Mashup IT reflects human behavior, in real-time and on location, and it tracks people changing their behaviors.
When it comes to big data, most CIOs find themselves in the same boat as Bill Chalmers, beleaguered main character of the novel The Diagnosis. Chalmers work involves an ever-expanding swamp of data. He uses every trick available to master it. He can't keep up. No one in his office can. In a cunning plot element, when he suffers a mysterious ailment, no amount of information seems to help the doctors diagnose him.
Chalmers' world provides an apt metaphor for today's, where information expands exponentially, straining our capability for understanding. Now we've got unstructured data pouring in from sensors, mobile apps, GPS and other real-world, real-time sources. Must we suffer like Chalmers and his hated boss, Stumm, who secretly brings his wife in at night to help him try to catch up?
Perhaps not. Chalmers didn't have Mashup Analytics.
What's Mashup Analytics? An example comes from the Brooklyn Nets basketball team. The Nets rolled out their AchieveMint Challenge, a health-related marketing app in May.
Fans could sign up for the Brooklyn Nets app and feed data to it from apps like FitBit, RunKeeper, Twitter and FourSquare. Fans who signed up and then posted earned points, which could be redeemed for Nets merchandise. Grand prize was a Nets party on draft day, June 27.
The app did well -- more than 1,600 fans signed up in one week. Those fans interacted with the Nets more than 100,000 times in the three weeks of the promotion, checking in regularly from an average of three apps.
What makes this Mashup Analytics? For one, the Nets didn't gather the data, or even initiate the campaign. The Nets had the brand, and the customer base. The team worked with Van Wagner, a sports marketing firm (it pioneered rotational ad signage at sporting events). Van Wagner's client is French pharmaceutical Sanofi, which had the marketing budget. Sanofi knew about AchieveMint, a startup that aggregates social media and health apps. AchieveMint provided the technology, the analytics and, in effect, the IT.
A mere 100,000 data points doesn't sound like the kind of thing that would trouble Bill Chalmers, or any self-respecting CIO. But this is not ordinary data. It's drawn from apps that don't talk to each other. It reflects human behavior, in real-time and on location. And it reflects something hard to do: people changing their behaviors, in response to what was in effect a hands-off marketing campaign.
Mashup IT like this will matter most immediately to CIOs in pharmaceutical and health benefits companies. In markets like the U.S., where the entire health care model is switching away from paying for treatment to paying for outcomes, apps like AchieveMint yield behavioral data that will become crucial to healthcare. Nearly a quarter billion health and fitness apps will be downloaded by 2017, up from 156 million today, predicts iSuppli. Separately, sales of sports and fitness monitors, like heart-rate monitors and pedometers, will reach 56.2 million units in 2017. Many of these will be on mobile phones, and they will increasingly connect to the Internet.
Fitness apps and devices represent a remarkably splintered market (iSuppli tracks the top 20 vendors). AchieveMint may not become the Huffington Post of fitness apps, but it's clear that aggregation is coming to this new kind of content: consumer behavioral data.
Such data does exist now, says Joseph Stetson, VP at Van Wagner Sports, "it's just hard to get to it."
He's excited about Mashup Analytics because it's going to get a lot easier to get that data, something he thinks will change the game for any company that interacts with consumers.
CIOs need to recognize that no one company will be able to create such powerful data by itself. That demands Mashup IT. Questions it creates include ones like these: Since pedometer readings aren't like claims data, how should a health care company meld claims data with individuals' self-reported exercise and diet behavior? How can companies begin to understand what links behavior and risk? Is it best for the CIO to devise the algorithms and structures to do the processing? Or to let third parties do the work, be they an AchieveMint or a consultant?
The answers aren't clear yet, says Mikki Nasch, AchieveMint's CEO and co-founder. She thinks it will take at least six months to devise analytics models that can take these new kinds of data, merge them with existing data, and say something useful about them. "Right now, we don't know what correlates. If I go to Whole Foods and I run every day, what kind of person am I?" she asks.
There are plenty more questions to ask, but a smart CIO can get a head start on diagnosing what it means for his or her company by starting now.
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