B-Schools Respond to the Data Revolution

As schools integrate data analysis into new and existing programs, it seems likely that every student soon will need to know this critical business skill.
Deriving Meaning from Data

Human beings have become incredibly proficient at generating vast amounts of data. In fact, more than 90 percent of the data that exists today has been created in just the last three years, according to “Data Never Sleeps 6.0,” an annual report from the cloud computing company Domo. Each minute, human beings post 49,380 Instagram photos, share 473,400 tweets, conduct 3.87 million Google searches, and send nearly 13 million texts. By 2020, the company estimates that 1.7 MB of data will be created each second for every person on the planet.

But while humans are adept at producing data, they are less adept at mining it for insights, says Kaijie Zhu, director of the MSc in business analytics and associate professor of the department of decision sciences and managerial economics at the Chinese University of Hong Kong (CUHK) Business School. “It has become a pressing and unprecedented challenge,” he says, “for organizations to compile business data, derive useful information from the data, and utilize the information intelligently.”

As organizations rely more on data to drive their decisions, they’re setting the expectation that all students, regardless of specialization, will know how to turn data into solutions for business and society. And this expectation is shaping the business curriculum in pervasive and permanent ways.


One of the first schools to offer a master of science in business analytics was the McCombs School of Business at the University of Texas at Austin, which debuted its program in 2012. The number of applications to its MSBA has increased from 400 in 2012 to 900 so far for 2019–2020, filling a cohort of 65. “We have near 100 percent placement of students who are looking for jobs,” says Prabhudev Konana, associate dean of instructional innovation.

CUHK also has seen a surge in applications to its MSc program in business analytics, from 1,000 in 2016–2017 to 1,600 in 2017–2018. This increase has prompted the school to double the size of the cohort from 60 students to 120 students. And at Melbourne Business School (MBS) at the University of Melbourne in Australia, applications to its master of business analytics program have gone from around 350 four years ago to 1,100 (and counting) for the next run of the program this year. That surge has prompted MBS to nearly double its cohort size from 32 to 60; in addition, last year the school added a new master of analytics management to its portfolio.

“We don’t want our MBA programs to be technical programs, but we need to improve the extent to which they have technical components.” — Ujwal Kayande, Melbourne Business School

MBS faculty are now looking for ways to integrate more analytical components into the school’s MBA programs, says Ujwal Kayande, an associate dean and director of the Centre for Business Analytics. “We don’t want our MBA programs to be technical programs,” says Kayande, “but we need to improve the extent to which they have technical components.”


Business schools aren’t just adding new courses in analytics—they’re launching multiple master’s programs, adding concentrations and certifications, and opening new centers. Some universities even are dedicating entirely new colleges and divisions to the subject. (See “Higher Ed Makes Big Moves with Big Data”.)

At some business schools, recent additions to the curriculum are intended to reach students outside technical disciplines. Here’s just a sampling:

Last March, the Asian Institute of Management in Manila, the Philippines, opened [email protected] (Analytics, Computing, and Complex Systems), a data science and advanced analytics laboratory, in conjunction with the launch of AIM’s new MS degree in data science (MSDS). MSDS students and faculty work with data scientists on projects focused on sparking growth and improving conditions in emerging economies in the Asia Pacific, says AIM’s dean Jikyeong Kang.

The lab houses a supercomputer donated by the Stan Shih Foundation—Shih is co-founder and honorary chairman of the Taiwan-based computer and electronics company Acer. Equipped with a 500-teraflop system, the supercomputer has the capacity to handle 500 trillion calculations per second; it has 500-terabyte storage capacity and a large graphics processing unit farm that is “optimized for artificial intelligence,” says Kang.

Starting in the fall of 2018, all undergraduates at the University of South Florida’s Muma College of Business will pursue the school’s new Citizen Data Scientist certification. The certification is coordinated through the school’s Center for Analytics and Creativity, which partnered with data visualization software provider Tableau to design the program. Departments throughout the college worked together to determine which seven existing courses would include relevant assignments. Individual instructors can determine the specifics of the exercises, as long as each assignment requires students to analyze a relevant data set, produce data visualization graphics, and create videos to highlight their methods.

All students enrolled in the master of science in accountancy (MSA) program at Wake Forest University’s School of Business in Winston-Salem, North Carolina, now will learn data analytics skills in their core courses. Last July, the school hired Deloitte’s Tom Aleman as a professor of practice in accounting analytics, so that he could help the school integrate data analytics into MSA courses. Aleman was formerly Deloitte’s U.S. national and global leader of analytics and forensic technology services.

Students now will take four analytics courses throughout the two-semester program. The first, Introduction to Analytics, establishes concepts in data analytics, big data, visualization, and presentation. The second course teaches techniques in data wrangling, which refers to the process of requesting, extracting, and cleaning up imperfect data from different systems and applications. “In the real world,” says Aleman, “accounting professionals aren’t usually handed a nice clean data set. Data fields change over time, systems are updated, and legacy data is often left in its original form. This leads to inconsistencies, gaps and potentially lost data.”

The third course teaches students to master data visualization software such as Tableau and Alteryx, the science of visuals, and the ethics of visualization. The final course focuses on data communications and presentations.

The program closes with a capstone course in which students will work on projects for companies. Students will learn that analytics is “not just about providing a clean spreadsheet to a manager,” says Aleman, “but asking the right questions, solving the business issues, finding the insights, and communicating them to inform the organization’s decisions.”


While every school has its own approach to teaching analytics, most programs rely heavily on industry partners to supply students and faculty with real-world projects and data sets. Most programs integrate short-term projects throughout the curriculum, before culminating in capstones that put students to work on projects for corporate clients.

When the McCombs School launched its MSBA, it did so with support from Walmart and Deloitte. The school has since added new partners, including Southwest Airlines, Charles Schwab, General Motors, Dell, EOG, USAA, HomeAway, Indeed, and 3M, and practitioners from the companies sometimes teach classes in consultation with research faculty. These partners have asked students to solve a range of data-driven problems, from identifying factors that make managers successful to helping companies optimize their employee rewards programs.

At ESSEC Business School in Paris, France, faculty and students in the Strategic Business Analytics Chair program work with around 15 corporate partners. However, the company playing the biggest role in the program is the global management consulting firm Accenture, which provides funding and coaching for students. The company also connects students with its executives and invites them to its Paris headquarters to work with data on its internal cloud-computing platform.

“We cannot teach data analytics from an academic textbook or a 20-year-old data set in an Excel file as we did ten years ago.” — Jeroen Rombouts, ESSEC Business School

“The most interesting data are within companies,” says Jeroen Rombouts, ESSEC’s head of the information systems, decision sciences, and statistics department and the Accenture Strategic Business Analytics Chair. “If we don’t collaborate with private companies that are willing to share their data, we cannot teach students. We cannot teach data analytics from an academic textbook or a 20-year-old data set in an Excel file as we did ten years ago.”


Although many analytics programs have been in place for only a short time, they already must adjust to the changing needs of industry. ESSEC, for example, has directed more content to the use of artificial intelligence, in areas such as natural language processing and recommendation engines.

Carnegie Mellon University’s Tepper School of Business in Pittsburgh, Pennsylvania, recently added two core courses on machine learning to its MS in business analytics program. “To make an impact in practice, students need to go beyond simply running a script in Python or R,” says Willem-Jan Van Hoeve, the Carnegie Bosch Associate Professor of Operations Research at the Tepper School.

Tepper’s corporate partners have made it clear that they aren’t looking for employees who only know how to code, Van Hoeve emphasizes. They want to hire graduates who can identify business opportunities presented by data sets and determine the best analytics methodologies to apply to business problems—who are able to interpret outcomes and communicate results.

“That is why our program includes not only technical courses, but also classes like communication, managing teams, and business fundamentals so that our graduates can both crunch the numbers and make strategic recommendations,” says Van Hoeve.

At AIM, MSDS students are strengthening their understanding of data’s impact on nearly every aspect of human life, says Kang. That’s why they work on a wide range of projects, from predicting global child mortality rates to studying hashtags on Twitter to spot commonalities among social media users. “Our program,” says Kang, “emphasizes the importance of thinking beyond data.”


While languages such as Python and R are widely used today, that doesn’t mean they will be in a few years. That’s why business schools are preparing students to work with programming languages that might not yet exist, say these educators. “Our program is tools agnostic,” says Konana of McCombs. “That means we focus on deep skills in methods and solve problems with various tools and skills, including R, Python, SAS, SQL, Hadoop, and Matlab.”

“We don’t want students to be dependent on one computer language, so we make sure they understand the underlying logic of programming,” agrees Kayande of MBS. “While each programming language has its idiosyncrasies, they all are essentially the same.”

Analytics programs also are incorporating content that teach more than just the technology. AIM, for example, covers topics such as data governance and data privacy in its Introduction to Data Science course. With the European Union’s enactment of the General Data Protection Regulation last year, students at ESSEC now are learning about new roles emerging in many companies, such as that of data protection officer. “Students need to know what the legal and ethical boundaries are when they use data generated by individuals,” says Rombouts.

Determining the ethical boundaries surrounding the collection, management, and application of data could be one of the most important skills students learn in their analytics education, says Kayande. At MBS, for example, companies have expressed increased concern over the ethical use of data. This concern has led faculty to bring in guest lecturers to speak on the issue, as well as ask students to write essays in response to different ethical scenarios.

How to teach the ethics of data is still an evolving part of the analytics curriculum at MBS, says Kayande, who stresses that no matter how adept students become at using data as strategic tools, they will need a strong moral compass to ensure that their decisions do no harm. “They are going to have incredible power in their hands,” he says. “They will have to decide where to apply data and where not to, and if their companies are encouraging them to misuse that power, it will be their job to push back. Employers like the fact that our graduates push back and aren’t afraid to have those difficult conversations. We tell our students, ‘At the end of the day, it’s going to be on you.’”

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