Manager’s Dilema: SAS vs R vs Python

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There are countless articles on this topic already, and I must begin by accepting that I am quite late to this superstar battle. However, every time these champions of analytics face off, experts argue on the basis of Cost/Affordability, Ease of Learning, Data Handling Capability, Customer Support and Graphical capabilities. Though all these are absolutely important, I will attempt to cover aspects that are often not covered. All of these languages have their fair share of supporters and detractors and I don’t fault them for holding such prejudices.
But as a manager tasked with the creation of a Data Science team or a project manager tasked with a Data Science transformation project, it’s very important to ask the right questions and surprisingly it’s not which is the better analytics language: SAS, R or Python. The right question is, which approach best fits with your organization’s goals and resources: both human and financial? The idea of this article is to help you find the key that unlocks your door to a successful data science implementation or transformation.

‘The right question is which approach syncs best with your organization’s goals and resources, both human and financial.’

I will use scenarios for some of the most common situations because there are no silver bullets, what works for X may not work for Y. Therefore, during this series, we will use cases to help recreate various situations a manager finds himself/herself in while deciding between SAS, R, and Python.

Use Case I: There is no existing data science team in the organization, the company generates a lot of transactional data (structured/unstructured), but the senior management is wondering whether data science can help in improving the company’s bottom line. And you have been tasked with “making it happen”.

If the above use case pretty much sums up your situation then I would recommend going for R and hiring data scientist who is trained in R. There are several reasons for my recommendation.

  • This is almost 2017 and the company doesn’t already have a preexisting data science team. It implies that there is a lot of apprehension in the senior management regarding data science. Therefore, the team you build will need to prove that data science works in your business. So you would need to build a team which has a strong background in statistics / mathematics and obviously R skills.
  • The cost will play an important role in motivating the senior management to prefer R over SAS since those annual SAS licenses do not come cheap.
  • R leads the analytics industry with its beautiful and easily customizable graphics. But SAS plots are not that easy to customize unless someone has a very good understanding of the working of the SAS graphics package. R is built for customization in plots.

Though for this particular use case you can still make a good defense for python, but it is not that easy to find good python programmers who also have a strong background in statistics. Therefore I would still recommend you to go with R. The fact that computing power is getting cheaper with each passing day and SAS licenses are not getting any cheaper, it becomes harder to recommend SAS, but having said that SAS still is far from being obsolete.

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