How many analytical projects have foundered due to lack of problem definition and other soft skills? As my SAS colleague Sascha Schubert writes, people and process matter, in addition to great technology. Great technology is a great first step, but having the right people following the right process is critically important.
The increasing use of predictive analytics in mission-critical business decisions and operations brings new challenges to the forefront for many of our customers. Throughout the last year I spoke to many customers about their use of predictive analytics and where they see areas of improvement to achieve even more success
In part 1 of my thoughts about analytics maturity, I deferred talking about issues related to the actual assessment of your organization’s level. Today I intend to detail some of the ways my peers and I are thinking about analytical maturity, comment on scales in use today, and address some
As rain settles in over the green fields of England, I’ve been reading the Times Higher Education (THE) periodical. It’s always a lively read, as it invariably takes the part of untenured junior lecturers in any dispute. It is also very well researched and informed. This week’s THE edition has
Analytics maturity is a hot topic right now. Many come to SAS for answers on how to assess their analytics maturity and advance their use of analytics, especially at a corporate level. I want to share the highlights of what we usually prescribe from a best practices perspective regarding advancing
How do you hire a Chief Data Scientist? That's not a hypothetical question: I know of at least three companies that are actively looking for a "Chief Data Scientist" at the moment. Hiring the right person is harder than you'd think. Whether or not a Chief Data Scientist is a
We all have some sort of intuitive idea of what time series data is – it’s a bunch of measurements or observations that are marked by a time stamp – we know when the measurement was taken, as well as what was measured. This natural temporal ordering of the data
I’ve heard it said that the only thing you can count on in life is change. The same can be said of technology. Change is certain, and the rate of change seems to accelerate with each passing year. Change requires us to adapt, but as we race to keep up with
Andy Pulkstenis of State Farm thinks it is, stating that this red-headed stepchild among modeling technques is where predictive modeling was ten years ago. He opened his talk, "Do You Know or Do You Think You Know? Creating a Testing Culture at State Farm," at the A2012 conference in Las Vegas with
A year ago I set out to periodically blog from my perspective of leading R&D for our advanced analytics software. I invited SAS colleagues who also work in various areas of advanced analytics to blog on their own interesting conversations with each other and with customers about the intersection of
