this is absolutely correct. and on a related note On Apr 30, 2013 12:06 PM, "Vaishnavi Jayakumar" < [email protected]> wrote:
> You Can't Just Hack Your Way to Social Change > by Jake Porway | 1:00 PM March 7, 2013 > > blogs.hbr.org > http://blogs.hbr.org/cs/2013/03/you_cant_just_hack_your_way_to.html > > "We have a lot of data, but we have no idea what we should do with it." > The director of the foundation looked plaintively across the table at me. > "We were thinking of having a hackathon, or maybe running an app > competition," he smiled. His co-workers nodded eagerly. I shuddered. > > I have this conversation about once a week. Awash in data, an organization > — be it a healthcare nonprofit, a government agency, or a tech company — > desperately wants to capitalize on the insights that the "Big Data" hype > has promised them. Increasingly, they are turning to hackathons — weekend > events where coders, data geeks, and designers conspire to build software > solutions in just 48 hours — to get new ideas and fill their capacity gap. > There's a lot to be said for hackathons: They give the technology community > great social opportunities and reward them with money and fame for their > solutions, and companies get free access to a community of diligent experts > they otherwise wouldn't know how to reach. For all of these upsides, > however, hackathons are not ideal for solving big problems like reducing > poverty, reforming politics, or improving education and, when they're used > to interpret data for social impact, they can be downright dangerous. > > At DataKind <http://datakind.org/> we run "DataDives", weekend events > that team nonprofits with pro bono data scientists to solve tough social > problems. They are not easy to get right. Data events like these require > special requirements beyond your average hackathon. You need to have a > clear problem definition, include people who understand the data not just > data analysis, and be deeply sensitive with the data you're analyzing. > > Any data scientist worth their salary will tell you that you should start > with a question, NOT the data. Unfortunately, data hackathons often lack > clear problem definitions. Most companies think that if you can just get > hackers, pizza, and data together in a room, magic will happen. This is the > same as if Habitat for Humanity gathered its volunteers around a pile of > wood and said, "Have at it!" By the end of the day you'd be left with a > half of a sunroom with 14 outlets in it. > > Without subject matter experts available to articulate problems in > advance, you get results like those from the Reinvent Green > Hackathon<http://www.nyc.gov/html/digital/html/opengov/reinventgreen.shtml>. > Reinvent Green was a city initiative in NYC aimed at having technologists > improve sustainability in New York. Winners of this hackathon included an > app to help cyclists "bikepool" together and a farmer's market inventory > app. These apps are great on their own, but they don't solve the city's > sustainability problems. They solve the participants' problems because as a > young affluent hacker, my problem isn't improving the city's recycling > programs, it's finding kale on Saturdays. > > To avoid this problem, organizations have to be willing to put time and > effort into scoping problems with the technologists ahead of time. Reinvent > Green could have invited recycling managers, urban planners, or other > experts to converse with the hackers before the event. Organizations also > need to be willing to get down-and-dirty with the data geeks during the > weekend. It's not enough to just throw the data over the wall and hope for > the best. > > Subject matter experts are doubly needed to assess the results of the > work, especially when you're dealing with sensitive data about human > behavior. As data scientists, we are well equipped to explain the "what" of > data, but rarely should we touch the question of "why" on matters we are > not experts in. Take for example a finding from the data team at Uber > thatprostitution > arrests increased on > Wednesdays<http://blog.uber.com/2011/09/13/uberdata-how-prostitution-and-alcohol-make-uber-better/> > based > on Oakland Crime Data. One hypothesis for the uptick was that welfare > checks are distributed on Wednesdays, meaning more welfare recipients had > money to spend on prostitution. Wild, right? However, one commenter on > Uber's site who had worked with the Oakland Police Department pointed out > that prostitution arrests occur on quieter nights, so maybe there weren't > more prostitution incidents on Wednesdays, just more prostitution arrests. > If experts in the data — like arresting police officers — had been > involved, this would have been apparent. > > Statisticians have long known that data analysis helps us understand our > world, but never fully explains it. George Box famously said "All models > are wrong, but some are useful." What this means is that we must be > vigilant in communicating that, while all of this new big data will give us > new and wonderful insights into our world, no single result should stand as > the ultimate truth. > > Take, for example, a project the Grameen Foundation brought to a DataKind > event. The Community Knowledge > Worker<http://www.grameenfoundation.org/what-we-do/mobile-phone-solutions/agriculture> > program employs Ugandan workers to provide rural farmers with timely > agricultural information via cellphone. Grameen wanted to use the mobile > data to evaluate which of their workers in Uganda were "good" and which > were "bad". If you only look at the number of times a worker gives someone > information, a certain set of people are identified as good performers. If > you instead look at the number of farmers a worker gives information to, a > very different set is seen as effective. Which metric is right? Well, both > of them. And neither of them. They are merely different perspectives on the > same data. Together they form a richer picture of the world for Grameen > Foundation, but neither should be considered "right". > > We live in exciting and promising times. The flood of data we are > collecting will yield new and earth-changing insights, some of which will > be made by enthusiastic volunteers at hackathons. Let's lay the foundation > for their success by bringing together world-class teams to ask the right > questions, collaborating on the best interpretations of the data, and > striving, always, to be sensitive. Data isn't just a spreadsheet or a > database: It's us. It's the people we care about. It's our world. Let's not > just hack it. > > * > * > > *Please join the conversation and check back for regular updates. Follow > the Scaling Social Impact insight center on > Twitter@ScalingSocial<https://twitter.com/ScalingSocial> > and give us feedback <http://vovici.com/wsb.dll/s/1549g52d90?wsb2=ictr>.* > > http://about.me/vjayakumar > > -- > For more details about this list > http://datameet.org/discussions/ > --- > You received this message because you are subscribed to the Google Groups > "datameet" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/groups/opt_out. > > > -- For more details about this list http://datameet.org/discussions/ --- You received this message because you are subscribed to the Google Groups "datameet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/groups/opt_out.
