3 lessons industry leaders can teach each other about data and analytics

Sports, health care, and education aren’t all that different when it comes to collecting and using data.

By Meredith Somers  |  February 26, 2018


From left: Justin Pinchback, Amy Howe, and Matthew Fields at the 2018 MIT Sloan Sports Analytics Conference

Why It Matters

The sports industry isn’t the only business still finding that sweet spot of collecting and analyzing data.

You wouldn’t ask whether a quarterback can throw accurately, or whether a receiver can catch — you’d ask to see them in action.

The talent strategy team at finance firm Citadel is following that NFL-inspired approach when it comes to bringing on new technical team members.

Justin Pinchback, head of talent strategy and solutions at Citadel, shared his company’s tactic during a Feb. 23 industry panel at the MIT Sloan Sports Analytics Conference. The panel was billed as a discussion about what sports could learn from other industries, but panelists showed that every industry has room for improvement when it comes to data and analytics. Here are three examples:

To win the data game, you have to adapt
To see how potential new hires act in real situations, Citadel created competitions that allow them to use the same data and address the same problems as they would on the Citadel payroll.

“We want to create simulations where we can watch talented people in their craft — sometimes individuals, sometimes teams — so we can then see is this person going to lead, is this person going to follow,” Pinchback said. “What’s going to happen when they’re under tremendous time pressure, when the stakes are real?”

Just like NFL teams looking to attract and keep the best players, it’s about balancing flexibility with the individual needs of people, while staying within a company’s culture and accomplishing what you want to do as a business.

“Figuring out how we adapt is going to be critical for not just attracting great people but for keeping people motivated,” said Amy Howe, COO of Ticketmaster North America. “You’ve got to figure out how to create advocacy for your best talent in your organization. People want to know that there’s a career path for them, that you’ll have their back during very difficult situations.”

Matthew Fields, executive vice president at publisher Houghton Mifflin Harcourt, said for the education industry, it’s about highlighting the mission and purpose of the job, and recruiting a diverse workforce.

“We struggle in our industry, too, on how to apply those same principles to attracting talent and developing talent that looks more like America,” Fields said. “In that regard sports can learn from sports, and we can learn from sports.”

Data supports decisions — it doesn’t make them
Another thing the education industry is learning is that analytics can be an assistant to teachers, but not a replacement for them.

“The algorithms do a lot, but they can’t tell you if a kid is being bullied, if a kid didn’t have breakfast, or if the kid is having problems with somebody else in the classroom,” Fields said. “For us it’s understanding the limits, scoping out what the data and the analytics can do — things like tracking and recommending content — but teachers still make the final decision. There’s a parallel there in sports: [Data] may help you triangulate, but it’s not going to make the ultimate decision for you.”

That’s something the health care industry has also realized as it balances using personal data to customize patients’ treatments.

Corbin Petro, president and CEO of Benevera Health, said that data can tell you what’s wrong with someone’s health, but it can’t tell you the root cause of a problem — like that someone with diabetes suffers from recurring low insulin because they can’t afford to refill their prescription.

“Obviously the data helps point you in the right direction,” Petro said. “Health care is human intervention. It’s incredibly important. We really want to get it right when you identify something with a patient. But it’s not the right industry to test things.”

Figuring out how to use data effectively requires strategy
For an industry like ticket sales and distribution, it’s important to test and try things that help “take the friction” out of the customer experience.

Howe said her company launched Verified Fan last year in an effort to give customers what they wanted: tickets.

“When you start to pull back the data, of course people will say they don’t like the fees, but the real issue is that the average fan can’t get to the ticket they want,” Howe said. Verified Fan “separates the registration process from sale activity using algorithms and data science to be able to identify if you are a fan that’s going to go to the event or are you going to flip it on the secondary market.”

Not every industry is effectively using the data it collects, however. Fields offered as an example identifying patterns of response in a testing environment. A student who gets eight questions in a row correct and the last two wrong, is different than a child who answers every second question wrong.

“Understanding how those patterns of response tie to and then demonstrate mastery, before you promote a student and move on, is what we spend a lot of time figuring out,” Fields said. “The other thing is stepping back and doing some meta-analyses with the data available at our disposal, just to make the programs better. Stepping back to ask where is the program deficient, and not necessarily the kid.”

The sports industry might see that as an interesting implication for how to develop athletes, Fields suggested. Where sports falls behind a bit is that even though everyone has wearable technology now, the data to make an important training decision for an athlete might not be there.

And having the right data is key when “thinking about rehab and how you can bring folks back faster in an effective way without going past that line and pushing them too much,” Fields said.