Analyzing the Future

What kinds of programs should business schools develop to fill the growing need for data analysts and AI specialists?
Analyzing the Future

THE RISE OF DATA ANALYTICS is one of the hallmarks of 21st-century business. By the turn of the century, companies had been accumulating data in various transaction systems for several decades, and many desired to analyze the data to make better decisions. Their interest intensified in the early 2000s as they saw the great success of online firms from Silicon Valley, many of which were highly analytical.

In fact, during the mid-2000s, I conducted research showing that some companies were “competing on analytics”— that is, emphasizing their analytical capabilities as a key element of their strategies—and that those companies tended to outperform other firms in their industries. Information about analytics even made it into popular culture, especially through books such as Moneyball, which was also a successful movie. Both depicted the way the Oakland A’s of California built a winning baseball team through targeted data analysis.

These market forces rapidly led to a demand for analytical talent within firms. The first wave primarily focused on traditional analytical professionals and web analytics experts. But by 2010, online firms had started to realize they needed a new version of the analytics professional, someone who could analyze the massive amounts of data they had been collecting from online clickstreams, social media, and the sensors within internet-enabled devices (the “Internet of Things”). They needed professionals who not only had strong analytical skills, but who also could push the frontiers of the new technologies—many of them open-source programs—that were designed to store and structure data. They needed data scientists.

Not only did Silicon Valley companies hotly pursue data scientists, mainstream businesses also saw the need to bring them on board. In a 2011 report, McKinsey Global Institute researchers estimated that there would soon be a shortage of up to 200,000 data scientists in the U.S. alone—although there was never great clarity about what it took to become one.

Then, around 2015, companies became enthused about the potential of artificial intelligence (AI) to transform business, and this excitement sparked a new wave of analytics-related demand. Silicon Valley companies such as Google and Facebook again led the charge, incorporating many AI capabilities into their offerings, and other firms soon followed. I have helped conduct surveys that suggest that up to 25 percent of large companies in the U.S. have multiple AI projects underway. A few highly aggressive firms have launched more than 100 projects, some of which are entering the production deployment stage.

Not surprisingly, all of these firms are seeking data scientists or AI engineers who understand how to apply new technologies such as deep learning to business contexts. As before, these firms face what appears to be a severe shortage of individuals with this skill set. What kind of opportunity does this represent for business schools?


Business schools were swift to respond to the first wave of demand for professionals with analytics skills. Many quickly designed master’s programs in analytics by drawing from strengths in established disciplines, including statistics and operations research. They also brought in faculty from marketing, finance, and organizational behavior to show how analytics can relate to those specific business disciplines.

As a result of all this activity, master’s degrees in business analytics currently are among the most popular new offerings in business schools. North Carolina State University debuted what might have been the first master’s in advanced analytics in 2007; today, according to estimates from AACSB International, there are more than 400 analytics degree programs offered by nearly 220 business schools worldwide. An article on the Poets & Quants website declared, “Without a doubt, the business analytics master’s is the belle of the specialty degree ball.”

But business schools have found it somewhat more difficult to respond to the second and third wave by creating programs in data science and artificial intelligence. Not only is data science relatively new and difficult to define, it also has a strong computer science orientation. Unlike data analytics programs, which primarily focus on analytical/statistical methods, data science programs teach students complex programming methods for handling big data. For that reason, many data science programs are offered by schools of engineering, information, or computer science.

In fact, some universities have multiple analytics programs residing in various colleges within the institution. For example, the University of Minnesota and the University of California San Diego offer master’s degrees in both data science and business analytics. At Minnesota, the MS in business analytics is offered through the Carlson School of Management, while the MS in data science is run by the schools of engineering, liberal arts, and public health.

The University of California, Berkeley, has at least six different master’s degrees that are oriented to analytics. An MBA focus area in data science and strategy is offered by the Haas School of Business; the rest can be found within other departments and colleges, such as engineering, public health, and computer science. To help students navigate the many options, the university maintains a [email protected] webpage that describes the “rich ecosystem of data science-related research, teaching, and communities across campus.”

While data science degree programs have begun to proliferate, programs built around AI are still rare. Both Northwestern University’s engineering school and the University of Georgia’s college of arts and sciences offer master’s programs in AI, and both MIT and Carnegie Mellon University’s computer science schools have recently instituted undergraduate programs in AI. Several schools, mostly in Europe, also offer master’s degrees in artificial intelligence— these include the University of Gothenburg, the Free University of Amsterdam, and the Ecole Polytechnique in Paris. However, these programs are either contained within the computer science or engineering schools, or they draw from multiple schools within the university.

To my knowledge, no business schools in the U.S. have degree programs in AI. This is not surprising, given the paucity of faculty with expertise in AI. Technical faculty who are able to develop AI programs often have been hired— at very high compensation levels—by private sector firms such as Google and Facebook. Such faculty maintain university affiliations, but often not with business schools.


Because AI programs are so difficult to put together, most business schools will concentrate on programs for analysts and data science professionals instead. In my opinion, these professionals must acquire four different types of expertise to be competent in their jobs.

Quantitative and statistical skills are the foundation of any analytics role. Anyone holding such a job must be proficient at general statistical analysis—at least up to logistic regression analysis, often conducted in fields such as medicine and the social sciences to predict binary outcomes based on one dependent and one independent variable. Such professionals also should be able to analyze categorical data, probability and statistical inference, optimization, and basic experimental design.

If graduates want to be hired into specific industries or business functions, they also will need to master other quantitative techniques, such as lift analysis in marketing, stochastic volatility analysis in finance, biometrics in pharmaceuticals, and informatics in healthcare fields. Some types of analysts— those involved in “business intelligence” or reporting work—may be able to get by without substantial statistical knowledge, but this lack probably would limit their careers.

Analysts also must know how to use the software associated with their type of analytical work, whether it is used to build statistical models, generate visual analytics, define decision-making rules, conduct “what-if” analyses, or present business dashboards. These tools once were offered only as proprietary packages from vendors, but are increasingly open source today.

Data management skills are just as important to analytical professionals as statistical and mathematical expertise. Such professionals spend the majority of their time manipulating data—finding, integrating, cleaning, matching, and so on. And surveys suggest that the skill most commonly sought by data scientist employers is not expertise with a statistical program, but rather with SQL—a query language for data management.

Like analytics professionals, data scientists also perform data management tasks, but their tasks tend to be more complex and often rely on languages such as Python. Their jobs often involve taking relatively less structured data like text and images and creating the rows and columns of numbers that are well suited to statistical analysis.

The topic of data management also encompasses data privacy, security, and ethical issues. In fact, some master’s programs in analytics, including one from the University of Notre Dame, require students to take a course in ethics.

Business knowledge and design skills enable analysts to be more than simple backroom statisticians. Analysts need enough general business background to understand the problems and processes they are analyzing, so they should be familiar with marketing, finance, HR, and new product development. They also should know how analytics can be used to drive business value, and they must have insight into the opportunities and challenges their employers are facing. Many of these skills and content domains are taught in MBA programs, but most business analytics degrees don’t require a lot of traditional MBA courses.

Relationship and communication skills are vital to the success of all analytical projects. Analysts who can advise, negotiate, and manage expectations will be able to work effectively with their business counterparts to conceive, specify, pilot, and implement analytical applications.

These skills are critical when analysts must communicate the results of their work. They will need to share best practices with their bosses and colleagues, while emphasizing the value of analytical projects. Outside the business, they will need to develop working relationships with customers and suppliers. They also might have to explain the role of analytics in meeting regulatory requirements— for instance, an analyst might use data to help a utility company make a successful bid to increase its rates for service.

Analysts who can “tell a story with data” are highly prized. The ability to communicate effectively about analytics is the single most sought-after capability among graduates from analytics and business intelligence programs, according to one survey of employers, and some schools realize this. For instance, Communicating with Data is a required course within the new master of business analytics program at the Massachusetts Institute of Technology’s Sloan School of Management.

In addition to mastering these four sets of skills in the classroom, students typically participate in internships or practicums, during which they work on real analytical problems for real organizations. Students not only gain experience in what it’s like to be analysts or data scientists, they also get a chance to meet potential recruiters.

For schools, the challenge is combining all these necessary components into a single degree program. It’s particularly difficult to cover all the material because the typical master’s in analytics is only one year or three semesters long—hardly enough time to adequately train students who arrive at the program lacking basic analytics skills. It is probably more feasible for business schools to target students who already have undergraduate degrees in quantitative fields and provide them with “finishing school” skills in business and communication.

Another challenge schools face is assembling faculty with expertise in all the relevant areas. Some universities draw on faculty members from multiple schools to create their analytics programs. For instance, the master’s in advanced analytics at North Carolina State, which was intended to be cross-disciplinary from the beginning, involves faculty from the statistics, mathematics, bioinformatics, and computer science departments—and even the English department, because communicating effectively about analytics is so important. Such cross-disciplinary programs can be excellent for student learning, but are not always easy for schools to maintain.


There’s yet another challenge business schools face as they design their analytics offerings: how to set their programs apart from those of their competitors. Geography simply isn’t enough of a differentiator, because many programs are wholly or partially online, and others are offered at multiple locations. For instance, Notre Dame runs one MS in business analytics program on its main campus in South Bend, Indiana, aiming it at students right out of undergraduate programs—but it runs a second one in downtown Chicago, where it targets working professionals.

Instead of competing based on their locations, schools commonly differentiate themselves by the topics they focus on in their programs. For instance, some emphasize “applied analytics,” assuming that students will need a mixture of business and analytical expertise. Others are more quantitatively intense or give more weight to data science issues.

Going on the assumption that analytics generalists won’t be enough for some employers, other schools have focused their programs on functional specialties within the field. For instance, Pace University’s Lubin School of Business has an MS in customer intelligence and analytics, American University’s Kogod School of Business has an MS in human resource analytics and management, and Rutgers University offers a master’s in supply chain analytics. Stevens Institute of Technology and New York University both offer master’s degrees in financial analytics, while Babson College’s MS in business analytics aligns with the school’s entrepreneurship orientation by focusing on the intersection of analytics and entrepreneurship. Washington University’s Olin Business School offers multiple tracks in subject areas such as healthcare, supply chain, financial technology, and customer analytics.

In the future, I expect that more master’s programs will focus on specific analytical methods and tools, particularly AI, although specialist AI programs may remain within schools of engineering and computer science. At this point, it’s not yet clear what the role of business schools will be in the next wave of analytics programs.


What is clear is that technology is continuing to reshape the competencies that students will need to cultivate. Up until this point, analysts have needed the ability to select the right algorithm for the problem and data at hand. For example, if I’m trying to predict a binary variable using several continuous variables, I need to know that logistic regression is the method of choice. Whatever the algorithm used, I also need to know the process of analysis—such as how to choose independent and dependent variables, select data, deal with missing data, do the analysis, create some variable transformations to improve the fit of the model, and interpret the outcome.

Increasingly, however, these types of expertise are not required of human analysts. “Automated machine learning” programs can determine which type of algorithm is most appropriate; they also can perform most of the process described above with relatively little intervention by the data scientist. In other words, we are probably entering a “post-algorithmic” era in which detailed knowledge of analytical methods may no longer be necessary.

If this trend continues, it’s likely that the most valuable skills will be nontechnical ones—understanding the business, building relationships and trust, communicating analytical outcomes, and working in teams to convert analytics into action—the very skills taught in business school. If automated systems can eventually do most of the analytical heavy lifting, it may be that an MBA is the perfect analytics degree of the future.

Thomas H. Davenport is President’s Distinguished Professor of IT and Management at Babson College in Wellesley, Massachusetts, and a Digital Fellow at the MIT Center for Digital Business in Cambridge. He is also a senior advisor for Deloitte Analytics.

Read a review of his latest book, The AI Advantage.

This article originally appeared in BizEd's January/February 2019 issue. Please send questions, comments, or letters to the editor to [email protected].


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