SAS (previously "Statistical Analysis System") is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. SAS was developed at North Carolina State University from 1966 until 1976, when SAS Institute was incorporated.

SAS was further developed in the 1980s and 1990s with the addition of new statistical procedures, additional components and the introduction of JMP. A point-and-click interface was added in version 9 in 2004. A social media analytics product was added in 2010. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. 

SAS provides a graphical point-and-click user interface for non-technical users and more advanced options through the SAS language. In order to use Statistical Analysis System, data should be in a spreadsheet table format or SAS format. SAS programs have a DATA step, which retrieves and manipulates data, usually creating a SAS data set, and a PROC step, which analyzes the data

2019 Magic Quadrant: DS + ML

As Gartner puts it in its 2019 Magic Quadrant for Data Science and Machine Learning Platforms, “data science is a core discipline for the development of AI, and ML is a core enabler of AI, but this is not the whole story. ML is about creating and training models; AI is about using those models to infer conclusions under certain conditions. AI is on a different level of aggregation to data science and ML. AI is at the application level.” Gartner adds that, “Data science and ML models must be combined to work together with other capabilities, such as a UI and workflow management, to constitute an AI application. A self-driving car, for example, has ML capability, but its AI requires much more than that.”

SAS retains its long-held status as a Leader. Although the company faces threats on multiple fronts from other large vendors, maturing disruptors and open-source solutions, it retains a strong presence in the market. SAS’s Completeness of Vision is in the same class as many highly innovative competitors, but the company is falling behind in key areas such as deep learning and contributions to the open-source community. Its Ability to Execute is hampered by high and sometimes unpredictable costs, which cause existing and prospective customers to explore other options. Like other veterans of the data science market, in addition to focusing on new clients, SAS is embracing the challenge of supporting legacy customers and users while adapting to a rapidly changing landscape.

SAS’s long market presence and considerable staying power have earned it much respect from customers. Many reference customers praised its products’ quality, stability and reliability. That solidity might have come at the expense of a few advances (such as quick adoption of opensource capabilities), but it has not prevented SAS from innovating and staying on a par with many of its newer competitors.

SAS EM’s reliability throughout the analytics and data science life cycle is recognized throughout the market. From data ingestion and preparation to model production and deployment, the platform continues to deliver dependable results. SAS is well-placed to replicate that remarkable on-premises strength in a multi-cloud environment.

SAS VDMML received excellent scores for user interface and data exploration and visualization. It also received strong scores for data preparation and automation and augmentation. SAS VDMML appeals to citizen data scientists as well as code-focused data scientists and developers. SAS’s comprehensive worldwide support infrastructure is unmatched.

Customers choose SAS for its robust, enterprise-grade platform capabilities, which range from exploration to modeling and deployment. SAS also offers significant analytic and industry expertise, which customers rely on. Reference customers gave high scores to SAS’s documentation, customer and analytic support, and overall service and support.