Database Reference
In-Depth Information
Gutierrez:
What is something a small number of people know about that
you think will be huge in the future?
Heineike:
I think there's been a focus on people working in tech companies
that have a big website, where there's a lot of click-traffic of people moving
around and they're using data science methods to optimize user experience and
sell a lot of ads or products. There's been a lot of focus on using your company's
own data and then optimizing on top of it. That will continue to happen.
What I actually think is more exciting is that there's a lot of data available now
which could tell us broader stories about what's really going on in everything
from global economic or political systems, to epidemics and conflict, to cities
and our physical environments. Maybe you need slightly different tools to
analyze this data, but the analysis could really transform the way that we make
decisions or interact with those systems. I'm fascinated with how that's going
to change the way that will change those systems in general. And it might be
dangerous, right, because you get data that's not really representative, and
then you can come to the wrong conclusions or bias against some types of
people. So there are definitely some parts we have to be very careful about.
For example, if you think about the broad range of information that can be
gleaned from phone sensor data, that could inform us about the way that
people interact with cities, and how this could change the way that our urban
systems work. Or if you think about the masses of data on what our govern-
ments are legislating and how they are financed, that are really hard to under-
stand at the moment, but could change the way we think about democracy
and government accountability.
We're getting all these tools available, but a lot of the information that we
could be tapping into is actually—when you dig into it—very underutilized at
the moment. I think that means there are a lot of opportunities to do interest-
ing new things with it, and it's going to be fascinating to see what those things
are. And, hopefully, more of them will be, “We did a better job of understand-
ing things. We made better decisions. We built a better way for people to
interact with each other.” And fewer of those will be, “Oh, that's really creepy.
I'm not sure I'm happy with that.”
Gutierrez:
How do you think about hiring people?
Heineike:
An interesting thing that's happening at the moment is that the
academic space is struggling because of funding cuts. And so there are actually
a large number of very, very intelligent people coming out of computational
PhDs and postdocs who are thinking, “I want to get into industry,” and are
actually looking at others doing interesting stuff with data science, and think-
ing to themselves that maybe they'll have a go at that. But often they don't
have any experience in working in business and they don't necessarily have any
ideas about what it's like to work with an engineering team, for example, or
what it would look like to have a product built on top of their work.
