Does data-driven decision-making matter?

Given the stream of current events, I try to take a few minutes each day to read various interviews and analysis of our political and economic leadership on both sides of the “Lake”.  Try as I might, I’m having trouble ignoring the claims of faked facts, fake news and radically divergent opinions. In an age where empirical data is omnipresent, why does it seem that our political and economic leadership seems to be making things up as they go along? Are our daily realities too complex, too uncertain, and too ambiguous to make sense of the data? Is there something flawed in our personal decision-making when we continually use the facts to support our own beliefs and prejudices? In short, has data become useless, or do we need data more than ever?

The data available to our decision making processes aren’t necessarily stored in a data-base or accessed through the Internet.  We can use both quantitative and qualitative data to make sense of what see around us, to explore our cognitive filters that influence our perceptions, and to qualify our feelings and experiences each day. The structure of the stories we tell ourselves, our colleagues, and our customers is another level of data used to help us aggregate and interpret how interact each day. These forms of metadata are perhaps even more important than the data we see on the screen – for they can be used to calibrate our perceptions of work, value and experience.

Improving decision-making isn’t a question of ignoring the data, but of understanding how data can help us take better decisions. Quantitative data, whether expressed as continuous measures, ordinal numbers or ratios offer us a lever for reducing the uncertainty inherent in management. Qualitative data, in the form of opinions, categories, and impressions, offer us a complementary line of inquiry. Perceptions of the physical context in which we interact offer us a primary source of data. Our lecture of the printed media offer is a secondary source if information with which to work. In this sense our decision environment is neither wholly physical nor digital, but a combination of the two filtered by our beliefs and experience.

Analytics isn’t a synonym for machine learning lodged in the Cloud, but a mindset that we take to work each day. There are a great number of methodologies available whose value depends both on the data at hand and the types of problem s we are trying to solve. Decision trees are extremely useful in analyzing sets of qualitative data in which the solutions sought are contained in the data itself. Market basket analysis is used efficiently when dealing with categorical data in which the solutions aren’t known in advance. Various forms of regression analysis are useful with continuous data in supervised learning environments. The value of analytics is tied less to the software we use than to ability to recognize the data and the problems with which we are dealing.

If our objective as leaders and managers to continuously improve our ability to take better decisions, data is more important than ever. Data on our own beliefs and experiences to understand how we address the problems at hand, Data on the context in which we interact to understand the scope for invention and innovation. Data on how others decide to appreciate how are own recommendations and proposals can be put into practice.  Fortunately, in most cases we already have more data than we need, what we need are better handles with which to use it.

The practice of business analytics is heart and soul of the Business Analytics Institute.  In our Summer School in Bayonne, as well as in our Master Classes in Europe, we put analytics to work for you and for your organization. The Institute focuses on five applications of data science for managers: : working in the digital age, managerial decision making, machine learning, community management, and visual communications. Data-driven decision making can make difference in your future work and career.


March 10,2017