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SAS Operationalizes Data Science

SAS Operationalizes Data Science

I recently spent two days at the Ritz Carlton in Naples, Florida to hear from SAS about their latest and greatest product improvements and their strategy. The analyst conference showed some excellent progress from SAS in terms of providing tools to operationalize data science. Here are some highlights:

  • It’s all about choice. Ryan Schmiedl, VP of product management, made the analogy that if someone really likes diet coke they just aren’t going to be happy with a diet pepsi (even if diet pepsi is pretty comparable). This metaphor highlights the understanding by SAS that if a data scientist likes writing in R, that SAS will absolutely now let them write in R and have the ability to call SAS functions as part of a library. This is critically important as it effectively keeps all the data and processing within one ecosystem.
  • Move over SaaS. Going beyond “Software as a Service”, SAS is beefing up what it calls “Results as a Service”. This RaaS offering will couple cloud data and software services with consulting services and specialized expertise in particular verticals and functional areas (e.g. money laundering detection in finance).
  • Oracle and SAS in the clouds. Though unlikely to be a breakthrough relationship, SAS has shown its desire to be open and run where its customers want it to run. Thus SAS is running in the Oracle cloud as well as AWS, Azure, and Google.
  • Collaboration for acceleration. SAS is also helping its users make friends with each other. A new offering coming out later this year, “SAS Drive” will allow for collaboration among users for reports, projects, models and pipelines (SAS version of model workflow). This is significant as collaboration is what propels organizations from merely operationalization into acceleration.
  • Planting seeds for future SAS users. It was interesting that one of the very first things that Jim Goodnight, CEO at SAS, spoke about as he opened the conference was SAS’s programs to support college students. SAS is getting serious with their free offerings: over 1 million downloads of SAS University Edition, 4,000 subscribers to University Edition on AWS, and nearly 200 joint certificate programs at universities. SAS is expending a lot of effort reaching out and capturing new users. Expect these efforts to start to payoff and make SAS a viable alternative to other free tools for those emerging data scientists.
  • Push to the edge. SAS has a strong commitment to IOT and edge computing. They seem to understand that even with awesome cloud hardware, Big Data is still overwhelming our ability to process it. Pushing data science to the edge to decide what data gets kept and what data falls on the floor is becoming a big deal as CIOs and CDOs recognize the power of data science at the edge, and they acquire some humility about their inability to stop the tsunami that big data has pointed at them.
  • Show me those neurons. Playing catch up here, but SAS has really demonstrated an ability to change and improve. The interface to their tools is clean, up to date, and has clearly undergone significant user testing. And they demonstrated a very simple, yet powerful graphical interface for building deep neural networks. It's not rocket science, but it's very significant that SAS is willing to move in the right direction and invest in usability to support all user levels.

Overall I was impressed with the progress that SAS had made since last year’s conference. By way of analogy, it strikes me as similar to the changes that Microsoft has been able to make despite being a big company. SAS, though not as nimble as smaller companies, appears willing to make the hard decisions that benefit their customer and maintain their leadership role in the marketplace.

Stephen J. Smith

Stephen Smith is a well-respected expert in the fields of data science, predictive analytics and their application in the education, pharmaceutical, healthcare, telecom and finance...

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