It’s not a tool for programmers. It’s a toolbox for people doing other jobs, who happen to know how to program.

Would we invest in SAS if we were starting today from scratch? No.

But none of SAS’s biggest customers are starting from scratch, and things like continuity, risk, and embedded institutional expertise are incredibly important to these customers, in a way that might not be obvious if you’re coming from the fast-paced world of startups who have the luxury of starting with nothing.

In the endgame though, I can’t see how SAS will be able to resist the cost pressure coming from the Python/R/Hadoop/FOSS-in-general ecosystem, as those tools continue to mature.

Even COBOL will die one day.

The specific syntax of COBOL maybe, but the concept of a COmmon Business Oriented Language lives on in Java

But their licensing model is straight out of the 90’s. there’s no point using automated load balancing if your license costs are a fixed multi-million dollar per year cost.

They have moved to a web-based client. It’s great that it’s not stuck on Windows. But it’s a very closed client in that it only works with SAS products. As the article says, no Python or R. And as a web based IDE it’s a pale shadow of the likes of Cloud 9.

If’s disheartening (but not surprising) to hear a SAS employee say they don’t care about the core language any more, they make most of their money from BI tools aimed at non-programmers.

SAS (previously "Statistical Analysis System") is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.

SAS was developed at North Carolina State University from 1966 until 1976, when SAS Institute was incorporated. SAS was further developed in the 1980s and 1990s with the addition of new statistical procedures, additional components and the introduction of JMP. A point-and-click interface was added in version 9 in 2004. A social media analytics product was added in 2010.

According to IDC, SAS is the largest market-share holder in "advanced analytics" with 35.4 percent of the market as of 2013. It is the fifth largest market-share holder for business intelligence (BI) software with a 6.9% share and the largest independent vendor. It competes in the BI market against conglomerates, such as SAP BusinessObjects, IBM Cognos, SPSS Modeler, Oracle Hyperion, and Microsoft BI. SAS has been named in the Gartner Leader's Quadrant for Data Integration Tool and for Business Intelligence and Analytical Platforms. A study published in 2011 in BMC Health Services Research found that SAS was used in 42.6 percent of data analyses in health service research, based on a sample of 1,139 articles drawn from three journals.

— https://en.m.wikipedia.org/wiki/SAS_(software)

TL;DR: It’s kind of a big deal in analytics.

However, to be somewhat of a hypocrite, I don't mind abbreviations for broadly known terms, such as SaaS, DevOps or db. I guess it boils down to the context.

Well, at least SaaS and DevOps are easily googleable. Still, in any type of written article, I would just take the time to write out the abbreviated thing in full the first time. Takes maybe 2 seconds and 10 letters more, and you won't have people like me complaining :)

That said, the rest of the article is very accessible for me - my only confusion was what SAS is. Maybe a link to a page that explains what SAS is would be a good solution?

Then, our company acquired a competitor to SAS; SAS was booted out and literally, overnight, we were asked to stop using SAS and switch over. Thanks to this development, my team and I scrambled to port it over to the new tool. Of course, the transition wasn't smooth and a lot of our programs couldn't be migrated. This is when I began exploring Python and fell in love with it. I rediscovered programming, powerful ML libraries and the awesomeness of the open-source paradigm.

I end my random anecdote to say that I'm grateful for this (unexpected) development that helped me accelerate my career in data science, which would have definitely not happened if the abrupt removal of SAS hadn't taken place. I would've most likely still be churning regular dashboards built on legacy SAS code for some bank.

So anyway, the presentation includes showing off a Jupyter notebook and showing how you can easily use pandas to load in your data set, build a model (with Viya), and plot the results with matplotlib. Standard data science stuff, but distributed automagically thanks to Viya.

During the QA, I asked the following questions:

- So you are basically attempting the same as what Spark has done, and are just using R/Python/... as a client? Yes, but we're way more powerful, manageable, etc...

- Can I see the source code of your models, or build new ones myself? No... well maybe, you can write them in SAS base and call them.

- Can I at least export the model to something like PMML? Oh yes you can export (shows this off)... to SAS base code.

- Do you have GPU support for those fancy convolutional neural networks? No (although I've heard this has changed, though I would still put my trust more towards Tensorflow or Pytorch).

It kind of seems like a last attempt to get some new lock-in going. Even Apple's re-open sourcing of turi (https://github.com/apple/turicreate) is more honest (and more powerful, actually).

I like SAS, they have some very smart people working there and some great consultants (PhD's, often), but there's too much sales going on. This is what killed IBM as well.

If that is the strategy I'm not entirely sure it is well implemented. The version available for windows using students requires running a virtualized linux in the background to access a browser based, sluggish IDE like interface.

Compared to SAS, R and Python look like an usability dream.

edit: scala with spark is increasing in use as well.

Granted, I worked about 30 minutes from SAS's HQ in Cary, but still.

With the generation of programmers who wrote the code retiring it’s even more entrenched because even though they rely on that code dearly, few if any people remember why it does what it does how it does it.

For many big COBOL systems that’s long past; in many cases the programmers that were coming on board when that generation retired are near retirement.

Do any of those legacy systems work on "hard problems" similar to how FORTRAN is still used in fluid dynamics, physics, crystallography and chemistry?

One would look at google. https://www.tutorialspoint.com/cobol/index.htm

Where SAS succeeds is they make it very easy for non programmers to do things like query databases extract, transform, merge data, create computed columns and output graphs and reports.

Enterprise Guide is 100% scriptable and has a programming language underneath it the "SAS langauge" basically anything you can do in the GUI has code that is generated in the backend which user can edit directly if they desire. In old versions of Enterprise Guide code the GUI generated tended to be pretty inefficient (especially the SQL side of things) so a lot of people hacked the code directly and bypassed the GUI - nowadays it has gotten pretty good not as much need to hand optimize things.

Best analogy I can give is Enterprise Guide is like Excel on steroids.

SAS also has very powerful statistical capabilities beyond what something like Excel offers. We leverage a lot of this stuff when we need to build engineering models, things like Time Series Analysis, Statistical Process Control, Linear and Non Linear Modelling (Including Stepwise and Logistic Regressions). There is even some Optimization solver type stuff I've used in past (though MATlab is more often used in my org for this work). SAS kind of takes the kitchen sink approach in R you need to install external packages from CRAN to unlock all this stuff in SAS tends to be "baked in". I've found if I read about a statistical technique in a paper good chance it is available in SAS - good example is a few years ago a grad student came to our plant and helped us perform some analysis using a technique called "Projection to Latent Structures" (PLS) to identify relationships between various factors on plant causing yield loss. At the time this wasn't very common - I think it is used in machine learning nowadays but SAS had a PLS procedure available for us to use (kind of like a function in other programming languages) and the documentation SAS provided was very good (especially for someone like me with engineering background rater than stats background).

The only other tool I know of that is remotely similar to Enterprise Guide is IBM's Cognos software and that is vastly more limited as far as the statistical modelling features are concerned.

My work is keen to try other software, in part due to license costs, I think. In the Last 6 months I've been on two training course for Microsoft Power BI, and Microsoft R-Open (which I think is just Open source R-Studio with some extra proprietary stuff thrown in).

I've now used both tools to do my day to day job. I managed to get by with each of them but I found usability fell short of ease of use of enterprise guide.

Power BI was nice and I think it has something to offer as far as data visualization but you need to do the actual modelling in some other language so doesn't really eliminate needs for SAS - more like compliments it.

R on the other hand is a lot lower level it has all the advanced statistics but you have to write the low level code yourself to unlock them which requires learning an entire programming language to build simple models. It will be a pretty hard sell getting Engineers (I'm talking mechanical, chemical engineers not software kind) and finance analyst people to use a tool like R.

I think R would hugely benefit from something like an "Enterprise Guide for R" - I.e a drag and drop GUI which generated R code for you.

Also I wouldn't say the SAS Languge itself is strictly an old-school legacy language (sure the origins of SAS are from the 70's and 80's and there is a lot of legacy cruft in the SAS programming language I'll give you that) - For me SAS is a little like C++ there is a heap of stuff that lives in the langauge for backwards compatibility with old programs but modern C++ program looks very different from early 90's C++ - same with SAS code.

I've found modern ODS graphics (SGplot, SGpannel, etc) was very much on par with "ggplot2" functionality in R which is what the training course covered for producing reports using R.

SAS to me is a bit like MATLAB (and hey we use both in Engineering world). Everyone hates this software and wants something better but nothing else approaches the usability of these tools so models continue to be built using them.

Now I work in a startup bank and since I could chose the tools we're now running PostgreSQL/R/Python (RStudio and Spyder). Lately the marketing department are also using Tableu.

It's another world when it comes to output and efficiency of the team I lead compared to using enterprise guide.

https://groups.google.com/forum/#!topic/comp.soft-sys.sas/oF...

Both actors appearing in that clip are total muppets but it seems relevant ;-)

It's very confusing that you don't get even a basic insight of what the article is about.

I also thought the airline first, but most people in USA have not heard of Scandinavian.