Leo Sadovy currently manages the Analytics Thought Leadership Program at SAS, enabling SAS’ thought leaders in being a catalyst for conversation and in sharing a vision and opinions that matter via excellence in storytelling that address our clients’ business issues. Previously at SAS Leo handled marketing for Analytic Business Solutions such as performance management, manufacturing and supply chain. Before joining SAS, he spent seven years as Vice-President of Finance for a North American division of Fujitsu, managing a team focused on commercial operations, alliance partnerships, and strategic planning. Prior to Fujitsu, Leo was with Digital Equipment Corporation for eight years in financial management and sales. He started his management career in laser optics fabrication for Spectra-Physics and later moved into a finance position at the General Dynamics F-16 fighter plant in Fort Worth, Texas. He has a Masters in Analytics, an MBA in Finance, a Bachelor’s in Marketing, and is a SAS Certified Data Scientist and Certified AI and Machine Learning Professional. He and his wife Ellen live in North Carolina with their engineering graduate children, and among his unique life experiences he can count a singing performance at Carnegie Hall.
Like most boys my age at that time, I wanted to be an astronaut. Fate, however, intervened, in the form of nearsightedness, so I had to find an alternative occupation. Coming to my rescue for the launch of Apollo 11 was my father, who presented me with a huge booklet that broke
There’s more than one way to make a poor decision. Bad data, inappropriate assumptions and flawed logic are just three of the missteps you can take on your climb up the Ladder of Inference, a concept first developed by Chris Argyris, professor of business at Harvard, in 1974, and later popularized
What is information? The lack of a working definition plagued both science and the emerging telecommunications industry until the arrival of Claude Shannon and his famous 1948 paper, “A Mathematical Theory of Communication”, based on his cryptography work during WWII while at Bell Labs. The landmark article is considered the
Having a mentor is the number one factor in increasing the steepness of your personal learning curve. So says my oldest, Garik, a Park Scholar at North Carolina State University (class of 2012), during a discussion he recently had with the incoming Park Scholar class of 2019. To accept the
Big data, by which most people mean Big Volume, doesn’t get you very far just by itself, but with the addition of Big Variety and analytics, now you’re talking. In fact, most organizations who are making headway into capitalizing on their data assets now refer to the process as "big
“Half the money I spend on advertising is wasted, the trouble is I don't know which half.” ~ John Wanamaker, U.S. department store magnate and merchandising / advertising pioneer. I’m not going to claim that I can pinpoint exactly which half of your marketing dollars are wasted in the space
With all the hype over big data we often overlook the importance of modeling as its necessary counterpart. There are two independent limiting factors when it comes to decision support: the quality of the data, and the quality of the model. Most of the big data hype assumes that the data
Why visualization? Several reasons, actually, the most compelling being that sometimes visualization literally solves the problem for you. I remember an exercise in eighth grade English class where we were asked to describe, in words only, an object set in front of us with sufficient clarity such that our classmates,
We are all modelers. Whenever you plan, you are building a model. Whenever you imagine, you are building a model. When you create, write, paint or speak, you first build in your head a model of what you want to accomplish, and then fill in the details with words, movements
The metaphors we choose to describe our data are important, for they can either open up the potential for understanding and insight, or they can limit our ability to effectively extract all the value our data may hold. Consisting as it does of nothing but electric potentials, or variations in
