Analyst comments about SAS’ 24th annual analyst conference continue to dribble out. Ordinarily, events like this produce a storm of Google alerts, but this year the quiet speaks volumes. Yesterday, Tony Cosentino of Ventana Research published his perspective on the conference, writing at length about SAS Visual Analytics; link here.
Here are a few quotes from Mr. Cosentino’s post, with my embedded comments.
“For SAS, the competitive advantage in Big Data rests in predictive analytics…
…a capability that is completely absent from the current version of SAS Visual Analytics, the software featured in Mr. Cosentino’s article. The big “news” of the analyst conference is that SAS says they plan to add some toylike predictive analytics to Visual Analytics this year, which will give the application functional parity with, say, MicroStrategy vintage 1999. I don’t completely understand why this is news at all, since SAS said they would do this at the analyst conference last year, but spent 2012 attempting to sell their other in-memory architecture without visible success.
“…according to our benchmark research into predictive analytics, 55 percent of businesses say the challenge of architectural integration is a top obstacle to rolling out predictive analytics in the organization.”
No doubt this is true, and SAS’ proprietary server-based architecture is one reason why this is a problem. SAS/STAT, for example, is still one of the most widely used SAS products, and it exports predictive models to nothing other than SAS. SAS Visual Analytics simply adds to the clutter by introducing an entirely new architecture into the mix that is hard to integrate with legacy SAS products in the same category. For more details about the data integration challenges posed by SAS Visual Analytics, see my previous post.
“Integration of analytics is particularly daunting in a big-data-driven world, since analytics processing has traditionally taken place on a platform separate from where the data is stored…”
A trend that continues with SAS Visual Analytics, which is deployed on a platform separate from where the data is stored.
Jim Goodnight, the company’s founder and plainspoken CEO, says he saw the industry changing a few years ago. He speaks of a large bank doing a heavy analytical risk computation that took upwards of 18 hours, which meant that the results of the computation were not ready in time for the next trading day.
Banks have suffered serious performance issues with analytics for more than “a few years”. And 18 hours is pretty good compared to some; there are organizations with processes that take days and weeks to run in SAS.
Goodnight also discussed the fact that building these parallelizing statistical models is no easy task. One of the biggest hurdles is getting the mathematicians and data scientists that are building these elaborate models to think in terms of the new parallelized architectural paradigm.
Really? Parallelized algorithms for statistics and data mining are hardly new, and commercial versions first appeared on the market in 1994. There are companies with a fraction of SAS’ headcount that are able to roll out parallelized algorithms without complaining about how hard it is to do. A few examples: Alpine Data Labs, Fuzzy Logix, Revolution Analytics (my current employer) and Skytree.
The biggest threat to SAS today is the open source movement, which offers big data analytic approaches such as Mahout and R.
If this is true, SAS Visual Analytics is not an effective response because it caters to a completely different user persona. The biggest threats to SAS today are IBM, SAP and Oracle, who have the analytic tooling, deep pockets and credibility to challenge SAS in the enterprise analytics market. SAS Visual Analytics seems more like an attempt to compete with SAP HANA.
At the same time, SAS possesses blueprints for major analytic processes across different industries as well as horizontal analytic deployments, and it is working to move these to a parallelized environment. This may prove to be a differentiator in the battle versus R, since it is unclear how quickly the open source R community, which is still primarily academic, will undertake the parallelization of R’s algorithms.
Actually, it’s already done.