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A common task in data analysis is to locate observations that satisfy multiple criteria. For example, you might want to locate all zip codes in certain counties within specified states. The SAS DATA step contains the powerful WHERE statement, which enables you to extract a subset of data that satisfy
Everything’s bigger in Texas and that definitely held true at SAS Global Forum 2015. The conference was bigger and busier than ever, especially for the education industry. There were so many amazing presentations and announcements, that you may have missed a few -- here are the highlights. We had a several customer presentations on SAS Visual
We’ve all been there. You’ve knuckled down, cleaned out the garage, the attic, and that cupboard under the stairs, thrown away a ton of stuff, only to need it again the very next week. Until recently, that’s exactly what many businesses did with their data. The data explosion has radically
We have all heard the old joke about the person who sidles up to a doctor at a party and describes in great detail a medical problem a "friend" is having in order to get free medical advice. It could just as easily be a person buttonholing an accountant for
Once you have assessed the types of reporting and analytics projects and activities are to be done by the community of data analysts and consumers and have assessed their business needs and requirements for performance, you can then evaluate – with confidence – how different platforms and tools can be combined to satisfy
Data governance and data virtualization can become powerful allies. The word governance is not be understood here as a law but more as a support and vision for business analytics application. Our governance processes must become agile the same way our business is transforming. Data virtualization, being a very versatile
Everyone loves a “mental health” day, one of those days when we get to relax and escape from the everyday worries and stresses of life. Imagine the challenge of dealing with true mental health issues everyday – especially as a child or youth where mental health issues can cause isolation,
I was surprised to find that the size of the U.S. federal government is smaller today, than in the past many decades - let's graph it out, so it's easy to analyze... The way I got started on this little adventure was via Jishai's graph on dadaviz.com. Here's a snapshot
Have you noticed how your smart phone seems to know everything about you? Where you live, where you work, and even how long your daily commute will take! A lot of that information is generated by your daily activities while using your connected devices. There is much to be found by analyzing the
Today’s natural language processing (NLP) systems can do some amazing things, including enabling the transformation of unstructured data into structured numerical and/or categorical data. Why is this important? Because once the key information has been identified or a key pattern modeled, the newly created, structured data can be used in
New York City Mayor, Michael Bloomberg made a new-year's resolution to learn code. Apple’s Steve Jobs said, “I think everybody in this country should learn how to program a computer because it teaches you how to think.” President Barrack Obama said, "Don't just buy a new video game, make one.
Seguramente siga escuchando acerca del Internet de las Cosas sólo como un concepto, lo cierto es que con ejemplos como el que queremos contarle a continuación, se hace evidente el impacto que tiene no sólo en las cosas sino en las personas. Imagine un mundo en el que su carro
On Monday, SAS announced the beginning of a new era with its Toshiba Global Commerce Solutions OEM partnership. This is the first time SAS has provided its technology for an equipment manufacturer to wrap into its solution to help retail customers gain the benefits of advanced analytics. We always ask
I was privileged with the opportunity to present a couple of papers at SAS Global Forum 2015 in Dallas, Texas this year. While there, I was also excited to attend presentations with new and inventive approaches for working with the administration and architecture of SAS solutions. This is a collection
In my previous post I used junk drawers as an example of the downside of including more data in our analytics just in case it helps us discover more insights only to end up with more flotsam than findings. In this post I want to float some thoughts about a two-word concept