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At Enova International Inc., a Chicago-based online financial services firm, the company has been investing heavily in Ph.D.-level data scientists. But that approach to building an analytics team raises a question: How you do you adapt academic predictive modeling techniques to business processes?
Joe DeCosmo, Enova's chief analytics officer, said a member of his team recently told him that when the analyst first started working at the company, he had to get over his academic instincts to detail every theory-based aspect of the predictive models he builds in order to focus more on the business impact the models can have.
"They have to realize they don't have to build the perfect model," DeCosmo said. "It's about building something that's better than what we're doing currently."
This issue is heating up as more businesses look for workers with data science skills. Often, the people who have the skills organizations need, which include statistical analysis, machine learning, and R and Python programming, come from academic backgrounds. But businesses don't have the kind of time that Ph.D. programs give students to build analytical models. In the real world, models need to be built and deployed quickly to help drive timely business strategies and decisions.
Focus on perfection doesn't pay
About 20% of the people on the analytics team at Enova have doctorates. DeCosmo said most of the analysts come around to a more business-focused way of doing things once they see how the end-product of their work can improve a specific business process. For example, Enova recently applied predictive modeling techniques to identify suitable recipients for a direct mail marketing campaign, to better target the mailing. That helped improved response rates by about 25%, according to DeCosmo. The model may not have been perfect, he added, but the kind of rapid improvement it led to helps data scientists understand and appreciate the value of their work.
"At our scale, if we can get a model into production that's 10% better, that adds material impact to our business," DeCosmo said.
There's always a tradeoff between time and predictive power when developing analytical models. Spending more time on development to make a model better could allow a data scientist to discover new correlations that boost the strength of its predictions. But DeCosmo said he sees more business value in speedy development.
"We're very focused on driving down the time [it takes to develop models]," he said. "There's no such thing as a perfect model, so don't waste your time trying to build one. We'd rather get that model out into production."
Simplicity drives predictive modeling speed
For Tom Sturgeon, director of business analytics at Schneider Electric's U.S. operations in Andover, Mass., the top priority is empowering business analysts to do some straightforward reporting themselves and free up his team of data scientists to focus on more strategic analysis work.
Schneider Electric is an energy management company that sells products and services aimed at making energy distribution and usage by corporate clients more efficient. In the past, for every new report or analysis a business unit wanted, Sturgeon and his team would have to pull data out of a complex architecture of ERP, CRM and business intelligence systems, all of which were themselves pulling data from back-end data stores. Sturgeon described these systems as middlemen because they hold a lot of useful data, but on their own don't make data easily accessible. His team had to manually pull data out, an action which itself has less value than the actual analysis.
But since 2013, they've been using a "data blending" tool from Alteryx Inc. to bring all the data into an analytics sandbox that business analysts can access with Tableau's data discovery and visualization software. Sturgeon said that allows the business analysts to skip the "middleman" reporting systems and build their own reports, while his team does deeper analyses.
"We take the data and bring it together," he said. "Then we say, 'Here's the sandbox, here are some tools, what questions do you want to ask?'"
Tom Sturgeondirector of business analytics at Schneider Electric's U.S. operations
Even when doing more data science work, though, the focus is on simplicity. The analytics team is still working to develop its predictive capabilities, so for now it's starting small. For example, it recently looked to see if there was a correlation between macroeconomic data published by the Federal Reserve and Schneider Electric's sales. The goal was to improve sales forecasting and set more reasonable goals for the company's salespeople. The analysts could have brought in additional economic data from outside sources to try to strengthen the correlation, but they instead prioritized a basic approach.
"We aren't looking to build the best predictive model," Sturgeon said. "We're starting simple and trying to gain traction."
Predictive modeling isn't BI as usual
In looking to unleash effective and speedy predictive modeling techniques in an organization, bringing a standard business intelligence mindset to the process won't cut it, said Mike Lampa, managing partner at consultancy Archipelago Information Strategies.
Speaking at the 2015 TDWI Executive Summit in Las Vegas, Lampa said workers involved in predictive analytics projects need to have much more freedom than traditional BI teams, which typically spend a lot of time initially gathering project requirements. That would be a waste of time in a predictive project, he added. Meaningful correlations are often found in unexpected data sets and may lead to recommendations that business managers weren't necessarily looking for.
Setting project requirements at the outset could slow down the analytics process and limit the insights that get generated, Lampa cautioned, adding that data scientists have to be able to go where the data takes them. "You can't create effective models when you're always tied down to predetermined specifications," he said.
Predictive modeling is about much more than math
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