Big Data, Artificial Intelligence and Business Professionals

Big Data, Artificial Intelligence and Business Professionals


by Gabriel Pereira Pundrich, Dept. of Accounting, Bocconi

Big Data is not something new. Companies have been working with extremely large data sets for decades, analyzing mountains of information to gain insight into customers, their consumer behavior and demographics. Yet, it is only recently that Big Data gained attention. The reason is simple: storage and computation of large amounts of data made the technology affordable with faster computers and lower costs of memory. 
Once data becomes cheap and easily obtainable, however data is valuable to an organization only when we can derive knowledge from it. Yet, Big Data is much more than simply storage of large datasets: it is a set of techniques that rely on the abundance of very detailed data to produce knowledge. Such techniques could be classified in two groups: those allowing the store and access large amounts of data, and those enabling analysis to detect patterns, trends, and associations. Data by itself has no value if information and knowledge are not derived from it. The hype about Big Data is in fact not regarding the large amounts of data it can deal with, but with the analytics that can reveal new knowledge using such datasets.

One of the main benefits of Big Data is that it enables Artificial Intelligence (AI). Data is the raw material for AI technologies and even though has existed for many years, it is the fast production of data that has allowed it to advance at astonishing speeds. Data is growing faster than ever and more data has been created in the past two years than in the history of the human race. This explains why we feel as living in the science fiction era where AI is transforming the way we operate. Robots fueled by AI are present in several tasks such as choosing optimal investment opportunities in major banks, screening and selecting staff, identifying costs and suggesting improvements, and even able to drive our cars.

AI and Big Data also bring changes creating new opportunities. Financial Times, for example, has recently released an article where John Cryan (Deutsche Bank chief executive) has warned that a “big number” of people working for Deutsche Bank will ultimately lose their jobs as the bank embraces its “revolutionary spirit” and ends the era of accountants acting like “abacuses”. So, how should we, as business practitioners and academics, participate in this technological revolution and use it as an opportunity to grow as professionals? The answer comes from demystifying the expectation that AI will solve all our problems without the support of a human being. AI is a very sophisticated tool, but without providing the right meaningful business data, selecting the right business problem and properly interpreting its output, it becomes a very sophisticated (and expensive) tool that doesn’t provide any real significant benefit. Technical people are not trained in these activities and that is exactly where business professionals can make a difference.

To be part of this revolution it is important to be prepared. It is very difficult to design business solutions using advanced technical tools without knowing how these tools operate. Not understanding the characteristics of a technology limits our creativity since we don’t know how far it can take us and the associated costs and benefits. It is crucial that we as business people are technically proficient, who can talk to developers and interact with data scientists. A survey of about 80 data scientists in 2016 conducted by CrowdFlower indicated that data scientists spend around 80% of their time on preparing and managing data for analysis. The question is, while Data Scientists are busy with data management tasks, aren’t business professionals missing an opportunity to be the ones specialized in selecting the most valuable business opportunities to benefit from Big Data and AI? Given their training, shouldn’t they also be involved in analyzing and interpreting patterns and applying insights of these systems to the proper business context?

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