Hilary Mason Turns Big Data Into Plain English
WHY YOU SHOULD CARE
Whether we like it or not, big data is our future. But the woman leading the charge says the human touch is essential.
By Emily Cadei
Little screams “Eighties!” louder than the boxy, beige Apple IIe, the one that sent waves of glowing green script down its screen when turned on. But for data scientist Hilary Mason, the musty hardware worked a revolution: It was her entree onto the path that would lead her to the cutting edge of “big data,” the hottest field of the 21st century, if you believe the Harvard Business Review.
“I actually learned to write code when I was a kid, just by the fact that you turned these machines on and they prompt you to do something,” the 35-year-old recalls of her days as a budding tech geek.
Two degrees and several tech jobs later, Mason has become a leading voice for a new generation of scientists who are inventing ways to process huge sums of human data. Advances in digitized information sharing and computing power have opened almost the whole world to analysis.
Data scientists take a real-world conundrum, dive into algorithms and mathematical models, and bring it down to earth to explain the data.
Yes, the concept of big data — using high-powered computing tools to scrape the records of gazillions of individual actions and draw out larger societal or business trends — carries a Big Brother tinge. Target’s ability to predict whether we’re pregnant and Facebook’s creepy social experiments on unwitting users are just a hint at what’s possible. But whatever you think of it, big data is set to gain even more power, not just in business but in society at large — and Mason helped make it so.
The quirky, engaging Manhattan native had timing on her side — she entered the startup world from academia late last decade, right as the hype around data was building. Back in 2009, before the term big data existed, she was among those formulating the concept of a “data scientist” and carving out a role for data analysis in the business world. Perhaps most importantly, she’s helped build a community of data geeks — “awesome nerds,” she calls them — giving some coherence to this emerging field.
That includes co-founding HackNY, an organization that develops young tech talent, and helping convene an annual conference for data scientists in New York, DataGotham. It’s helped build a buzzing New York tech scene that has come into its own in the last five or six years, with Mason a visible participant.
She’ll tell you that those social instincts are the key to a true data scientist.
“You need someone who is empathetic, who understands what the business is really trying to do,” Mason explains to OZY. Data scientists must be able to take a real-world conundrum, dive into an esoteric world of algorithms and mathematical models, and bring the whole query back down to earth to explain what the data says.
Mason began moving between high-tech and human early on. As a teenager, this daughter of an investor and a pastry chef traded in the cab-clogged streets of the Upper West Side for the Grinnell College, in the open plains of central Iowa. She describes it as “a really nice hippie liberal arts school where people care a lot about the world” — nice enough to let her double major in English and computer science, something that wasn’t all that common at the time, she says. Studying literature and philosophy as well as code “had a huge impact” on her professional skills later on, she says.
Afterward, she became a software engineer at a startup that went bust when the dot-com bubble burst. Engineering made her realize that “what you learned in academic computer science doesn’t bear that much resemblance to actual computer programming.”
Then there was an unhappy stint as a professor. After completing a master’s degree in computer science at Brown, she taught at Rhode Island’s Johnson & Wales University. It “wasn’t very much fun and was also very clear to me that it wasn’t where I belonged,” Mason says. But “trying to teach computer science to people who are not that excited about it, nonmajors,” also helped with her communication skills, she says.
As the technologies and the concepts mature, big data expertise will become ‘a piece of every business … not a separate business of itself.’
At Bitly, where Mason worked from 2009 to 2013, she was in charge of making sense of its mounds of user data — from all the people using the service to shrink and share links all over the world. It came down to human behavior and interaction, looking at how people in different places at different times responded to breaking news stories or viral videos of cats cuddling bunnies. Mason’s team also went further, building models to predict how many clicks certain pages might get from certain audiences.
Now Mason is adding a new skill set to her arsenal: businesswoman.
In June, she launched Fast Forward Labs with three employees. The concept is to play the “role of smart, nerdy friends” who advise companies on how to turn data into better business decisions. So far, the firm has three paying clients, enough to pay the bills while Mason recruits more, she says. She’s opted not to use venture capital funding.
She’ll face plenty of competition. Data analytics shops are “popping up everywhere” right now, says George Mason University astrophysics professor Kirk Borne, a big data connoisseur. It’s like the spread of dot-coms during the first round of Internet innovation in the late 1990s, he says. He predicts that as the technologies and the concepts mature, big data expertise will become “a piece of every business … not a separate business of itself.”
The possibilities have only begun to be realized. Mason says she’s excited about her company exploring efforts “to build machines that can do things that we previously would have thought were the domain of humans alone.” A news site, for example, that could “gauge what a reader has already read and tailor stories to the level of information they need — a lengthier explainer for someone who is coming fresh to the topic or a two-sentence bulletin for the avid follower.”
The idea of one’s personal data becoming a scientist’s plaything doesn’t appeal to everyone. But Mason thinks of the promise of big data in collective, not individual, terms. For the first time, we can “study human behavior at the scale at which that human behavior is actually occurring,” she says with glee. “We can actually learn about people as a community.”
Immensely powerful tools, indeed. But a few words of wisdom, which apply equally to Spider-Man and the woman at the forefront of an entirely different kind of Web: With great power comes great responsibility.