| This post was kindly contributed by NOTE: The Blog of RTSL.eu - Development With SAS - go there to comment and to read the full post. |
It’s been relatively easy to predict the key topics for the last few years at SAS Global Forum: big data, mobile BI, and visual analytics. These have been industry themes, and SAS have invested a lot in Visual Analytics. They continue to invest heavily in Visual Analytics and its associated technologies such as the LASR server.
So, this year, I’m expecting to see lots more of the same, with lots of references to the “democratisation of analytics”. Yes, SAS will doubtless spell “democratisation” with a “z”, but George Bernard Shaw did say that England and America are two countries separated by a common language.
Not so long ago, making use of advanced analytics required massive investments in high-powered IT infrastructure. Obviously, that tended to limit the number of organisations that could afford to take advantage of advanced analytics software. But with the advent of massively parallel computing delivered on relatively cheap commodity hardware, the situation has changed.
The changes in hardware options and solutions have been accompanied by significant enhancements in ease-of-use and usability. There’s a bit of debate raging these days over where the centre of analytics gravity is going to be. Some contend that without enough data scientists, analytics will advance too slowly to have as much impact on the business in the near term as it should. Others argue that no matter what happens, the expertise needed to actually use these tools will always require the specialised knowledge of the business analysts. And, finally, there’s a camp that says analytics is about to become democratised in a way that puts the power of analytics in the hands of every user.
With the clean, simple interface of Visual Analytics, SAS are clearly in the latter (third) camp. In my own opinion, we need a continuum of skills and tools that accommodates the second and third camps. The experienced and skilled data analyst will develop sophisticated models, with robust measures in-place to ensure the quality of the models, and the models will be seen as offering competitive advantage; less-knowledgeable users will use their tools to understand the data better, and make simple inferences.
SAS have a range of solutions across the continuum and I look forward to seeing recent developments for all of them in San Francisco next week.
| This post was kindly contributed by NOTE: The Blog of RTSL.eu - Development With SAS - go there to comment and to read the full post. |