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SAS SWOT Highlights the Data Science Platform Market is Pivoting

By Jim Hare | September 25, 2018 | 0 Comments

Technology and Emerging TrendsArtificial IntelligenceAnalytics

Gartner just published an insightful SWOT about SAS Viya that underscores the transformation happening in the data science and machine learning market. For those of you not familiar with Viya, it is the vendor’s next gen cloud-ready platform with improved open-source support. SAS is one of five vendors who were rated “Leaders” in the 2018 Magic Quadrant for Data Science and Machine Learning (DS/ML) Platforms. But new rivals, open-source tools, cloud options are disrupting the market forcing established vendors like SAS to react and adapt. It is also about making the platforms easier to use by citizen data scientists who are often business users and application developers. How? By adding automation and embedded machine learning – what Gartner calls “augmented analytics”.

Some people may call this capability – automated machine learning (auto ML) – which is becoming a common feature with the more popular data science platforms including the AI/ML cloud service providers. But AutoML only addresses part of the data science pipeline; it is primarily focused on automating specific tasks such as algorithm selection or hyperparameter tuning.

Augmented analytics, on the other hand, offers the promise of adding automation and “smarts” into the entire data science process from data preparation to model building/selection to model deployment/management. It will make it faster and easier for expert data scientists to build and deploy larger numbers of models while reducing a lot of manual drudgery that currently exists. And, it will also make it offer self-service capabilities for citizen data scientists to select and run models without the need to code or dependence on professional data scientists or developers.

In fact, Gartner predicts that by 2019, due in large part to the automation of data science tasks, citizen data scientists will surpass data scientists in the amount of advanced analysis produced. This growth will be enabled by augmented analytics – augmented data engineering, augmented modeling, augmented deployment, and augmented operations – which will complement and extend existing data science and machine learning platforms to benefit from using machine learning to drive new sources of business value.

What does this mean?

Gartner believes that augmented analytics will be one of the disruptions in data science and machine learning eliminating current challenges such as the data scientist resource shortage while enabling new roles to participate. It will also help organizations scale their initiatives through automation and easier to use platforms for a broader range of users. To remain relevant, new and established data science and machine learning platform vendors alike need to be adding augmented analytics or they will become viewed as the legacy platforms of the past.

If you are a Gartner client, here’s the link to the SAS Viya SWOT analysis:

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