cheaper, better, more open, and less oppressive. It would be great if we poured some money into hiring more people whose job is to cultivate public networks, and if we’d pay people to translate materials from other places rather than just assuming strapping smartwatches to everybody will take care of it. It’d be nice to pull people into the process who specialized at pulling others in. At some point algorithms will matter most, but right now it’s the quality, quantity, and representativeness of input that represent the real roadblocks to better networked problem-solving