Arguably
Chicago's most important and most successful startup in recent years
was the Obama campaign and its celebrated data, analytics, and technical
staff, which attracted some of the city's best brains and re-elected a
president.
Basically
every politico I've ever talked to describes campaigns as start-ups: a
small group of smart people working impossible hours and trying to
remake the industry on the fly. But unlike start-up companies, campaigns
have a strict end date, disappearing as quickly as they're begun, and
leaving a group of top talent on the job market.
What
the Obama campaign's talent will do has been a topic of speculation.
And the chief scientist of the campaign's data analytics team has chosen
his path. Rayid Ghani, who worked as a director of analytics research
at Accenture before joining Obama for America in 2011, will be working
on a host of projects at the University of Chicago. He's taking a role
as chief data scientist for the new Computation Institute's Urban Center
for Computation and Data (a multidisciplinary organization focusing on
the use of data at the civic level), directing a data science fellowship
for social good, and working on collaborations with the Harris School
of Public Policy and the city.
The
reason this area is exciting is that non-profits and other social
organizations have spent the past few years collecting data, because
there's an understanding that it's important to collect, and cheap to
collect. But what they haven't been able to do to is three things:
One
is to really figure out how to ask the questions they really want to
answer. They might have a question about, "how can we be more effective
in this area?" That's not a data question directly. You have to first
translate that into a question: how do you frame that problem?
Second:
they haven't been able to hire the people to solve those analytics and
data problems. There aren't enough of them, and they're in great demand.
And they don't necessarily think that their skills are useful in the
nonprofit and social world. So they go off mostly into the corporate
world.
I
think the third thing is that ones that have these resources and the
right data haven't really been able to figure out how to make that into
things they can take action with. A lot of the people in the nonprofit
world haven't really been in an environment where decisions are made
based on data.
So
the primary goal for the fellowship is training the fellows in solving
real problems that have a social impact, and get them excited in this
area, but also to help nonprofit and social organizations learn more
about what does it mean to do an analytics project: how do you work with
these people, how do you talk to them,Starting today, you can buy these chinamosaic and
more from her Victoria. how do you get them interested in these
projects, and how do you take action based on the outcomes of these
types of analyses?
You
don't have the same deadline-focused enthusiasm and passion. If you
look at an education nonprofit, people are passionate about education,
but because there's no deadline—you can't get the millions of people to
go and do these things right at the same time—the thing you want to get
out is at multiple levels.
One,
in your core group of people, teachers and guidance counselors. You can
have all the sophisticated analytics in the back, but you want to make
it very easy to get this information so they can act on it.Full color streetlight printing and manufacturing services.
On the second is more on the grassroots side,Full color streetlight printing
and manufacturing services. people who care about an issue. They might
be really good advocates for an issue. Teachers and guidance counselors,
it's their job to do this. But other people in the neighborhood and
communities, friends and relatives, just getting that information out in
the communities is a channel we haven't really looked at enough.The
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your home is very important. People who care an issue or the community
may be able in interested in finding out how they can help.
If
you can get the right information to them, the biggest value to
analytics is that you can scale very easily. By having these
technologies, the same way in the campaign we were able to get
information to millions of people, so they can then have home
conversations, in-person conversations, the only way you can do that is
the kind of analytics we were working on.
Let's
go back to the hypothetical high-school dropout example, where we've
figured out the individuals or kinds of people who are at risk,Shop
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from cheap. and here are the neighborhoods that are at risk, and here's
a kind of intervention that actually helps. It could be that the
intervention is that getting people more involved in park district
programs helps. It could be as simple as basketball.
Or
connecting them with other people like them: if you're a high-potential
student, and you live in an area where there aren't students like you,
but there's somebody a couple miles away. We could have kids mentoring
other kids.
You
can use the community to hold events that improve those kinds of
outcomes. If we have data around what improves certain outcomes, then
these can be passed on to all these different organizations, and
influence their programs.
I've
had a lot of conversations with people in the open-data community. What
they've been really good at is exposing a lot of data sets—it's been
made accessible by the city and the government, and they've been good at
exposing it in easy-to-inform ways, like a map or a web app that shows
where the buses are.
That's
been good because the data is now in front of people, because the
normal consumer isn't going to go into a portal, and download a file,
and open it in Excel. So what they've been really good at is bring that
data to the people.
I
think the next thing is using that to make inferences, to make
predictions, to improve certain outcomes. So it's great that you can
look at this data and see where the buses are, but the next step is to
ask "can I improve the bus routes? Can I work with CTA to find better
scheduling?" What I'm pushing them towards is taking the same data
they've been having people look at, and asking "how can I improve the
process that's generating this data?"
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