In a world that privileges novelty, modernity, and innovation, analytics isn’t exactly a passing fad. Mankind has been trying to make sense of the world around him since at least the beginning of recorded history. Most of science, if not the arts, has been based on scanning the environment, qualifying the data at hand, choosing the best method to move forward, and transforming our impressions into individual and collective action.
What has changed over the years is the plethora of data at our disposal, we have created more data in the last two years than that produced since the dawn of humanity. All for not, for whether we look at economics, politics, or society we can justifiably question whether we take better decisions than our forefathers.
The leitmotif of the Business Analytics Institute is to develop the use of analytics in managerial decision making. We base our work on a vision of Analytics 4.0. Not a newer and better version of past analytical methods, but a call to action supported by four foundations designed to transform the abundance of data at our disposal into more effective, more impactful decisions. In this world of the Internet of things, artificial intelligence and neural networks, our end goal isn’t to make machines more intelligent, but to help each people take better decisions. Let’s briefly explore briefly each of these foundations in turn:
The first pillar of our vision is built around the role of data in modern economies. If data is now seeming as the lifeblood of economic activity, what is data? Data is a mere reflection of reality, an imperfect mirror of how we interact with the world around us. Although advances in information technologies have helped us work quicker and faster, they have often encouraged us to focus on the data rather than on business challenges and opportunities. The dawn of this Fourth Industrial Revolution is an invitation to leverage data in making sense of the world around us.
The second pedestal of our vision is built around how people use data in taking decisions. Data isn’t just numbers behind a screen, but the different forms of entropy before our eyes. Because people look at value from different angles, they rarely see data in the same light. Our work in the behavioral sciences explores how cognitive biases influence how we see the world around us. Anchoring, framing, and herding are examples of bias that condition how we interpret the data on hand. Human perceptions of risk, uncertainty, and ambiguity hinder practices of data-driven decision-making.
A third foundation of analytics is laid with each innovation in machine learning. Where traditional methodologies focused on regression analysis using ordinal data in deterministic environments, machine algorithms today offer a number of conceptual tools to address a wide range of business challenges. Mastering machine learning requires both understanding the parameters of the problem and constant practice. In decision contexts in which their one right answer, classification algorithms work well with ordinal or categorical data. In scholastic environments in which we can’t specify all the variables, clustering or dimension reduction provide more pertinent results.
Finally, the fourth dimension of analytics brings visual communications into focus. An analyst’s job may begin by examining the data, but only ends when working with people. Unlike spreadsheets and databases, humans don’t record specific bits of data or information, but impressions and sensations that when processed recall subjective experiences. In an economy where attention is perhaps our rarest resource, helping your audience focus on the message is a key success factor. The Gestalt principles of similarity, symmetry, proximity, continuity, and closure are fundamental tools in understanding how we reconstruct the reality around us. The gauge of a data scientist’s talent is in his or her ability to transform the data into a call for action.
Analytics 4.0 is thus built upon these four pillars: understanding the role of data in modern economies, examining the cognitive processes intimately tied to human decision-making, applying machine-learning given the types of problems we are trying to solve, and transforming data into actionable decisions. In our Summer School in Bayonne, as well as in our Master Classes in Europe, our focus on digital economics, data-driven decision making, machine learning, and visual communications we can help you put analytics to work for you and your organization.
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