Why understanding data science and AI will change everything
In a rapidly changing world filled with too much information, Tom Lounibos, CEO of Soasta Inc., has a simple, down-to-earth, one-word solution for his customers looking to make a digital transformation: practice.
A one-time college baseball star drafted by the major leagues, Lounibos admitted he often turns to sports metaphors when talking about business solutions. “If you’re making the move to playing pro ball, it’s the same game, but everything is just so much faster,” he explained. “So how do you get there? You watch the films, you hit the batting cage … in other words, you practice.”
For companies looking to develop software like the pros — Amazon, Netflix, Google — practice is really all it takes — sort of. Too bad there’s no digital equivalent of a batting cage. Instead, customers have to rely on something that’s far from simple: advanced software-monitoring solutions that track everything about software performance, usability and response rates. These platforms theoretically can let companies “practice” a design tweak or marketing campaign, but they generate so much information that mere mortals simply can’t wade through it all. Some companies end up with literally thousands of screens monitoring all the data, but at the end of the day, they don’t really know how to take the information and make it work for them, Lounibos said.
That “not being able to get your players ready for the big game” thing wasn’t working for Lounibos, and it’s been a very real issue for Soasta. Too much information is, indeed, too much, and sometimes the volume is off-putting. “We spent a lot of time selling people on our data science platform only to discover they just couldn’t wrap their heads around it all,” Lounibos said. So by putting on his coaching hat, Lounibos and his team of “quants” — as in quantitative analysts, i.e., data scientists — spend their days selling people on the benefits of data science: what it is, how to use it and why it matters. They explain that understanding and pairing data science and AI (artificial intelligence) can help develop better software faster. Now, it’s like going into a batting cage that simulates a real-world environment. But that’s a leap, particularly when it comes to AI, a long-standing technology that is not broadly used and owns a checkered past (HAL 9000, anyone?). That’s where Lounibos’ extensive experience in Silicon Valley with startups and established companies comes into play. He’s no stranger to fast-paced change or the need to coach customers on new technologies. “What I love about our industry is I think we get caught up with a lot of particular technologies,” he said. “But the best thing about this industry is we are more adaptive than pretty much anything else out there. We’re looking for change and not comfortable being static.” For starters, Lounibos realized Soasta needed to practice what it preached.
