This week’s news in the US, in Europe, and in the Middle East is a vivid reminder of the fallacy of rational decision-making. Whether we are reading about business, economics or society, each day seems to bring its load of conspicuously poor decision-making.
Jack Zenger and Joseph Folkman have outlined several reasons why decision-makers fail including negligence, lack of anticipation, indecisiveness, and isolation.[i]. Are fake news, faked facts, and manipulated opinions the cause or the result of poor decisions?[ii] Most importantly, what can be done to improve our decision-making skills for our organizations, our customers, and our careers?
We live in a time and space in which the which data is constantly mistaken for facts. Taking better decisions, rather than crunching the data, is the ultimate benchmark for improving management. We are currently producing roughly 2.5 quintillion bytes of data each day — more data in the last two years than in the previous history of mankind.[iii] Klaus Schwab suggests that we have entered into a Fourth Industrial Revolution in which value is defined by our ability to capture and to analyze this vast amount of data.[iv] To date, there is little evidence that this revolution has led to better decisions than in the past. Data Science is about transforming data into impactful action to address fundamental organizational challenges.
What does improving decision-making entail? In decision science, we learn that the major challenges to effective management are the perceptions of the complexity, ambiguity, and uncertainty of the environment in which we take decisions. In the cognitive sciences, we are taught that our pre-conceptions and prejudices both distort how we see the problem and bound our ability to propose innovative solutions. In management schools, we are trained to recognize the complexity of real-world problems today defy the s logic of “one best way”. Finally, in business, we sense that the culprit isn’t just our own decisions, but often those taken around us.
What is a better decision? In line with David Snowdon’s work on sense-making[v], we believe there is a clear distinction between good, better, and great decisions. Good decisions are possible in deterministic decision environments in which the right answer can be found by examining the data at hand. Unfortunately, most business decisions are taken in stochastic environments in which the right decision cannot be deduced from the available data — better decisions are none-the-less possible by reducing the causes of uncertainty. Finally, we refer to great decisions are those with which the context, challenges, and solutions allow us to re-examine the nature of the decision-making process itself.
Although machine learning is currently marketed to management as a mystical elixir, it’s nothing more than a technological tool used to explore the nature of the problems we face. Supervised learning represents one specific type approach to problem-solving in which we know that the answer is in the data, the challenge is deducing it. Unsupervised learning encapsulates a type of challenge in which there is no one right answer, but we believe that studying the data will allow us to induce patterns of potential responses. Semi-supervised learning represents a third approach in which we know the answer, but we are trying to calibrate decision-making processes to produce more reliable results. In all cases, infomation technology provides us with a mirror of how to think about the challenges around us.
How can the study of data science help us become better decision-makers? Business analytics is a four-step process designed to help people make better decisions in the context of their work. To begin with we need to scan the environment (physical and digital) to understand the nature of the problem we are trying to solve. The second step is exploring the quality of the data with which we have to work. The third step is applying the correct methodology to explore the data and formulate solutions in response to the types of problems we are trying to solve. Finally, we need to transform the data into stories that will motivate our teams and communities to take the appropriate actions. Data Science is less about theory than it is about practice, integrating these decision-making fundamentals into the way we work.
The practice of data science is the heart and soul of the Business Analytics Institute. The 2018 BAI Summer Program will explore the mission-critical skills in leveraging data science to improve managerial decision-making. Our unique summer session will provide four dozen participants from the US and Canada, Europe, and Asia with a solid understanding of the practice of using analytics — how to evaluate the data at hand, how to apply the appropriate methodologies to specific types of personal and professional challenges, and how to transform data into collective action.
Lee Schlenker is a Professor at ESC Pau and a Principal in the Business Analytics Institute http://baieurope.com. His LinkedIn profile can be viewed at www.linkedin.com/in/leeschlenker. You can follow us on Twitter at https://twitter.com/DSign4Analytics
[iv] Schwab, K. (2017). The Fourth Industrial Revolution. 1st ed. Random House Inc.