What is SAS (Statistical Analytical System)?
SAS (Statistical Analytical System) is one of the most common data analysis tools. It is commonly used for different purposes such as data processing, data mining, report writing, statistical analysis, market modelling, development of software, and data warehousing.

What is the future of SAS?
SAS continues to be a commonly used programming language and a large data and analytics software business remains the SAS Institute that creates, manages and licenses SAS software. Although other tools and programming languages like R and Python are gaining popularity in statistics, data processing, and data science, the SAS Institute continues to be a globally successful business with a large market share. SAS continues to broaden its product portfolio with new and creative tools such as SAS Viya and SAS Visual Analytics, thereby helping it stay competitive in the data science and business intelligence markets.

1- Pharma Industry & Drug regulators:
Clinical trial data analyses depend entirely on SAS, which is the preferred method for all drug regulators worldwide. Pharma firms have invested millions of dollars in developing software (SAS macros) for the purpose of monitoring and analysis, and it won't just go down. In fact, the FDA and other regulators would need to verify these results of clinical trials, and they will thus be much more hesitant to substitute SAS with some other method, because the associated expense and time are enormous.

2- Statistics edge:
Born at North Carolina University, SAS is one of the popular statistical analysis software, and this is one of the reasons why the pharmaceutical industry and banks, where a misanalysis may cost the business a billion dollars, rely so much on it. But R is also slowly gaining popularity within the Statistics community due to its free availability, good graphics and growing online support.

3- SAS research & development:
Over a very long period of time, SAS has been offering analytics solutions to many industries and so they understand industry and their customers better than any other tech company. To survive on the market, one must adapt with evolving technologies and SAS is very pragmatic in its approach, for example, Hadoop is not quite old in the big data industry, but SAS has a well-established company supporting Hadoop and other Big data components.

4- Brand name & trust:
We have a saying that means it is highly likely to make more profit and is being well promoted. In the case of SAS, it is generally known and trusted in the industry even with its high cost. If we expect R or Python to replace SAS in the analytics domain (anytime in the future), then we may have to wait for a major player (such as-IBM, Google) to embrace R / Python and build a platform similar to SAS and invest heavily in its marketing.
All comes with an expiry date SAS might one day go out of fashion, but with its current customer base and applications in various industries (particularly in the pharmaceutical industry) it will take at least about a decade.

By delivering a new generation of business intelligence tools and services that generate true enterprise intelligence, SAS tools has proved to be the industry leaders. The SAS Institute is the largest privately owned software organization in the world. It is also the only vendor that completely integrates leading data warehousing, analytics and traditional BI applications, to create intelligence from massive amounts of data.
SAS has a huge part to play in analytics and big data in the future. Everybody in the business world today must be aware of the advantages of possessing SAS expertise and understand that in current and future markets this expertise are in demand.

5- SAS vs R vs Python:
SAS: -
SAS is a Versatile method and is the integrated software management program and the pioneer in the field of data analytics. This app has a lot of features to provide excellent technical support including strong Interface, and others. SAS lets you perform the following tasks Data Entry, retrieval and management

● Writing reports and graphic design
● Statistical and quantitative analysis
● Market modelling and decision support
● Operations Research and Project Management

Reputable companies such as Barclays, Nestle, HSBC, RBS, Wells Fargo, Volvo and BNB Paribas use SAS.

R: -
R is a programming language for statistical computing and graphics which Ross Ihaka and Robert Gentleman developed in the year 1995. It provides a vast variety of mathematical and graphical techniques. It is a highly extensible Open Source path. It is a programming language which is simple and effective. It is more than a pure system of statistics. It does the job below Easily manipulates packages

● Manipulates strings
● Works with regular and irregular time series
● Visualize data
● Machine learning
R is used by top-rated companies such as Bank of America, Bing, Toyota, Uber and Foursquare.
Python: -
Python is an object-oriented programming language that has a clear syntax and readability. It was created in 1991 by Guido Van Rossem. It is easy to learn and will help you work more quickly and effectively. It has become more popular in a short period of time because of its simplicity.

Python is used by such well-known businesses as ABN-AMRO, Quora, Google and Reddit.

6- Customer support & community:

Compared with the other two languages SAS is the highest one on customer satisfaction and operation. SAS has a dedicated business and customer care, and a culture. If you have any technological issues, you can directly contact the support centre.
R has a wide community online but no Customer Support Centre. You will get support from them but not immediately.
Python lacks a Customer Support Centre. It provides assistance to its clients but not to SAS level

The Reasons are:
● SAS full stack has a big coverage, so it's a complete solution essentially. They have been using SAS for ages and have systems built around SAS
● SAS is safer than open source tools like R and Knime, very important for Data Security and Basel II norms and more statutory norms
● Together with SAS JMP and SAS Visual Analytics, SAS has implemented big data capabilities.
● Ability level of production: SAS will bring the analytics into banking and financial systems development. R doesn't have this, but we need to see where Revolution R (bought from Microsoft) is stacking up
● It's easy for a coder, but a non-coding person like me can learn it pretty quickly.
● SAS gives you the highest degree of versatility in configuration.
● Market solution, banks and other companies prefer SAS over open source software like R, python. (Although I believe R and python need to be trained for long-term sustenance as well).

SAS has been industry leader in data analytics, and it will stay leader due to above mentioned reasons. With presence of other tools in industry, SAS becomes more comparative and come up with more innovative tools. In addition, the job market for SAS Professionals will remain open and may open variety of avenue with more and new skills of Data Science.

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