How MBA Programs Use Big Data to Turn MBAs into Data-Savvy Managers

As more companies look to incorporate big data into their business strategy, the more we find its inclusion, within data science, in the MBA curriculum. While much has been said of the need for skillsets offered by STEM subjects, there’s also a demand for data-savvy managers. Read on to learn how UC Berkeley-Haas, the University of Georgia’s Terry College of Business and NUS Business School are using data analytics coursework to prepare their graduates for the workplace.

Why are more business schools adding data science and big data to the curriculum?

Big data is becoming the standard in business, which is one of the main reasons why more business schools are adding data science and big data to the curriculum. Berkeley-Haas lecturer, Gregory LaBlanc, feels that big data is gaining a foothold in MBA programs because it is, “permeating every aspect of business.” This includes functional areas from finance and operations to marketing – areas that LaBlanc says have all been, “fundamentally transformed by developments in big data.”

One of the reasons more businesses are embracing big data is because data-driven decision making results in bigger profits. “Organizations have discovered that data-driven decision making has become more profitable. The research has come out in recent years to support this and it’s a trend that companies are adopting,” explains Rick Watson, a management information systems (MIS) professor at the University of Georgia’s Terry College of Business.

Adding big data to the curriculum is also another way in which business schools are responding to changes in the technological and business landscape. “The explosive growth of the internet, and the proliferation of smart devices, cameras, microphones, sensors, RFIDs and so on, has led to the tremendous growth and affordable, easy access to large quantities of fast-moving, unstructured datasets, commonly referred to as big data. Aided by the data and a host of new technologies, managers are able to glean strategic, tactical and operational insights that yield quicker and more effective business decisions,” states Ashok Charan, associate professor in marketing at NUS Business School.

There’s also student demand for big data, as evidenced by the fact that LaBlanc’s data science and strategy class is usually full. “It’s not so much that the students’ bosses are telling them to learn data science. They know that this is the way of the future,” explains LaBlanc. 

How data science is Incorporated into the curriculum at UC Berkeley-Haas

At Haas, the data analytics curriculum begins not with electives but the first course of the MBA program – ‘Data Driven Decision Making’. In this class, LaBlanc says that students learn how to use data in all aspects of decision making, as well as statistical techniques and how to make inferences based on the data in their possession.

A further data analytics course at Haas, ‘Data Science/Data Strategy’, teaches students how to incorporate data into their business strategy. “The class is not designed to convert our MBAs into data scientists,” states LaBlanc. Instead, the goal of the class is to give students enough data science knowledge to be able to incorporate it into their business strategy and know how to manage teams of data scientists in order to enhance corporate capabilities. The first part of the class covers techniques while the second part covers applying data science to corporate strategy. The latter part of the class features industry speakers from both traditional businesses, such as Wells Fargo, UPS and PG&E, and technology companies, such as Cloudera, LinkedIn and Facebook, to discuss ways to apply data science across different functional areas and industries.

Haas’ third major data science course is an elective in which students receive a big data set from a company and work in teams to solve a real problem for a client. Half of the students in the class are MBAs and half are data scientists from other schools at UC Berkeley.

Data science is incorporated into other Haas classes as well. For example, the ‘Analytics for Workforce, Workplace and Wellness’ class looks at how workplace incentives can be designed on the basis of analytical data. In the past, companies lacked empirical evidence as to how workplace incentives like yoga classes or meditation rooms helped boost productivity. Big data is now transforming HR, however, since there is now evidence coming out of the field about the effectiveness of workplace incentives. Companies like Google are paying attention to this data, for the simple reason that the quality of environment and culture a company can offer has emerged as a point of competition for the most talented hires.

In addition, LaBlanc feels that data science has transformed several of Haas’ core courses, such as finance and organizational management. “It’s not just about these courses that have the word ‘data’ in them. It’s about the approach.” Eventually, LaBlanc thinks the day may come when data strategy disappears as a specialization, because it will be a standard that is incorporated into most courses.

Data analytics courses at Terry College of Business

Terry College of Business at the University of Georgia encompasses a data analytics class in its core curriculum. The course covers data modelling and SQL (structured query language) in order to give students an understanding of how databases are designed as well as how SQL is used to query databases. Watson includes SQL as part of the course because it is, “still very much a commonly used language for extracting data.” The course also discusses data management using the R language, a broad programming language which can be used for data visualization and manipulation, text mining and dashboards.

‘Energy Informatics’, also taught by Watson, is another course available at Terry that incorporates analytics, looking at how information systems can be used to improve energy efficiency. Other data analytics courses available to Terry MBAs include ‘Business Process Management’ and ‘Business Intelligence’.

One of the major focuses of Terry’s data analytics research and coursework has been digital data streams (DDS). Unlike historical analysis which is focused on gathering big pools of data and storing them in a warehouse, digital data streams allow you to tap into a stream of data before it gets to the warehouse. “If you really want to accelerate decision making, you want to tap into the data as it’s generated,” states Watson.

One example of digital data streams being used by businesses could be a car insurer who monitors the weather. When an upcoming hail storm is detected, the company can go into its database, pull up a list of customers living in the affected area and send them a text advising them to cover their car and protect it from the coming storm. This use of analytics saves the customer from having to deal with the hassle of getting their car fixed and saves the insurance company the cost of a claim.

Data analytics at NUS Business School

NUS Business School’s approach to data analytics has been to change pre-existing courses more often than to create dedicated data courses. “There are a few new courses but the key difference is that the content of existing courses has been altered and enhanced to reflect the new environment,” states Charan.

NUS Business School’s marketing courses have been changed in order to bring in analytics. Some NUS courses have also been renamed to reflect these changes. For example, Charan teaches a course called ‘Market Analytics’, formerly known as ‘Market Research’, and says that it now, “encompasses well-established, conventional research and analytics as well as newer technologies.”

In order to provide students with direct experience of working with analytics, NUS Business School uses a marketing simulator called Destiny. This simulator is used across most of NUS Business School’s program offerings, including the MBA, EMBA and MSBA (Master of Science in Business Analytics) programs. The simulator is said to allow students to, “become proficient in the use of market knowledge and financial data for day-to-day business decisions.”

NUS Business School also has several initiatives focused on staying ahead of big data initiatives in order to better prepare students for the workforce. One of these is the Business Analytics Center (BAC), the product of a collaboration between IBM and NUS’s business and computing schools. BAC offers the university’s MSBA program which Charan says has become, “hugely popular,” since its launch three years ago. Charan feels that the master’s program, “fills the vacuum that once existed in the area of specialized business analytics, and is very tightly connected with the industry.”

Applying data analytics to the workforce

“The market has a high demand for people who can do analytics,” reasons Terry’s Watson. Since a data analytics class is part of Terry’s core curriculum, all of the school’s MBAs can take knowledge from the course into their summer internships and many will get valuable work experience with analytics during this period.

Watson says that many of the school’s MBA graduates get jobs that involve data analysis because there’s a high demand for employees with SQL and R language skills. This is evidenced by the fact that Watson received several ‘thank-you’ emails this summer describing how students are applying the concepts learned in class to solving real business problems. “Employers want graduates who can actually do something, not just talk about generalities,” Watson explains.

At NUS, the MSBA graduates are the ones who land jobs which directly involve data analytics, but most of the business school’s management students will work in jobs where it is important to have a general understanding of analytics. “Rather than get soaked in the technologies and the analytics processes, the business students need to know how to interpret and apply the knowledge, and feel comfortable in taking business decisions based on this information,” states Charan.

While IBM, Coursera and Microsoft have data analytics programs designed for data scientists, LaBlanc feels that the real challenge will be finding data-savvy managers. He also feels that, contrary to what McKinsey said in its 2011 report, there are solutions to the problem of finding data scientists, with for-profit companies like General Assembly and Galvanize, “creating data scientists by the thousands,” through courses and other offerings. In addition, a lot of the work data scientists do can be automated through machine learning. “The real bottleneck going forward will be in data-savvy managers. That’s where the real premium is going to be. If you’re a data-savvy manager, you’ll be able to add so much more to the organization than someone who only has general management skills,” states LaBlanc. General management skills are still important, however, since data-savvy managers need to redesign the culture of an organization in order to embed big data into what he refer to as the, “corporate DNA.”

One MBA who is using his business school education to become a data-savvy manager is Jason Lars Bergquist, a part-time MBA student at Berkeley-Haas who works full time in the customer experience group of video game company, Electronic Arts (EA). Bergquist says that the data science courses he has taken at Haas have given him an understanding of what data can be used for. “The most important thing is to understand what data science can and can’t be used for. If you don’t spend a lot of time specifically thinking about it, it’s just a black box where it’s hard to understand what the real benefits are.” While a PhD is needed to carry out the highest level of analytics work, Bergquist says that a manager still needs to be able to ask the right questions, understand the approach and know when analytics tools are going to be useful.

Bergquist currently uses analytics at his job to identify customers’ usage patterns and the monetization aspects within EA’s games. He is working with the company’s customer-service group to draw conclusions about customer sentiments, such as where they are having pain points. The information gleaned from this data analysis will be used to improve the gaming experience for EA’s customers. EA is looking at using naïve Bayes, a technical method for analyzing text and speech data that Bergquist learned about as part of his Haas coursework. Control charts are another thing Bergquist has learned about in class and that he now uses in his everyday work in helping to understand the scope of a problem within EA’s games. The high-level strategy information Bergquist has acquired from attending lectures by speakers from companies like Google has also been incredibly valuable. “Talking to people with experience and thinking about data analytics on a day-to-day basis is incredibly powerful for recognizing the utility of these tools and understanding how to administer their use and execution.”