How big data became a silent colleague for artists and designers

How big data became a silent colleague for artists and designers


JUDY WOODRUFF: One major transformation profoundly
affecting the global economy is the way that big data and artificial intelligence are being
used in commerce and business. What’s gotten less attention, how this decision
is driving changes in the creative industries. In the second of two pieces, special correspondent
and Washington Post columnist Catherine Rampell looks at some of the fundamental questions
this is raising for artists, designers and other creators. It’s part of our regular series Making Sense. CATHERINE RAMPELL: This bright, cheerful clothing
line is a hot commodity, worn by the likes of Michelle Obama, Aidy Bryant, Taylor Swift,
Beyonce. TANYA TAYLOR, Tanya Taylor Clothing: People
wear us to be happy. CATHERINE RAMPELL: Tanya Taylor is undoubtedly
creative, an artist who paints original prints for her clothes. But she’s in demand partly because she gives
customers what they want. TANYA TAYLOR: To me, the biggest part about
being a successful designer is absolutely listening to a customer and knowing who they
are. CATHERINE RAMPELL: And she knows what they
want because they tell her quite explicitly. ACTRESS: So what do you guys do with your
closet space now that you rent the runway? CATHERINE RAMPELL: Thanks to detailed feedback
she receives from Rent the Runway. It’s a platform that allows customers to rent,
rather than buy designer clothes. NARRATOR: Endless styles, infinite possibilities. SARAH TAM, Rent the Runway: We basically had
harnessed millions of data points over the last decade. CATHERINE RAMPELL: Sarah Tam is Rent the Runway’s
chief merchant officer. SARAH TAM: Every item of clothing that we
have on our site is tagged with over 60 attributes, things like color, fabric, silhouette, length. We also have millions of customer interactions
that we collect and millions of photo reviews. CATHERINE RAMPELL: The data help Rent the
Runway refine its inventory and predict what its typical customer will want next season. SARAH TAM: Last fall, we noticed that blazers
really performing extremely well. She loves color and pattern, so we sourced
brands like Veronica Beard that we launched on site. She loves fitted. She likes to outfit in suit sets. So we brought in this Veronica Beard set here. CATHERINE RAMPELL: The data also get Fed back
to designers like Taylor, who use it to nip in the hips or let out the bust, or choose
a different color or fabric, or mash up elements of different designs that are working well. TANYA TAYLOR: So this was our Inez Dress,
and it was definitely the most rented dress of our last spring season. And what we learned is that people loved the
stretch linen, but they didn’t love the snap at the neckline. The next step we learned was that people love
jumpsuits from Rent the Runway. So we’re like, let’s combine that information. What we did, is we took away the snap, and
then it’s a little jumpsuit shape. CATHERINE RAMPELL: In a dark neutral print,
because that’s what the data advised. Access to this kind of feedback significantly
improves the chances that a creation will succeed. CHRISTOPHER SPRIGMAN, New York University
School of Law: The biggest risk for most creators is the risk that what they create will fail. CATHERINE RAMPELL: Law professors Christopher
Sprigman and Kal Raustiala have researched how the harvesting of vast troves of data
is changing creative industries, and what it might mean for their legal protections
and economic rewards. CHRISTOPHER SPRIGMAN: Human creativity has
always been an incredibly risky endeavor as a business. If data can lower that risk, it makes creative
endeavors easier to invest in, potentially more rewarding. KAL RAUSTIALA, UCLA Law School: It’s not a
guarantee, but they’re going to place a better bet. CATHERINE RAMPELL: Creative industries have
traditionally had difficulty predicting what will sell and what won’t. As screenwriter William Goldman memorably
put it in his 1983 memoir, “Nobody knows anything.” WILLIAM GOLDMAN, Screenwriter: Very simply,
people go to see hits because they want to see that movie. They don’t go to see flops because they don’t
want to see that movie. It’s as simple as that. And the problem Hollywood has is, they can’t
figure out why. KAL RAUSTIALA: It’s one of the many reasons
we see so many sequels. What’s worked before will probably work again. CATHERINE RAMPELL: Big data has allowed companies
to figure out what works with much more precision, which, of course, can mean more precise pandering
to the masses. KAL RAUSTIALA: The processes that we’re talking
about tend not to give you something wildly different. They tend to give you more of what you already
watched or listened to or liked. CHRISTOPHER SPRIGMAN: Keep in mind that there’s
a bunch of literature on how much novelty people want. And the answer is a relatively modest amount. People like paintings that look somewhat like
the paintings they have seen. People like movies that are somewhat like
the movies they have seen. CATHERINE RAMPELL: That said, data has been
used to overturn at least some of the conventional wisdom about what and who audiences want to
see. CHRISTOPHER SPRIGMAN: An example is Netflix,
which not too long ago produced a film with Sandra Bullock called “Bird Box.” They cast an older female lead, a relatively
diverse cast in this horror film. That’s a relatively adventurous choice that
turned out to pay off for them. And the talk among Netflix people was that
they did that in response to data. CATHERINE RAMPELL: A ton of data pulled from
more than 100 million users’ viewing habits. CHRISTOPHER SPRIGMAN: Size, scale is very
important here. To make use of data, you have got to collect
a lot of it. CATHERINE RAMPELL: How replicable is what
you do? Could an upstart produce the high-quality
data and analytics that you have? SARAH TAM: It’s not so easily replicable. We have a decade worth of data, along with
a lot of the technology that we employ to analyze the data. CATHERINE RAMPELL: This hunger for data might
be driving consolidation in creative industries. Take the merger of Time Warner and AT&T. CHRISTOPHER SPRIGMAN: They went to the judge
and they said, look. Time Warner is a programmer. AT&T has a platform. What we need to do is, we need to link these
things up so that we can get the data to Time Warner that allows them to produce better
content. CATHERINE RAMPELL: That might be good for
the newly merged company. but, says Sprigman: CHRISTOPHER SPRIGMAN: If the returns to data
keep growing and growing and growing as you get bigger, we could have a pretty strong
impetus toward monopoly, or at least significant market power. And that’s a concern. CATHERINE RAMPELL: Also a concern, privacy. Consumers may not know their Netflix-watching
habits, for example, are being closely monitored. KAL RAUSTIALA: Most people don’t realize how
much data about their activities, when they’re stopping, when they’re starting, that’s being
in a sense just gathered up and then spit back at them in different ways, or maybe sold
to third parties, which is a concern that a lot of people increasingly have about their
data in other contexts. CATHERINE RAMPELL: On the other hand, some
customers turn over this information willingly. SARAH TAM: Our customers, 98 percent of them
give us item level feedback after every time they rent something. So we can understand if our customer loves
an item, how it’s fitting her, how many times she’s wearing it and where she’s wearing it
to. CATHERINE RAMPELL: And the customers just
provide all of this information to you voluntarily? SARAH TAM: Yes. Believe it or not, we have built this incredible
brand community. CATHERINE RAMPELL: There are other legal questions
that arise from this use of big data, like whether we should rethink copyright law, which
exists in part to incentives artists to create. CHRISTOPHER SPRIGMAN: Copyright is a way of
lowering the risk of investing in creative enterprises. If data-driven creativity is lowering that
risk, then it will kind of be a helpmate to or even a stand-in for copyright protection. CATHERINE RAMPELL: And who even deserves to
own the copyright to a work, if it’s created by algorithm, rather than artist? CHRISTOPHER SPRIGMAN: The author is now not
bringing something out of nothing. The author is kind of conjuring all of our
preferences, taking them into account, and in a sense reflecting ourselves back on us. If this shifts people’s views of who’s responsible
for the creative work, where it’s more of a community project, then this might shift
some of the moral supports that undergird copyright protection. CATHERINE RAMPELL: Yes, do I own my consumer
preferences or do the companies whose stuff I buy own those preferences? CHRISTOPHER SPRIGMAN: That is a very current
debate over whether you and I own the data that we in a sense produce through our activities
and that we transmit to these companies. CATHERINE RAMPELL: Artists will argue that
they’re still running the show. SARAH TAM: The algorithm isn’t really telling
them how to create the art. I think it’s just optimizing the art they
create. CATHERINE RAMPELL: It wasn’t like the data
was plugged, it was, like, fed into a computer, and boop, boop, boop, boop, boop, like, the
algorithm spit out this. TANYA TAYLOR: No. I don’t think women’s minds work in algorithms,
unfortunately. I wish it could be that straightforward and
easy. It’s more intuition, and you have to read
between the lines with the data. Where women are going next is hard to predict. CATHERINE RAMPELL: At least for now. For the “PBS NewsHour,” I’m Catherine Rampell
in New York.

Dereck Turner

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