DataScience Puts The Data (And Science) Into Data Science

DataScience is, literally, the name in data science.

The Los Angeles-based startup that was founded in 2014 uses algorithms and computer science to provide data-driven solutions to just about any problem any company might face (seriously, DataScience counts among its customer base everything from hot retail startups, like Loot Crate, to international brands, like Eddie Bauer, to even the City of Los Angeles).

Data can be massive, tricky to tackle and usually means a lot of math.

“While there are analytic dashboards for every type of function, business teams are still awash in data without extracting enough knowledge,” the company says on its website. “We realized that the problem is not in the lack of tools available but in their inability to answer why events are happening. To answer the why, you need data expertise, an asset that many business teams are missing. With our solution, any business team can ask questions and get answers from a team of data scientists.”

DataScience CEO and Cofounder Ian Swanson believes that data mining and analysis will play a significant role in the future of eCommerce, especially when it comes to the retail industry, and although big names, like Google, Facebook and Amazon, may have figured that out a long time ago, many more retailers are just now beginning to wake up to its importance.

“There’s only probably 22 leading companies right now — that’s it. And all of them are driving growth away from their competitors because they are algorithmic-driven,” Swanson said. “So, what’s the opportunity? Everybody else has to do this, or their lunch is being eaten. That’s the bottom line.”

Swanson said his company currently has about 75 employees and has raised around $28 million in funding to date.

Companies like Swanson’s can build algorithms that, although they cannot predict the future, can pretty accurately tell a brand or company when a customer is going to cancel a monthly subscription or even when might be the best time to send an email to that customer.

“Some of the core things we solve and challenges for our customers is: How do I grow revenue? How do I keep revenue? How do I reduce operation costs?” Swanson said. “Figuring out what are those levers that a business can control to increase their customer base, increase the revenue of that customer base. We’re the team that builds those algorithms.”

And because of DataScience’s top team of data scientists and data engineers, Swanson said that the models his company builds for clients are usually extremely accurate.

“It depends on the quality of the data, but results that we see are generally above 90 percent accuracy, sometimes even as high as 95 percent,” Swanson said. “So, it’s definitely more than just guiding. It’s something that has some real data, some real valuation behind it, that a company can feel good about making those decisions.”

So, what’s DataScience’s secret sauce that makes it one of the key players and the literal name in the data science industry (other than just being really good with data and things like counting)?

Ironically, or maybe ingeniously, it may be the company’s name itself.

“One could say the name has something to do with it, like we own ‘data science,’” Swanson said of his company’s early success. “When we changed our name from just a placeholder and announced we are DataScience, we went from a dozen applications to a little bit over 100.”

But the story of how DataScience got its name is a bit more interesting than that, because the name just wasn’t sitting around for any business to claim when Swanson and Cofounders Colin Schmidt and Jason Beckhardt started the company back in 2014.

“It was not listed on some website that you could sort of just click on and buy,” Swanson said. “It took a lot of forensic work to figure out how can we go and get that name and who owns it and let’s make a deal.”

Becoming the DataScience in the data science industry may be one of the smartest moves that Swanson and his company ever pull — and maybe it was one of their algorithms that told him to do it.