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.
You know that feeling when all your ducks appear to be in a row, all the numbers add up, all the boxes have been checked, but you’ve still got a sneaking suspicion that something is wrong? I’m not talking just gut feel here, more along the lines of, “The logic
You are going to be spending proportionately more of your IT budget on security than you have previously spent or ever wanted to spend. Why? Because you and everyone else on this planet is engaged in the still early stages of an escalating information arms race, that, while you didn’t
any factors go into your strategic global business decisions, from the physical placement of factories and distribution centers, to your choice of suppliers and partners, to your target markets and the business model itself. Businesses have a choice of fundamental global go-to-market investment strategies, from direct foreign investment on the one
“There’s no such thing as information overload - there is only filter failure”. ~ Internet scholar Clay Shirky Information overload is not just a recent phenomenon, it entered into human experience in the middle of the 15th century with Gutenberg and his printing press, and we’ve been devising ways to cope
“Begin with the end in mind” - Habit #2 from Stephen Covey’s ‘Highly Effective People’. The Edge Foundation is based on the premise of: “To arrive at the edge of the world's knowledge, seek out the most complex and sophisticated minds, put them in a room together, and have them
Forget about Big Volume, for my money the real value in Big Data comes from its variety. Why? Because just as there is “Value in the Network” when it comes to your business ecosystem, your data can be "networked" for value in much the same way. Before we dive into the business implications
“When it comes to the Internet of Things, the future clearly belongs to the Things”. I made this brash statement in a previous post (“Cloud encounters of the Fifth Kind”) referring to machine-to-machine (M2M) being the fastest growing component of non-human traffic on the Web. I say “brash” because that
"You show me a successful complex system, and I will show you a system that has evolved through trial and error." ~ Tim Harford TED Talk link: http://www.ted.com/talks/tim_harford Karl Marx died thinking that the first communist revolution would occur in Great Britain, driven by the long hours and unsafe
The Internet of Things is going to be driven by innovative business models as much as by innovative technology. In order to ground the following discussion, I found it helpful to create this visual depiction of the IoT that defines and distinguishes the key elements that enter into these business models.
“Let’s assume a normal distribution …” Ugh! That was your first mistake. Why do we make this assumption? It can’t be because we want to be able to mentally compute standard deviations, because we can’t and don’t it that way in practice. No, we assume a normal distribution to simplify
