Will the SAS language continue to hold its own in data science?
One of the latest and most innovative offerings is SAS Viya 4, which is a new functional API building that makes it easier for developers to interact with data scientists.
"Technology has the longevity of a banana": these are the famous words of Scott McNealy, the founder of Sun Microsystems. This is especially true for the SAS programming language, which has long been an essential tool for data scientists around the world. However, with the new design, many believe that SAS seems to be going backwards. How true is this, and what does the future of the SAS language in data science hold? SAS is still relevant even 40 years later.
SAS, formerly known as the Statistical Analysis System, was developed by the North Carolina State Institute in the 1970s, which was later incorporated as the SAS Institute. Apart from SAS software, its parent company with the same name has become one of the leading companies in the research sector, thanks to its extensive statistical offerings, learner-friendly GUI and unparalleled technical support. One of the latest and most innovative offerings is SAS Viya 4, which is a new functional API building that makes it easier for developers to interact with data scientists. It's a cloud-native, powerful analytics platform that turns raw data into better, faster decisions. It can be tailored to various business challenges such as combating fraud, managing risk, understanding customer needs and optimizing the supply chain.
Since SAS provides users with a plethora of product capabilities, including asset performance analysis, analytics for IoT, decision making and economics, it is highly desirable to analyze and understand the needs and requirements of customers. Powered by new technology
After listing the functions and capabilities that seem to keep SAS at the forefront of data science, one cannot ignore its major, serious flaws.
One of the biggest disadvantages of SAS is its business and high cost. This limits its use by startups and small businesses, and is full of freeware downloads like R and Python. This is considered the main reason why SAS is overshadowed by R and Python, despite offering more features and a higher support environment; and this trend is expected to continue. Also, while SAS is working to improve its graphics capabilities, the options are still very limited compared to R and Python. In addition, the lack of clear documentation in SAS has increased interest in R and Python among data scientists. SAS is also known to operate in a controlled environment, which affects the time it takes to update to the latest features and capabilities. On the other hand, some may see this as an advantage because working in a controlled environment means that the equipment is properly tested and the chance of error is greatly reduced. Does this mean that the golden age of SAS is over?
Given its serious weaknesses, is it safe to assume that SAS will not have another game in data science in the future like 2021? Well, the answer is no and yes to both.
Despite its many flaws, it still manages to hold the top spot in popular surveys. For example, its continuous promotion and innovation to meet the needs of today's challenges made it a leader in Gartner's Magic Quadrant for Data Quality Solutions 2020. It is based on "the perfection of vision and ability action". The report chose it as a leader based on its strength, long-term market presence and trusted brand, exemplary performance and management platform, and ease of use management as it offers an attractive drag-and-drop interface, while It provides support for code formats. Apparently, this is the seventh year in a row that SAS has received this recognition.
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