There’s something missing in the way that many higher education institutions educate students about the intersection between technology and business. The problem: they often don’t. There just isn’t enough focus on getting those people who are studying the science – and art – of working with data to also work in tandem with business peers to help deliver to business objectives.

That disconnect hurts companies that today are bemoaning the lack of qualified data professionals to help them reach business goals.

The big problem is that data pros can’t live solely in IT or business functions – they can’t exist in their own silos. “There has to be a good blend,” said Asha Saxena, founder and CEO of Data Management consulting company FutureTechnologies Inc., President of Healthcare Analytics company Aculyst, and Adjunct Professor at Columbia University. She teaches Big Data, Consulting, and Entrepreneurship at Columbia, with a focus on how to build data and drive businesses by using that data – the approach she has taken in her own career.

That isn’t the way all colleges and universities look at things though. “Look at many schools’ Data Science courses and do you ever see a mention of industry expertise there?” she asked. “Never, nowhere,” she answered.

So, a data professional’s scope of learning might be narrowed to highly technological processes, with their education focused on things like how to write the best query or determine how well a model is working. “But it’s not about just that,” she said, as that’s seeing only a small part of the bigger picture. “It’s about how well the model can serve the purpose, can solve the business problem.” The models they build must mean something to the business and how, in fact, the business is impacted by that meaning.

That means it is time for universities and colleges to put a premium on building leaders in all facets of Data Science, and Data Management in general, including its relationship to specific industries. No one should be put in the basement to work only on technology functions, and from there, “give us [the business] magic.”

Data and Business: They Go Together

As an example of the magic that can happen when tech and business functions combine expertise, Macy’s provides an interesting use case. Saxena said that its Data Science team used company data to discover that placing products in certain ways on store floors could improve sales. But that information didn’t translate into improved retail store results because of the lack of connections between the Data Science and business teams.

To get the word out, the data scientists went to the strategy group and told them that their department needed to sit under the strategy group rather than IT. With data scientists now having a real connection to corporate strategy, they were also able to communicate their findings about Macy’s ecommerce efforts, which resulted in the company funneling more money to its online strategy vs. opening more retail stores. “Going online made more money than opening retail stores,” said Saxena.

When data scientists have been educated to spend their work time playing with numbers and models that have no connection to the industry they want to work in, it’s not surprising if their work doesn’t translate well to delivering on a company’s business opportunities.

Data scientists may not have enough of an education about retail, for example, to understand that they can use data analysis to discover that men going out to buy diapers at their local supermarkets also are more likely to buy beer at the same time, because the two products are either in the same aisle or quite close to each other. “You wouldn’t know that unless you knew the retail business and how aisles were situated,” Saxena said.

Who’s Got the Right Stuff

When it comes to businesses seeking the data talent they need to reach goals, they must first get a grasp on how applicants have been educated and what they bring to the table. It’s okay to be a technologist or a business analyst or both, she said. But since the term “data scientist” can be very fluid, it’s the business’ job to determine how those talents might come together in the same individual or between individuals to support business needs.

“Some might understand a data scientist as a tech person writing models but not someone who understands the business, or a business person who doesn’t understand how to write a model,” she said.

And that may be true depending on how individuals were educated. “So, either you are looking for two people who can work together or one person who understands business and technology and knows how to combine that.” The latter is a trickier proposition, given the way schools tend to veer their Data Science education to more technical aspects. 

But even before that, business leaders should understand that just because everyone is talking about Data Science, it doesn’t mean they should start their own initiative without having a reason for it. Unless there is a specific project to be implemented and specific resources identified to get it done, it’s not appropriate to start weeding through applicants just yet.

Bring Continuing Ed into the Game

If a business finds that its analytics efforts are being hurt by the fact that data scientists haven’t been taught to understand industries – and at the same time that business analysts aren’t necessarily educated in how Data Science can boost their requirements needs – it can undertake its own efforts to bring the two teams together.

It’s an important part of growth to make the team successful by continuous learning, so everyone gets to know each other’s roles and how to exploit their expertise together. “They should be given a week off to take a class, but many companies don’t send people outside to do that,” Saxena said. 

This is the first of three components that Saxena identifies as critical to continuing education in this area. The second is to learn and then come back and put that learning into practice. The third is accountability among the tech and business experts, as they work together to check and recheck their practices. As for the employer, it’s important not to inadvertently set these teams against each other, by providing higher compensation to one than the other.

When she talks to organizations, she extols the value of creating learning opportunities to successfully pair Data Science technology with business analytics. Her consulting firm offers a program to help on this front, with a two-day workshop that brings tech and IT talent together.

“In the workshop I spend time on bringing the two together and working together on one project,” she said. That involves a planning session to pick a project with high priority and to get started working on it as teams. “Then every quarter we go in to make sure we are continuing that progress,” she said.

Consultants also check in a couple of time each month to target and resolve issues. Having a third-party overseeing efforts fosters the sense that data and business staff are accountable for what they do and don’t do, and encourages greater responsiveness than might occur between internal groups – at least before they perfect their ability to cooperate together. Also, external consultants coming in as outside advisors can see things that internal staff might not see or get teams to talk about issues that they haven’t openly addressed.

Last Piece of Education Advice

A student may not be getting both sides of the data and business connection as part of their core studies in a discipline, but they can look for other scholastic opportunities in school.

“Start your journey early on when you are in college,” she said. “Be self-aware and apply your passions to your studies.” That way, you’ll be better positioned to support both Data Science technical functions and the business drivers that should frame that work. Ideally, students will pursue that course in the context of their own industry passions.

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