With all the industry emphasis and collateral available on high performance analytics, business intelligence and visual analytics, it can be difficult to know exactly where to begin, especially if you don’t have a team of statisticians standing by. Thankfully, analytics covers a huge range of opportunities to empower your business, and
Thanks for returning to learn more about this critical technology. Following yesterday’s overview post on the new SAS Hadoop support, we’ll dig a little deeper today and consider the following: Under the Hood: A Peek at the Technology SAS Hadoop Value Summary A Note About the Future Under the Hood:
Hadoop – it’s not just hype! The community has shown tremendous interest in our plans for Hadoop – what will be supported, when it will be available, and so on. We’ve been blogging about big data and provided early plans for Hadoop, including SAS/ACCESS support for Hadoop. Well, it's official:
Mike Ames and I recently had an opportunity to talk to Fern Halper and Judith Herwitz from Hurwitz & Associates as they are doing a 4 part blog series on vendor views on big data and big data analytics. You can view Fern's blog post about the SAS perspective here. Here
Various research efforts and customer conversations clearly indicate that the data exploration and data preparation stage of the analytic lifecycle is complex and time consuming. Scarce data scientist or data analyst resources are spending the majority of their time on data preparation tasks vs. spending their time on deriving insight
It’s hard to believe, but now that 2011 is almost over it’s time to look ahead. The technology pundits are starting to publish their 2012 predictions, and it’s not surprising to see topics like analytics, cloud, big data, mobile, social networking, virtualization, open source on these lists. Instead of creating
Big hype about big data has played a significant role in driving awareness about the value of analytics. SAS welcomes the interest in big data, since it highlights our ability to work with huge volumes of complex and diverse data. Since this is such a critical topic, we have formulated
The basic big data problem is simple to understand: we create too much data to store and analyze it all. The problem gets bigger, however, when you consider the related factors: our problems themselves are getting bigger, the analytics needed to solve them are more complex and the data is
