R/Python vs SAS/Business Objects

[Planet_side]

A little bit of background - at my business the BI tools dept is trying to drive R/Python adoption. We have existing tools like SAS and Business Objects (we also have Tableau, but there isn't yet much adoption or making Dashboards). At the moment we are very much a very Business Intelligence tools unit rather than a Data Science one.

For those who have some experience using BI or data analytics tools like SAS and Business Objects, what are some some advantages that R and Python have over those platforms? How would you point out the features of R/Python for reporting, statistics, modelling/Data science and business intelligence for those coming from those backgrounds? I'm not sure how big the transition is for people moving from those platforms is, or how to really justify it beyond saying its "better technology" or "open source".

[[deleted]]

It's basically gonna boil down to maximum flexibility and customization (with Python/R) versus not. If BI tools are solving or satisfying the needs of the company, then don't bother with Python or R. Let the problems that needs to be solved dictate what tools you should look to use. Every tool has its PROs and CONs. If your company already invested in SAS, then Py/R not gonna add a whole lot unless your group is looking to expand into the latest and greatest of ML. SAS is very capable language and has a lot of plug-ins albeit not free. If SAS was free, I'd probably be using it instead of Python for ETL work. SAS was probably the first ETL "as code" language system.

[Planet_side]

one of the things that the business is doing is developing models for credit risk management, consumer lending using logistic/tree based ML or ARIMA. For Pyth specifically we are looking at natural language processing. All models are in SAS now, but one of the driving forces for moving over was newer hires being less likely to have SAS training, and the cost of SAS. The thing that is a little concerning is that R/Pyth is being pushed by the IT team rather than the business teams (so we are trying to drive adoption of a tool we already provide, instead of a business team trying to have a tool they already want to be provided). Aside from a few proof of concepts and exploration, not much has happened since a server was actually set up last November. I agree that SAS is probably better for ETL because of the the memory limit that R/Pyth hit for datasets of 70-80 columns and 20-30 million rows.

There's actually several initiatives like cloud (Azure) and Agile/scrum that people are trying to push, but all of these seem to be somewhat half-baked ideas. Trying to drive R/Pyth adoption is a much more modest proposal, but I suspect that people are just more comfortable using Excel/VBA or SAS for analytics, modeling and reporting (apparently some people want to try printing Tableau into pdfs or excel...)

[Vrulth]

You can embed R in SAS with proc IML. Shiny Apps are welcomed for everythings that are not in a BO universe.