Quiet storm brewing outside ivory tower

All of our MBA students at the University of San Francisco are required to take a course entitled “Spreadsheet Modeling and Business Analytics” [1] that has become very popular. A few years ago, students began to clamor for more analytics courses to follow up on this one.

In response, my colleagues and I began to create an ad hoc collection of electives on topics that were interesting to us (business statistics, data mining, data visualization, data strategy). Given that the vast majority of our MBA students do not have technical backgrounds, we have avoided requiring the students to write code, opting instead for user-friendly tools such as Excel (the workhorse tool for MBAs for more than a generation), JMP and Tableau.

Over time, these electives have grown in popularity. Recently, the administration took notice and encouraged us to “package” our courses into an MBA specialization that could be presented to the outside world as a coherent program of study. This is when I suddenly became nervous. Implicitly or explicitly, we were about to start making a promise to students to help them prepare for specific types of careers – but since I did not know what the profile for those careers looked like, I also lacked an honest and rigorous way to examine how well our electives were meeting this goal.

To deal with my newfound anxiety, I reached out to many of the students who had taken our electives over the past few years. To my surprise, I found that their careers had ventured into a wide variety of industries and a diverse set of functional roles, ranging from financial analyst to technology consultant. All of them agreed that their experience in studying analytics during business school had been professionally valuable, but there seemed to be myriad explanations as to how and why.

Brian Liou helped me understand my data more clearly. Liou is CEO and co-founder of Leada (www.teamleada.com), a Y Combinator-backed Bay Area start-up focused on developing data training software and services. Liou spends a lot of his time talking to business executives – and business faculty members – around the country to understand the kinds of data skills that today’s business professionals need.

I had met Liou through a former student of mine, and recently decided to use Leada’s excellent online tutorials on SQL, R and Python as part of a popular new elective in our business school called “Coding for Analytics.” During a recent conversation, Liou proceeded to explain why so many of my MBA students were so eager to take this class.

To begin with, he pointed out data analytics groups have (under various names and charters) been around for quite a long time in most industries. Over time, functional groups such as marketing, finance and operations have come to lean on these groups for their data needs, ranging from routine reports to ad hoc “deep dives” to answer specific questions.

However, as executives have become more aware of how valuable predictive and prescriptive analytics can be, there has been more focus by (and much more pressure on) such groups to not only provide operational support to different parts of the enterprise but also to deliver its own data-driven business insights and innovations. I nodded at this part of his explanation, as it was consistent with the findings of a recent study that Jeanne Harris and I had conducted [2].

As our conversation continued, I slowly began to realize that just outside my ivory tower office, drowned out by the relentless buzz associated with “big data” and “data scientists,” there is an equally powerful storm taking place, albeit one that is much quieter. Advanced analytics are indeed being recognized as being more valuable than ever. And demand for high-end analytic talent far exceeds the supply, which means that highly skilled analytic specialists remain rare and are increasingly more expensive. Meanwhile, the pressure from management on all departments to quickly make better, more data-driven decisions is putting stress on business analysts of all stripes – marketing analysts, supply chain analysts, operations analysts, financial analysts – to access and effectively utilize data more quickly and creatively than ever. While the proliferation of business intelligence software has helped to automate much of the repetitive data manipulation, analysts nevertheless often need to get their hands on a more customized chunk or summary of data to answer a constantly growing list of questions whose answers have not yet been codified.

So, Liou explained, analysts can put in requests to a centralized group and expect to wait days or weeks or longer for the desired data. Alternately, they can try to figure it out by writing their own code to access the data (usually via SQL) and analyzing it themselves. Given these two choices, many analysts were increasingly keen to try their hand at writing basic queries and analyses, simply because they lacked the time and patience to wait for someone else to do it for them. Think of this as a choice to learn how to fish rather than waiting around for someone to perhaps someday give you a fish when if they get a minute.

“Speed to decision-making is what is making these business professionals require a different skillset,” Liou said. “And we’re also seeing demand for data visualization skills and a better understanding of how to design experiments.” Liou was also quick to point out the importance of teaching business students and business professionals relevant technical concepts and tools within the context of their job functions. “With us, regardless of whether you are learning coding skills or analytic techniques, it is very case-based and always incorporates business data and language.”

Teaching analytic concepts and tools within a business context is something we have long been doing with our MBAs at USF, so this final point also resonated with me. And Liou’s overall summary – better decisions at faster speed + overworked specialized resources = increased demand for business analysts with a modicum of technical competency – really helped give me a much better understanding of why enrollment in our MBA electives are rising.

Final thought: Given that these basic technical skills are an increasingly large part of so many analyst jobs, how long until basic programming skills become a required part of every MBA program? I suspect that day will be here sooner than we think.

Notes & References

1. To learn more about this class, see http://dx.doi.org/10.1287/ited.2015.0149
2. http://sloanreview.mit.edu/article/getting-value-from-your-data-scientists/