Data is every company’s biggest asset.
Two companies that deal in big data — Jabil and Fidelity National Information Services — are using the massive quantities of data generated by their businesses to save their firms money and to better serve their customers.
“One of the biggest things changing our world today is the collection of data, the analysis of data and the way that it is changing the way business works as we live our lives, often in ways we don’t understand,” said Jason Mathis, CEO of the St. Petersburg Downtown Partnership, which joined forces with University of South Florida St. Petersburg’s Kate Tiedemann College of Business for the Sunny Side Up speaker series.
Jabil (NYSE: JBL), a manufacturing solutions company headquartered in St. Petersburg, has been working with USFSP on a program to educate its leaders and staff about data science for three years, said Candy Mitchell, director of artificial intelligence at Jabil.
“We have lots of data, so much that we don’t know where to look or where to turn, so it’s about knowing what data is that critical data, then visualizing that data, learning more about it, so we can move forward with that,” Mitchell said. “The next step is modeling with the data, predicting outcomes, so we can forecast better. We understand where we are going by looking at models and once we get those models in place where we can predict, then we want to move toward automation. That’s where artificial intelligence comes into play.”
Artificial intelligence is not about taking jobs, but rather automating tasks that are mundane, she said.
“We still keep the people because they need to be subject matter experts, and they need to understand when we have a change in a process that affects the model. It’s important to maintain those models because we don’t want to automate the wrong thing,” Mitchell said.
She cited four ways big data has transformed Jabil’s business processes.
Forecasting accounts payable. In the past, Jabil could only forecast out two weeks with poor accuracy, Mitchell said. “We started looking at how we could look at data differently, visualize the data and then move into modeling. Through modeling, we were able to improve the accuracy significantly and we could do it four weeks out. As a matter of fact, we have months where we were predicting 99.8 percent. Pretty amazing, right? That was a great win for us. If we can forecast that, now we can plan better and we can manage our money better.”
Credit worthiness. “We work with a lot of small companies because we do joint ventures and we help them, but there’s no credit rating for these companies. We don’t really know what interest rate we should give them or what’s the risk to Jabil. By looking at all kinds of factors — factors that are available in the public and factors that we have — we were able to come up with a model to predict their credit worthiness.”
Engineering capacity. “We used to take the dartboard and throw it to figure out just how much time it would take for this engineer to do a job. Now through modeling we can actually do a much better job of forecasting engineering needs.”
Medical devices, especially those that are used in the home and that need maintenance by non-technical users. “Now, it can automatically tell you this is what needs to happen and perhaps even automate that.”
Fidelity National Information Services (NYSE: FIS), a financial technology company based in Jacksonville, provides services to more than 20,000 clients, more than 1 million merchants and hundreds of millions of consumers.
“These fintech services generate petabytes of data. The value is not in the data. It’s in refining the data for our clients, creating new solutions and tools for them,” said Tara Bonnell, senior vice president of the data solutions group. “Our clients specifically are using data to detect fraud. We’re using data to find efficiencies in back office operations and implement automation processes. We’re using data to make smarter loan decisions — not only knowing who might need a loan in the future, but who might actually pay it back.”
Both Bonnell and Mitchell said it’s important that data not be relegated to the IT department only, but that it be shared with leaders in business lines throughout the company.
“It’s important to align data scientists with business so they can work together because we want to make sure that we have the right process, the right data and the models are good models,” Mitchell said.
The challenge is finding data scientists who not only understand the data, but understand the business and know how to correlate the two.
“Coming up with really cool stuff from data is one thing, but really cool stuff that can make a company money is something completely different,” Bonnell said.
FIS is constantly looking for ways to better use its data.
“We have innovation labs across the globe, a fintech accelerator program in Little Rock [Arkansas], and we put hundreds of millions of dollars into venture investments and research to foster ideation and development of these disruptive digital solutions that will empower our clients. We have to. If we don’t we’ll be a dinosaur in a few years, or months,” Bonnell said.