David leads the pre-sales technical team for SAS US Energy which solves business problems in the Oil & Gas and Utilities industries using advanced analytics. He is a lifetime learner who enjoys sharing information and helping others to grow their careers. He earned a BS in Industry Engineering and a Computer Programming Certificate from North Carolina State University. Furthermore, he has over 29 years of business experience working with SAS across R&D, IT, Sales and Marketing in the Americas and Europe. He is an expert in working with data and producing insights through the use of analytics. David has presented at SAS Global Forum, the 2012 SAS Government Leadership Summit, IBM’s Information on Demand(IOD), EMC World, CTO Summit Conferences, is the author of the book: "Big Data Analytics with SAS", and he currently holds 14 patents for SAS in several countries: US, CA, Norway, UK, China, Mexico, and Hong Kong.
In the oil and gas industry, analytics are used to improve both upstream and downstream operations, from optimizing exploration and forecasting production to reducing commodity trading risk and understanding customer's energy needs. If you plan to derive value from the digital oil field, big data, and analytics, one of the first things
What's that productivity related quote by Charles Dickens? "My advice is never do tomorrow what you can do today." For years, machine learning has been written about and discussed widely with a focus on the benefits it will bring in the near future. But guess what? The future for machine learning
The smart grid is a technology infrastructure that adds intelligent capabilities to the electricity distribution system. When you apply analytics to the smart grid data, you can automate and improve operations, maintenance, planning and customer satisfaction - among other processes. As utilities continue to upgrade meters, transformers, and add new sensors and equipment,
I've been playing around with this equation to show how big data derives value using analytics. From my perspective, it looks like this: Big data + analytics = Hadoop + SAS = Value. To expand the equation, I started thinking about the V's of big data and the D's of analytics. You've probably heard about the
I'm not just talking about all the energy put in planning, scheduling, and supporting this great event, which is in Dallas, TX next week, April 26-29. I'm talking about all the presentations about using analytics in energy related industries, particularly in Utilities and in Oil and Gas. There is a
When do analytics really provide value? All the time, of course. However, one of the best times for analytics to prove their value is when you are asked to do more with less. Often, the reason we are asked to do more with less is because of an economic downturn
How many of us have heard or even said the phrase, "If it's not broke, don't fix it." While on rare occasions this may be the correct approach, it is a statement that stops innovation and creativity in its tracks. You might as well say, "Because we've always done it that
As a follow-up to my previous Christmas blog post, Twas the night before big data, I give you ... Bayesian network, the directed acyclic graphical (DAG) model ( yeah, yeah, I know it doesn’t quite have the same ring as last year’s title, but you'll recognize the tune) You know Linear and
After attending the SAS Day at Texas A&M University on Oct. 2, I came away with a new perspective on some of the different educational offerings to help fill the analytic talent gap (which according to studies and research continues to grow). In essence, there is a yin and yang of
SAS has been developing "secret sauce" technology for more than 38 years. Whether it has to do with being platform independent, processing in-database, running across a grid, or analyzing data in-memory like our SAS LASR Analytic Server or our High Performance Analytics offerings, secret sauce makes everything taste or, in
