At SAS Global Forum 2014 customers were asked to describe SAS in one word and they came up with quite a few including awesome, powerful, data, fun, easy, and of course statistics, analytics, and PROC. Then I gave it some thought on what one word would I choose to describe SAS. This
What's the differences between predictive analytics and basic reporting? Predictive analytics provides insight about what will happen in the future. Basic reporting only looks at past performance. Why is this difference difficult to grasp? It's partly because transitioning to predictive analytics requires change. And most people don't embrace change. Take
As more and more data is being collected and analyzed, it becomes even more important to have a strategy in place that will allow you to get value out of your data. Since it's humanly impossible for your brain alone to process fast streaming data, an event stream processing (ESP) engine
Twas the night before "big data," when all through the data center Not an IT supervisor was stirring, not even the help desk on-call. The servers where all humming along nicely in hopes Big data would soon be there. The business users were nestled all snug in their offices
Industry-changing dynamics like mobility, smart products, social media and embedded computing put a premium on big data and the insights you can gain from organizational data. As a result, the opportunity to be disruptive with analytics has never been greater. Yet, when it comes to making analytics work, not all organizations
In the first two posts in this series, Seeing the Light: How SMBs are Using Data and Insights to Get Ahead, I shared the motivations that prompted three SMBs to replace spreadsheets and intuition with a more sophisticated, analytics-driven approach to run their businesses. In the second, I discussed the
Did you ever experience a time where you hear or see the same thing over and over again? Whether you chalk it up to coincidence, immersion or saturation, you clearly start seeing the same ideas or topics discussed in multiple places. Lately, I have been hearing about the topic of
Fraud detection presents myriad analytical challenges: gathering sufficient known cases to make typical modeling techniques possible, gathering inputs from disparate data sources, and combining expert knowledge from investigators with findings to be gleaned from the data in an efficient way. Of course, analysts can fall into the trap of thinking
I've recently had the chance to work with Olivia Parr-Rud, an internationally recognized expert and thought leader in predictive analytics and innovative leadership. Her pioneering techniques in predictive modeling led to the writing of her first book, Data Mining Cookbook, Modeling for Acquisition, Risk and Customer Relationship Management (Wiley 2001). Olivia's passion
Are you always looking for that inside perspective? Most of us are! As we all know, customers are the primary – perhaps exclusive – source of cash flow for many organizations. Knowing which ones are most profitable is critical to maximizing future economic value. To help today's marketing and business leaders learn
