The Internet of Things, that glorious futurescape in which billions of connected devices take much of the work and tedium out of daily living. As human beings, we’re addicted to our stuff and what it does for us. So a world in which most of our cell phones and other
English
![Painting with big data analytics](https://blogs.sas.com/content/sascom/files/2015/10/Seurat-La_Parade_detail-390x336.jpg)
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
![Beyond the Basics: What’s next in predictive analytics for hotels?](https://blogs.sas.com/content/sascom/files/2017/01/CustomerIntelligence-1-702x336.png)
with Natalie Osborn, Senior Industry Consultant, Hospitality and Gaming Practice, SAS We’ve taught analytics 101 through the last couple of blog posts, and now that you have passed that course, you are ready to take an advanced course in analytics. Ok, not really, we won’t subject you to that, but
![There’s no Internet of Things without event stream processing](https://blogs.sas.com/content/sascom/files/2015/10/503256071-702x336.jpg)
The Internet of Things (IoT) is a revolutionary approach that will radically change our lives, our way of integrating with technology, and the way we do business and marketing. Companies have already defined a strategic plan for collecting and organizing data coming from the IoT. The next step is to
![Transporting analytics to the Internet of Things IoT in Transportation](https://blogs.sas.com/content/sascom/files/2015/09/185241914-702x336.jpg)
Why are so many companies across a diverse set of industries investing in and around the Internet of Things? Everywhere I go, every blog I read … I sound like my favorite band from the 80s: the Internet of Things is watching me. In reality, it’s the reverse: I'm seeing
![Am I running slower as I get older? Average finish times by age group for the New York City Marathon](https://blogs.sas.com/content/sascom/files/2015/09/NYCFinishTimes-560x336.png)
I read an article recently discussing how runners inevitably slow down with age, particularly after 50. Data from the New York Marathon and Boston Marathon back this up with generally flat average finishing times for ages 20-49 followed by a steady, almost exponential, increase after 50. I haven’t reached the
![Back to the Basics Part Two: What can hoteliers do with analytics?](https://blogs.sas.com/content/sascom/files/2017/01/CustomerIntelligence-1-702x336.png)
with Natalie Osborn, Senior Industry Consultant, Hospitality and Gaming Practice, SAS This week, we continue our fall “back to the basics” refresher series on analytics for hoteliers. Last week, in part one, Natalie and I reviewed the analytic methods that can be utilized by hoteliers. This week we will explore
![A Shopaholic’s Guide to Analytics II.A](https://blogs.sas.com/content/sascom/files/2017/01/CustomerIntelligence-1-702x336.png)
I realized a little while ago that I may have more loyalty cards and memberships than the average person. (And that I more actively prove my loyalty than the average person). But as anybody who has ever signed up to a mailing list or for a store card knows, having
![Back to the Basics Part One: An analytics primer for hoteliers](https://blogs.sas.com/content/sascom/files/2017/01/CustomerIntelligence-1-702x336.png)
with Natalie Osborn, Senior Industry Consultant, Hospitality and Gaming Practice, SAS. It’s back to school time, and back to school reminds me of getting back to the basics. So, we thought we’d start the fall with a “back to the basics” refresher series on analytics. To accomplish this, Natalie and
![Flipping the data equation](https://blogs.sas.com/content/sascom/files/2015/09/Big_Data_For_Small_Companies.jpg)
Big Data has become a technology buzzword. But how is Big Data changing insurance? Historically, insurance companies have used SMALL data to make BIG decisions. Today, insurers are using BIG data for SMALL decisions. What does this mean? Traditionally, insurance companies have aggregated data to group risks into broad categories