SAS

SAS by sennchi

Predictive Analytics And Machine Learning Solutions

SAS reimagines its data science portfolio. sAs is unifying its comprehensive portfolio of data science solutions under sAs visual suite. it brings together world-class data prep, visualization, data analysis, model building, and model deployment. this unified tooling approach provides a consistent user experience that data scientists need to build even the most sophisticated models. sAs’s vision for data science is not limited to innovation in tools. it has been quick to jump on new, promising analytical methods across multiple disciplines, such as statistics, econometrics, optimization, machine learning, deep learning, and natural language interaction. it recently introduced support for calling sAs analytics from python, Java, and lua, leveraging open source data science notebooks. A key challenge is that sAs has a target on its back by the open source zealots that summarily and wrongly dismiss sAs as old school. customers complain about premium pricing compared with other solutions

SAS

SAS is based in Cary, North Carolina, U.S. It provides many software products for analytics and data science. For this Magic Quadrant, we evaluated SAS Enterprise Miner (EM) and the SAS Visual Analytics suite of products, which includes Visual Statistics and Visual Data Mining and Machine Learning.

SAS remains a Leader, but has lost some ground in terms of both Completeness of Vision and Ability to Execute. The Visual Analytics suite shows promise because of its Viya cloud-ready architecture, which is more open than prior SAS architecture and makes analytics more accessible to a broad range of users. However, a confusing multiproduct approach has worsened SAS's Completeness of Vision, and a perception of high licensing costs has impaired its Ability to Execute. As the market's focus shifts to open-source software and flexibility, SAS's slowness to offer a cohesive, open platform has taken its toll.

STRENGTHS

• **Broad base and good visibility and mind share: **SAS again leads in terms of total revenue and number of paying clients. Customers are familiar with its brand and its extensive support for multiple use cases. Reference customers indicated that SAS is the vendor that most frequently appears on shortlists for product evaluation. Its partner network enhances its visibility and support.

• **Modern architecture: **SAS Viya represents a modernized architecture and the foundation of SAS's technological developments. SAS EM can fully exploit the capabilities in SAS Viya architecture, which gives customers multiple deployment options. SAS's Visual Analytics suite is generally available.

• **Appeal to a broad range of users: **SAS's offerings appeal to all types of user — from business analysts to citizen data scientists to expert data scientists. The Visual Analytics suite on the Viya architecture contributes to this appeal.

• **Operational excellence: **SAS's comprehensive worldwide support infrastructure is unmatched. Customers choose SAS for its robust, enterprise-grade platform capabilities, from exploration to modeling to deployment. Reference customers gave high scores to SAS's documentation, customer and analytic support, and overall service and support.

CAUTIONS

• **Pricing and sales execution: **SAS's reference customers gave scores for product evaluation and contract negotiation experience that were in the bottom quartile. In addition, SAS's pricing remains a concern. Free open-source data science platforms are increasingly used along with SAS products as a way of controlling costs, especially for new projects.

• **Complex and confusing multipronged approach: **Offering two platforms that are not fully interoperable and that have multiple components with different dependencies increases confusion and complexity in terms of managing, deploying and using SAS's products. The coexistence of SAS Viya and other SAS platform versions perpetuates the perception of a lack of cohesion. Although SAS has made some progress in this regard, migration remains an issue for those that want to exploit Viya's capabilities but are not currently on that architecture.

• **Product and sales strategy: **New entrants to this market have changed its landscape by offering open, innovative platforms and new approaches. The increased competition they bring requires "traditional" vendors, such as SAS, not only to respond but to proactively provide comprehensive, cohesive platforms. Some reference customers reported that SAS was slow to support new technologies and to act on requests for new features.

• **Lack of capabilities across both platforms: **Both SAS EM and the SAS Visual Analytics suite received low scores, in comparison to other vendors, for data access capabilities and flexibility, extensibility and openness, and coherence and collaboration. Reference customers also gave SAS low scores for its lack of open-source support and deep-learning algorithm capabilities (although Viya partially addresses this issue).