American businessman and futurologist Alvin Toffler is famous for his quote “the illiterate of the 21st century will not be those who cannot read or write, but those who cannot learn, unlearn and relearn.” The advent of big data is a classic example of the new context managers are in that requires new skills to be gained to survive. Today’s executives need to ensure that they have sufficient knowledge to make the right decisions given this complex resource that until recently was not available to them.
Saša Pekeč and Peng Sun are both professors in the Decision Sciences area at the Fuqua School of Business at Duke University in Durham, North Carolina. They teach both MBAs and executives how to handle and manage data to enable them to make better business decisions. “It's becoming like a universal truth, that new businesses are relying on data-driven decisions in order to thrive. And if a company doesn't want to do that, they should get ready to be squeezed out of the market by competitors says Pekeč. “Look at how Uber operates, or Amazon… what are their business models? Essentially data-driven decisions in every aspect of the business. And what is behind that? It's all data science and analytics.”
Digitization is a wave of new opportunity that is washing over every sector, and the challenge for businesses is to be agile enough to accommodate the constant change it demands. What Pekeč and Sun focus in on comes before this however; it is to understand the opportunities that can be identified and finessed by marrying data analysis with commercial understanding. One of the key factors in enabling this change to occur is the dramatic increase in availability of data that businesses can access and crunch to better understand both their current situations and how to build for the future. And this is often where the first problem lies for managers.
To leverage the opportunities that data can bring it is important to make the correct interpretation of that data, which requires good quantitative analysis. And good quantitative analysis requires a solid understanding of what the original data constitutes and then appreciating what relevant conclusions can be drawn from that data. Both Pekeč and Sun see that many managers today are not clear on how all these different elements fit together. “the landscape of business models and successful businesses have changed in recent years quite significantly…” says Pekeč, he goes on to note that today’s MBAs are eagerly signing up for electives on these topics “but MBAs already out in the field did not get any of that, because that was not where business was, even a few years ago” and so the exec-ed program they offer “is an opportunity to catch-up.”
“The target audience for our program is not for those already good at math” says Sun. The need in businesses is to get those with commercial acumen to understand how quantitative analysis works, so they can work more effectively with the IT experts in designing their systems to collect and structure data in a way that can be applied beneficially, or as Sun says “so that they can be better informed consumers of this type of analysis.”
Pekeč expands on this “You don’t need to know the details of the statistical techniques, but you should know that they are available and when and how they can be used.” So there is no need or expectation that managers become expert in designing or implementing the IT processes to gather the data, nor to be able to do the data scientist’s specific analysis, but today’s managers should understand what decisions will be possible to make, from specific data sets – and so set the environment and conditions to allow such data to be analysed.
Returning to the earlier example of the new logistics companies, Sun explains “What is meant by efficient? It means that when you have a truck, you want to fill the truck. But do you really want to wait until the truck is filled before you dispatch it or you want to schedule it before you know the actual realised demand? These are the kind of decisions that needs to be painstakingly made in order to make sure that things run smoothly.” Managers need to adopt this level of curiosity and have the mindset of knowing that these are the things that need to focused on or else the market will fly past them to competitors who have a better managed solution.
Summarising the difference between the importance of their decision focused program and other data programs, Pekeč reflects that he is on the board of a fast-moving consumer goods company in Europe, and that the management there is focused on analysing the data the company collects in order to spot new opportunities, both in the short term and long term. Even such businesses in which data traditionally did not play a central role, need to adjust and fully embrace data-driven decision-making. That is what lies at the heart of this program for managers: if you don't think this way, your competition might. Everybody has their own data and they will try to be able to exploit it. “If you're a manager who’s thinking about bigger issues, you don’t need to become a data science expert, but you might want to incorporate data science and analytics to enhance your business. But in order to do it right, you first have to figure out what data-driven decision-making can do for you and your business.”