![]() ![]() SAS is extremely efficient at sequential data access, and database access through SQL is extremely well integrated. As noted here, there is a massive amount of inertia/legacy behind SAS but SAS just like R is a way to a means, not the means itself. I have worked as effectively a SAS programmer for the last seven years, next to me a co-worker has been programming SAS longer than I have been alive. Stata is preferred in economics and policy-related circles, and the more I learn SAS, the more I like Stata. there's no brand name and the impact factor behind the online journals. Free publishing via professional societies will make it cheaper, people prepare their submissions in LaTeX these days, so they are camera ready, and the same people will be providing the peer review, so there will be no quality setback on any of the dimensions. In a sense, this is a similar story with the academic publishers: they are riding a tide of the end users maintaining their subscriptions out of necessity a university without subscription to Nature is not really a university. For those of you who worked in maintaining your statistical packages. (My experience doing this is that my Stata code is generally about three times shorter I once had a project converting SPSS code into Stata where I made it about 20 times shorter. Rewriting this in R or Stata would take a few years, the resulting code will become more flexible, more efficient, more transparent, easier and cheaper to maintain, but nobody will pay for such refactoring. The amount of production code accumulated since then in SAS in pharma and government is unimaginable, tens of thosands of human years. SAS was out there since 1970s, and at the time it was the only effective, by then-standards, scripting statistical language. And when you have a good quality production code that is known to do the job, you don't change it. On top of what gung has correctly identified here, the biggest issue in the corporate world is legacy. I think #2 ignores several facts: there is some vetting that goes on with R, many of the main packages are written by some of the biggest names in statistics, and there have been studies that compare the accuracy of different statistical software & R has certainly been competitive. Personally, I only think #3 has any legitimate merit, although there are approaches to big data that have been developed with R. Thus, if your data approaches the limits of your memory, there will be problems. Big data: R performs operations with everything in memory, whereas SAS doesn't necessarily.Distrust of freeware: I've had several people say they aren't willing to accept results from R because you don't have a for-profit company vetting the code to ensure it gives correct results before it goes out to customers, lest they end up losing business.(Making it more difficult, the way you think in SAS and R is different.) This can apply to anyone who might have to send you code, or read / use your code, including managers and colleagues. Tradition / habit: people are used to SAS, and don't want to have to learn something new. ![]() ![]() ![]() SAS® Intelligent Planning Cloud Ensure on-shelf availability through a hyperaccurate demand planning service.I think there are several issues (in ascending order of possible validity): SAS® Identity 360 Protect your organization from digital and identity fraud throughout the customer journey with a SaaS solution that delivers affordable, high-end identity verification and fraud detection. SAS® Energy Forecasting Cloud Optimize decisions, reduce computing requirements and unburden your IT organization with the highest-quality, AI-embedded short-term and very-short-term forecasts – delivered as a service. SAS® 360 Plan Centrally manage, integrate and streamline all marketing planning activities so you can be agile and accurate in accounting for marketing investments. SAS® 360 Match Take control of your advertising using analytics to quickly update your digital advertising ecosystem. SAS® 360 Discover Capture behavioral information across your brand’s digital customer experience and extend past traditional digital analytic reporting. SAS® 360 Engage Reach the right audience on the right devices with omnichannel marketing capabilities. ![]()
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