While every company in the world is chasing down that all-knowing data scientist who will deliver the information for staying competitive, they're forgetting about one other requirement: The leader who understands how to put the data to work.

The need for business to invest in data science is a near certainty. Half a billion Google results will tell you that data has become the magic bean to bring opportunities to light, solve complex problems, maximize returns and keep customers  coming back. Yet, too few of those links will also disclose this shocker: Having access to data scientists or the right analytics in their possession doesn't mean companies will know how to act on what has been uncovered in the data. The reason? Executives don't really understand what their costly experts are telling them.

As a recent Harvard Business Review article put it, "Efforts fall short in the last mile, when it comes time to explain the stuff to decision makers."

Quoting a 2017 survey on Kaggle, a website where people can compete on machine learning tasks, HBR Senior Editor Scott Berinato pointed out that four of the top seven barriers faced by workers in the "data science realm" were related to this communication challenge: lack of management support, lack of a clear question to answer, results not being used by decision makers, and just having to explain data science to others. Data teams may know they've got "valuable insights," Berinato suggested, "but can’t sell them."

What's needed, says Parviz Ghandforoush, associate dean of graduate programs in Virginia Tech's Pamplin College of Business, is a new generation of "data-literate" leaders.

These are individuals, he explains, who have "grown up with information technology and digital tools" and possess the ability to work closely with the data experts, understanding how to talk their language, ask probing questions, and best manage these "scarce resources" to "focus on projects that are going to be beneficial to their companies."

These aren't the same as  "data  wranglers," the employees who prepare the data or write the algorithms, adds Barbara Hoopes, academic director for the MBA programs at Pamplin. Rather, they're "analytics translators," a term coined by McKinsey to describe people who can help their organizations achieve the impact they want from their analytics work. Hoopes, Ghandforoush, and their Pamplin colleagues are at the forefront of an initiative to rethink the traditional master's degree in business administration. The goal: to help the latest crop of business managers be prepared to lead in the data-infused organization.


The working professionals who pursue the Executive MBA at Virginia Tech get a rich education in the tried-and-true fundamentals of business — accounting and finance, marketing, operations, ethics, communications and leadership. But woven around those foundation courses are "experiential modules" designed to accelerate the students' development in four essential and current areas:

  • Business analytics
  • Entrepreneurship & innovation
  • Leadership & governance
  • Global business

Each module includes two concentration classes along with a "big experience" course that puts the learning to work immediately, says Hoopes.

Corporate leaders from some of the largest companies in the world are brought in, notes Ghandforoush, "to provide guidance and bring real-life projects to students." By the time they're done with three courses in that module, he adds, "they have learned not only about those topical areas but also how to use them in corporations. They have been able to tie it back to real-world scenarios."

What does this look like on the ground? For the global module, which examines international finance, management and  strategy,  a  recent  experience included heading  to  Germany  and  the  Czech  Republic  to consult with companies in auto manufacturing, chemicals, marketing and communications — each with varying degrees of sophistication  regarding their use of technology and analytics — to tackle specific business problems  they  were  facing.  (This  year's cohort of students will visit the China headquarters for multinationals and work on supply chain-related issues.)

For the  leadership  segment,  those  same  students learn about valuation and financial analysis as well as managing in a time of significant organizational upheaval  such  as  a  merger   &   acquisition   scenario. To  nail  down  the  concepts,  one  recent  experience had them explore what the next possible acquisition might be for Amazon, including development of full change management plans to ensure the value of the acquisition would work out. Those business ideas were presented to a panel of judges, including an Amazon Web Services executive, and Hoopes says she wouldn't be surprised to learn that the company will decide to act on at least one of those proposals.

For the analytics module, which covers BI  and  data mining  along  with   marketing   analytics,   Hoopes   took a lead from Gartner's famous annual "BI Bake Off." She brought in  four  software  vendors  —  Microsoft, Qlik, SAS, and Tableau —  all  of  which  provided  access to their products for the duration of the course.

Each student self-selected which product they'd work with and representatives from each company served as coaches to help them tackle "big, messy problems" to show what the software was capable of doing.

One student, for instance, chose Tableau because his own company had recently invested in it, and he wanted to work with the software "in the context of his education," she recalls. The students addressed pressing issues with U.S. infrastructure  using  publicly  available data to analyze Congressional airport funding and its relationship to economic growth, identify  causal  factors for large utility outages and predict hazard classifications of dams in order to prioritize inspections.


However, emphasizes Ghandforoush, learning how to work with data should never be sequestered in its own module or class. "I can't think of a single course or business problem where students don’t have to analyze a data-driven scenario. And conducting analysis requires a good grasp of data quality, magnitude and relevance to solve the decision problem.

“As a business executive, you have to look at such things as the balance sheet, manufacturing data sets, payroll information and advertising data for marketing. These are all data-rich problems requiring careful attention to detail."

"These executives-in-the-making really need to understand how data can be used to support their decision-making," Hoopes asserts. That means "hearing the voice of the data" and "learning how to tell a story that convinces others" — in other words, traversing that last mile between the data scientist and the people at the very top.

Those aren't skills just anybody can learn, insists Ghandforoush. Students often arrive with an expectation that the data work they will do in pursuit of their MBA is a throw-away "because they don't need it or they have analysts or others at work who will do  this  for  them." And yet time and again faculty will hear back from those same former skeptics that those lessons turned out to be the most valuable in the program "because they're actually using it at work and they've seen the results."

That's just what Virginia Tech had in mind when it undertook its redesign of the MBA for working professionals. "It's not like students graduate and four or five years later we will hear if they have benefited from their MBA education," he concludes. "This is like a laboratory. We get to watch this as it's happening right before our eyes."

- Written by SmartBrief Education