Machines don’t take great decisions, people do

The Business Analytics Institute will once again be hosting its Summer School in Data Science for Management in Bayonne, France from July 2 to 11 2018.  In this exclusive five-part series on Why Future Managers Should Invest in Data Science, we will explore the BAI’s unique value proposition around improving managerial decision-making.  

We live in a time and space in which data is constantly mistaken for facts. Taking better decisions, rather than manipulating 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.i To date, there is little evidence that this revolution has led to better decisions than in the past.  Are today’s fake news, faked facts, and manipulated opinions the cause or the result of poor decisions? Most importantly, what can be done to improve your ability to take better business and career decisions?  

“A manager who ignores Data Science is much like a drunken man beside a lamp-post – he grabbles for support rather than illumination”

 What does improving decision-making entail?

Data Science is about transforming data into impactful decisions that address fundamental organizational challenges. In decision science, we learn that the major challenges to decision-making are perceptions of the complexity, ambiguity, and uncertainty of the environment in which we take decisions.ii In the cognitive sciences, we are taught that our pre-conceptions and prejudices both distort how we see the problem, and limit our ability to propose innovative solutions. In management schools, we are trained to recognize that there are distinct types of problems and that each requires unique approaches that can’t be solved in a uniform manner. Finally, in business, we sense that the culprit isn’t just our own decisions, but often those taken around us. 

What do some business problems never seem to go away?

David Snowdon and Mary Boone remind us that the nature of the problems we face today has evolved considerably over the years.iii They speak of levels of complexity that determine how managers evaluate the context of their business and where they look for data. They suggest that first level problems concern linear chains of events where process optimization provides pertinent and replicable solutions. Second level business challenges result from malfunctioning processes in which the answers can be deduced from management’s prior experience.  

Third level challenges require reformulating the problem, for neither the process nor prior experience provides sufficient data to deal with the problem. Snowdon and Boone argue that markets and organizations over the years have largely addressed the first and second level problems, leaving us today third level challenges of customer satisfaction, employee engagement and organizational effectiveness that are intricately woven into the way in which we envision work. 

What is a better decision actually mean?  

In line with Thomas Davenport’s thoughts on decision-making,iv 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. 

How can the study of Data Science improve your ability to make good decisions?

Data Science is a mindset rather than skill set- a management candidate needs to demonstrate his or her ability to “solve” business problems through the analytic method: evaluating the context (logic of the industry and the corporate business model), to identify the roots of the problem, evaluating the quality of the data at your disposal, choosing the right methodology to address the problem, and leveraging data to create the conditions for collective action. Data Science is less about theory than it is about practice, integrating these decision-making fundamentals into the way we work. 

Using real-life examples and case studies in the health and life sciences, participants in this year’s BAI Summer School will discuss the mechanics of improving managerial decision-making. You will apply the models and tools for creating, collecting, and codifying organizational information.  You will learn how to choose and apply the appropriate decision models based on the challenges at hand. You will explore how analytics can enhance decision-making by converting data into actionable insights. Finally, you will analyze the ethical issues inherent in knowledge and community management, as well as data-driven decision-making.  

The BAI Summer School on Data Science for Management

The Business Analytics Institute Summer School in Data Science for Management in Bayonne, France will be held from July 2 to 11 2018. This university and corporate accredited program targets senior and graduate management students who want to understand how data science can enhance both their jobs and their future careers. The session will be facilitated by internationally recognized professors and practitioners on digital economics, managerial decision-making, machine learning and artificial intelligence, and data storytelling. Detailed information on the program, social activities, student testimony, and university accreditation/professional certification can be found at http://baisummer.com 

Further Reading from BAI 

What would better decisions mean for you? 

How Data Science leads to better decision-making 

Analytics 4.0: it’s all about taking better decisions 

Measure the quality of your decisions, and not just your data 

Lee Schlenker

Lee Schlenker

Dr. Lee SCHLENKER is Professor of Business Analytics and Community Management and a Principal Consultant of the Business Analytics Institute.

Over the last twenty years, he has led dozens of missions for Big 4 consulting groupsin the manufacturing, telecommunications, public works and service industries.

Recognized as an expert for the European Commission in learning analytics, Lee has directed or participated in studies on improving management education in the US, Europe, Asia and the Middle East.

Recipient of the EDSF prize for the use of technology in teaching, Lee currently facilitates various management education courses in Europe and abroad.
Lee Schlenker