We often think of academics and professional business management as two worlds that are poles apart. The coming together of these two worlds is something most statisticians find difficult to comprehend.
I didn’t know what to do after high school, but back in 1992, I chose Mathematics, as it seemed that there were plenty of opportunities. I went to EPFL in Lausanne and started basic courses in mathematics, though my true interest lay in more applied topics. During my first year at EPFL, I was not sure whether I would make it to the second year and had the idea to become a wine-maker as it was my family background. But during the first two years, I got introduced to statistics and was instantly fascinated by the idea of exploratory data analysis. I was under the impression that statistics was a subtopic of mathematics which of course it is not, but that was how it was structured at the time (and unfortunately still is) in most European universities. During my third and fourth years of studies, I took all the possible statistics courses one could possibly take and for several semesters worked on models for soccer predictions. I completed my diploma in 1998.
In 2001, once I had completed my Ph.D., it dawned on me that everyone needs statistical consulting, data analysis and data mining (now rebranded as data science) services. I started my own company Statoo Consulting back in 2001. That is how I got into professional business consulting. Academics came naturally to me. I taught semester courses for engineers at the EPFL and later on started teaching at the University of Geneva. By 2016 I was nominated as Professor of Data Science at the Research Center for Statistics (RCS) of the Geneva School of Economics and Management (GSEM) at the University of Geneva. There is a lot to be done in both the domains: Business and Academics; and I am immensely proud to have the best of both worlds and try my best to bridge the gap between both worlds.
For the years, I have been interested in statistical engineering. If somebody asks me what my best job description is, I tell him/her: “I’m a statistical engineer”. I approach a client, consider the problems he/she faces and help him/her decide from a tactical level the best strategy on how to best utilize statistical concepts, principles, methods and tools, and on how to integrate them with information technology and other relevant sciences to generate improved results and to enable continuous improvement. They key is to help the client to augment (using, and along with, existing operational methods, tools and technologies) the strengths of his/her people, processes and services.
As such, a philosophy that has often driven me is that of continuous improvement using statistical thinking, which is based on the following fundamental principles: all work occurs in a system of interconnected processes; variation, which gives rise to uncertainty exists in all processes; and understanding and reducing (unintended) variation are keys to success. So how can this be done in order to continuously improve? This is really a philosophical approach which you can really apply everywhere. This idea of statistical thinking, augmented by the usage of statistical engineering at the tactical level, is a driving force in what I do in respect to business analytics and big data analytics. In the past, we used to have one task: “to do our work.” Nowadays, with the digital transformation and the related data revolution, we must take on another task: “to improve how we do our work.” It is possible to augment our strengths by automating any routinizable work and focusing on our core competencies thereby, ensuring “smart machines” and humans work together for a better result. Moreover, I believe bringing statistics and statisticians to decision-making tables will go a long way in setting up a culture to do data-driven decision making and demystifying big data (along with analytics, data science, statistics, artificial intelligence, machine intelligence and machine learning).
By using various channels like Twitter, LinkedIn and SlideShare, I tried to communicate the beauty of statistics to the masses, by educating people on statistical principles and analysis. I want above all for statistics to be seen for what it is actually about, and I always try to persuade people that statistics is the profession of the future and is at the heart of all things big and small these days. Since 2017 I am regularly ranked in “Top 100 Influencers” lists on big data, data science and analytics, and I got the visibility that I never thought possible.
Lastly, I believe that a good statistician should also be a good story-teller. He/She should be capable of presenting ideas to an audience who do not have any formal background in statistics or mathematics. The art of communication is often overlooked when it comes to statistical education. It is a critical skill that gears one up for a career in statistics and to make oneself heard. People always have time for a good story.
Meet Professor Diego Kuonen: A Statistical Engineer, Consultant, Academician and Wine & Soccer Enthusiast.
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