The Ubiquity of Big Data: In Business and the Business School

The Ubiquity of Big Data: In Business and the Business School

Big data's impact on business today provides scholars with an excellent opportunity to partner with businesses, but it also requires educators to prepare students for a data-infused workplace.

The ability to gather and process unprecedented amounts of data has had—and will continue to have—a profound impact on how businesses function. It is critical that business schools incorporate big data and the functions tied to it (machine learning, artificial intelligence, and cloud computing) into research projects as well as curriculum.

Big data refers to data sets both longer and wider than anything we have seen before. These data sets can have a million observations (length), but also many categories of data per observation (width). The irony is that businesses are only using a small fraction of the data they collect, and they are not always analyzing the data correctly. Accordingly, scholars have an excellent opportunity to partner with organizations to help interpret the data through a theoretical lens and prevent businesses from making decisions based on false correlations. It is too easy to find significance levels when the “n,” or sample size, is so large. Big data will provide business school faculty in practically every discipline the ability to improve the reliability of their research results. With increased access to data in such large quantities, the norms for acceptable sample sizes and levels of significance in order to be published may shift, as well.

Big data has also influenced the skill sets needed in the workplace. Business faculty need to be aware of how these changes impact their discipline and their students. At a recent specialized conference on big data sponsored by the Academy of Management, we heard a request from Cassie Kozyrkov, chief decision intelligence engineer at Google. Using the metaphor of a microwave, she stated that businesses, such as Google, do not need business schools to train students to build microwaves; they need us to make sure our students know how to use a microwave.

In other words, our students do not need to know how to create algorithms, but they need to understand what an algorithm is and what it does. Just as we stopped asking students to transpose matrices by hand once computers came along, Kozyrkov was suggesting that programs, such as Excel, now perform functions that students used to have to do by hand. As much as we might prefer that students still learn these basics, it is time to shift the focus to analytical skills rather than memorizing equations.

Analytical skills and data-based decision-making should be incorporated throughout the business curriculum. AACSB has reinforced this need by specifying separate criteria for “technology agility” skills in accreditation standard 9. Analytical skills must go beyond knowing how to interpret data. Students need to understand enough about data parsing, data weighting, and data analysis to understand the results handed to them by the analysts. Our students need to be able to question the accuracy of data and to be able to identify credible data.

Going back to the example of understanding algorithms, our students need enough understanding to be aware that algorithms are based on human assumptions, and these can be flawed. Another timely example is blockchain. A blockchain is a mathematical structure for storing data that is practically impossible to fake. Business students (and faculty) should have a working knowledge of what it is and why it is important, but they do not need to know how to create one.

The ethical issues surrounding big data also cut across multiple disciplines. It is relatively easy to identify someone with a small amount of information, so we cannot say that data sets are anonymous anymore. Attitudes toward privacy are shifting. Some in the millennial generation—followed now by Generation Z—have grown up with their private details shared first by parents and then personally on social media. Privacy issues compete with convenience.

Take the example of artificial intelligence, which relies on big data and machine learning. The right of people to protect their personal data, such as health information, may conflict with the potential benefits of data sharing to find a cure for a disease, for instance. In the book (and later movie) The Circle, a character poses a future where child abduction would be eliminated by having all children tagged with an embedded chip. In this future, anyone who commits a crime or act of terror would be found immediately because of hidden cameras everywhere streaming live feeds on the internet. The reader is left to weigh the advantages and disadvantages of such use.

Even when considering just the business disciplines themselves, big data is an important issue in each of them. For example, the marketing field in many ways has taken the lead with the use of big data. Businesses can compile extremely comprehensive data on current and potential customers. These data can also be useful for operations and supply chain decisions, financial analyses, risk management, and strategic decision-making. Data breaches are harmful to businesses, and managers need to be aware of the entire chain of custody of personal data in order to protect the weakest link.

In my own field of management, I can see many ways to incorporate big data into the curriculum. Organizational strategy classes could cover the impact of big data, for example, by examining Facebook’s data-sharing deals with select companies. Educators could discuss big data and artificial intelligence in relation to decision-making. As algorithms become increasingly complex, explaining how and why a decision was made becomes more difficult, thus reducing transparency. Management faculty could also address the issue of data-based decision-making in regard to the development of trust with data analysts. Managers need to understand what motivates analysts, how they think, and how to communicate with them in order to build trust. Trust is critical because managers are relying on the analysts to give them useful results.

The examples go on, and will only continue to grow in the coming years. We will need to continue to explore all of the areas big data can impact, for good or ill, and ensure our students are prepared to have at least a foundation in those areas so they can enter the workforce ready to handle the scenarios they will encounter.


Headshot of Deborah L. Kidder, Professor of Management, Associate Dean and Accreditation Coordinator, Barney School of Business, University of HartfordDeborah L. Kidder is professor of management, associate dean, and accreditation coordinator at the University of Hartford Barney School of Business.