By Bill C. Hardgrave, Dean and Wells Fargo Professor, Raymond J. Harbert College of Business, Auburn University
I had a conversation with a business school dean about eight years ago—long before things like cloud computing and big data were part of our everyday vernacular—regarding the use of data, how important it was for all business students to understand data and, most importantly, how to make decisions with data. This dean thought the idea of a tidal wave of data from a variety of sources and the corresponding need for data analytics/business analytics/data science (or one of the many labels used at the time) was a fad and would be a non-issue in a few years. Hindsight is always 20-20 and, looking back, this notion of what we now call Big Data and Business Analytics as being fads was completely wrong. Data—and its importance in business—are not going away. On the contrary, it is growing every day.
The idea of big data is not only about volume. Make no mistake, volume is a major factor. It is estimated that 2.5 exabytes of data is being produced every day. (An exabyte is one billion gigabytes or, a 1 followed by 18 zeroes!) To put this in perspective, in 1986, the world's digital storage capacity was estimated to only be around 2.6 exabytes. According to the website TechCrunch, every two days we create as much information as we did from the beginning of time through 2003.
The idea of big data is also about variety. We've moved from a world where data primarily comes from within organizations, to one where much of the data comes from outside the organization—the daily weather forecast, Twitter feeds, Facebook posts, news feeds, RFID tags, temperature sensors, etc. And, most importantly, we've moved from internally controlled, proprietary, structured data primarily residing in databases to unstructured data that may or may not be valid or reliable.
Finally, big data is about velocity. Remember the good ol' days when organizations ran monthly or quarterly reports as a way to look back at what happened? Today's data is instantaneous. Organizations can't afford to wait until the end of the month (much less the end of the quarter) to gather and use the data. Much like my teenage daughter, organizations expect access to data 24/7/365. My daughter—the next generation—has grown up in an environment where the velocity is mind-numbing.
Why a Business School Curriculum Must Include Business Analytics
If you buy into the premise that big data—lots of different types of data coming at you at blinding speed—is here to stay, then keep reading, because this is the important part: Big data doesn't mean anything if you don't do something with it. How a company uses data will separate the great companies from the good companies. Similarly, for our students, the differentiator in the workplace is no longer what you know; rather, it is what you do with what you know. This is why we must prepare our students for a big data world—and this is where business analytics comes into play.
Business analytics is a term that was made popular by Davenport and Harris's 2007 book, Competing on Analytics: The New Science of Winning. Davenport and Patil recently noted in an Harvard Business Review article that "the advent of the big data era means that analyzing large, messy, unstructured data is going to increasingly form part of everyone's work." Note: everyone's work. Thus, I would advocate that every business student needs to know something about business analytics. Furthermore, we need to develop specializations in business analytics in response to the demand from industry—a demand that I don't believe will go away.
In this limited amount of space, I cannot fully explain the field of analytics. To get an appropriate understanding of business analytics, watch the movie Moneyball. It epitomizes the concept of analytics in a particular setting that most can understand, and drives home a very important issue—it is not data, per se, that matters. It is how one harnesses the data that matters.
Where are companies getting their business analytics talent? Currently, they are forced to grow their own. They will take an IT person and develop the analysis and interpretation skills. Or, they will take a statistician and develop the IT and interpretation skills. Or, they will take a good business analyst and develop the analysis and IT skills. You get the picture. Here is our (business schools) opportunity: we must respond to the needs of industry to prepare our students for a big data world with the proper business analytics skills.
The January/February 2013 issue of BizEd magazine reported that "data analytics is expected to create 4.4 million jobs worldwide by 2015, but the skilled workers available will fill only one-third of those projected openings." If we don't produce them, who will? Likely other disciplines on campus, but they will be missing a key ingredient—the business knowledge necessary to make better business decisions.
The Way Forward
In developing our approach to business analytics, we followed a few basic tenets: (1) All business students should know something about business analytics; and (2) The study of business analytics should cover three important areas—big data, analyzing data, and interpreting/making decisions with data.
To accomplish this, we first changed our core business curriculum to include six hours of business analytics by replacing our statistics classes. Now, before the nasty emails start coming my way—we do believe statistics is important. However, the analysis of data is only one part of business analytics—gathering data and interpreting data are important as well. Thus, we broadened our courses to include all three elements. Heretofore, I think we have missed the target on how we deliver the analysis portion. Ask your students how often they have used their statistics course or calculus course knowledge in other courses. It's not that we don't do a good job of teaching p-values and confidence intervals. Where we experience a disconnect is in the context—most statistics are not taught in context. Students don't know when to use the techniques they were taught. Our philosophy is that we should be teaching what a p-value is, what it means, and when and how to use it. I have a power saw in my workshop at home—I don't need to know how to build a power saw to know how to use it to build a bookshelf. I simply need to know when to use it and how to use it.
Second, we believe all major business disciplines should include analytics: marketing analytics, human resource analytics, supply chain analytics, financial analytics, etc. Not everyone will (or should) be a business analytics major. But every discipline should have a good understanding of business analytics, broadly, and in their discipline, specifically. In our college of business, I have asked every discipline to consider how analytics could be integrated into existing courses—or to create new courses, as appropriate.
Finally, we've created an undergraduate major in business analytics, an undergraduate minor in business analytics, and an MBA concentration in business analytics. We see the need for graduates with a depth of analytics knowledge (as discussed earlier). Furthermore, we believe, in time, analytics will be a core business discipline.
Across our minor and major analytics courses, we use the framework of (1) big data, (2) analyzing data, and (3) making decisions with the data. One cannot look at any of the areas in isolation; they all work together. And, analytics should not be a recycled or re-labeled existing course on statistics (or operations or management science or data warehousing or ... ). Analytics must be approached with a fresh perspective such that all three of the aforementioned areas of the framework are in play.
Recently, I received a letter from a major company upon hearing that we now require business analytics for all our students: "Inundated with data, we need employees who can help us to analyze and understand this data. Our single biggest challenge in working in this data-driven environment is finding employees who are familiar with business analytics." Need I say more?
Bill C. Hardgrave