There is a shortfall of data scientists in the technology marketplace. And at Infor, Inc., the company is dedicated to recruiting the best scientists, mathematicians, economists and engineers through the company’s Dynamic Science Labs based Cambridge, MA. This team collaborates with customers to improve their data and overcome industry challenges by using science.

Leading the program is Leigh Martin, senior director of Science Management at Infor’s Dynamic Science Labs. During  Inforum 2016, Martin spoke with Dave Vellante (@dvellante) and George Gilbert (@ggilbert41), cohosts of theCUBE, from the SiliconANGLE Media team, about her team and the role of data and the data scientist in the enterprise.

This week, theCUBE features our interview with Leigh Martin as our Women in Tech of the week column.

The dynamic science of data

Vellante began the interview by asking about Dynamic Science Labs’ mission and how the company’s data scientists work. Martin described how her team engages the customer.

“We typically start our engagements with customers, and we do that through a proof-of-concept phase. So if we are developing a new product or trying to do a new science-based enhancement, we work with customers. And the first thing we want to do is bring in data. We’ll identify a particular problem in an industry that we are trying to solve; we’ll speak to a variety of customers; we’ll find a few good matches, a customer that is interested in working with us on a proof of concept in giving us their data.

“Then we’ll work with them over a series of a few months to look at their data. That’s always the first step, getting an understanding of their data. And, for us, because we work across all the industries at Infor, we’re touching lots of different types or data. The folks in our team don’t necessarily come with those industry backgrounds, and some of it’s learning as we go. So getting access to the data is a way for us to start understanding that industry.”

Recruiting data scientists — It’s not just for tech companies

Vellante noted the lack of data scientists and questioned if it is a barrier to growth. He asked if it is still high-powered data scientists that are needed or if there is a way to permeate these positions throughout organizations. Martin talked about the competition to recruit data scientists and how the need is expanding from tech companies to their customer’s businesses as well.

“There is a lot of competition for data scientist right now. It’s actually really hard to recruit folks to come into our team. Prior to coming to Infor, I worked at other analytics teams. So I’ve been around in the industry for a while now. When I first started, data science was sort of a niche field, but now it’s everywhere. It’s very broad, so when you hear the words data scientists and Big Data, people use it in a variety of different ways and a very broad meaning.

“So, for some people, it can mean BI-related things. For us, we think of it more as traditional, really data-oriented, using predictive analytics, using mathematical modeling, optimization forecast — so new methods and old methods. So, forecasting had been around for a long time and machine learning is relatively new. It’s the scale in how people talk about things. [The need for data scientists] definitely has grown over time, and we really do see a big competition for it in terms of recruiting and a lot of customers asking for it.”

The path of machine learning

Bringing up the topic of machine learning, Gilbert wanted to know how far along we are to help the data scientists become more productive. Martin pointed out that machine learning is not the usually the first step in the process.

“Machine learning is one of those things we’ve definitely needed progress on. My sense is it also has become a bit of a buzz word, and the term is thrown around a lot. There are some places where machine learning is happening. In fact, we have a project that we have been working on for the past couple of months that will ultimately, down the line, use machine learning.

“You’ve got to start somewhere, especially with a customer who is trying to solve a very specific problem. You want to start by solving that problem and then branch out. And maybe machine learning isn’t the first thing you bring to the table, because it tends to be, technically, slightly more advanced than some other techniques. I do think it’s being used. I do think it’s a great tool to have in your toolbelt, and I think it can bring a lot to the industry. But I also think there is a lot of use of the term, and it’s not always what we think of it as in a more traditional sense.”

Thinking about data for the long term

Working with data means having the right data. Vellante inquired if data quality is a big part of Martin’s job. Martin looked at it from the customer perspective.

“Managing data and cleaning data for the purposes that we need it for is really difficult. We do spend a lot of time with customers. [The] customers who want to do a project, their question isn’t actually, ‘Can we work with you on a project?’ … their question is, ‘What data do I need to start thinking about now for this project and the ones I want to do in the future?’

“That, in particular, when you think about IoT [Internet of Things] that is a really hot question that we get from people. ‘What kind of things should I be gathering around IoT and thinking about IoT?’, so they have something to analyze down the road.”

Wrapping up the segment, Vellante asked if Martin sees any natural affinities across the industries the company serves. She sees opportunities across the company.

“One of the great things about our team is the fact that we work across industries. There’s a lot of science going on at Infor, not just at Dynamic Science Labs. There is a lot of science going on outside of just our team, and there is the ability to take one concept and bring it to another area … we definitely see those opportunities down the road.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of the Inforum 2016.

Photo by SiliconANGLE