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False dichotomy about Excel vs. DSLs like R. "90% of the world is using Excel and similar tools to analyze and visualize their data. [...] What’s the next step-
by christopheraden 13y ago
False dichotomy about Excel vs. DSLs like R.
"90% of the world is using Excel and similar tools to analyze and visualize their data. [...] What’s the next step-up from the spreadsheet? Learning how to code. Learning stats. Leaving the comfort of a spreadsheet’s visual display for R, maybe?"
To make this statement would be ignoring that numerous non-free statistical packages have offered GUI front-ends for years. SAS/EG, JMP, Minitab, and SPSS instantly come to mind. Two out of those four are even marketed directly to people as statistical extensions of spreadsheets. Granted, the "long tail" will still need to learn a little bit of stats, but I fail to see how this is a problem that Excel solves (unless we're talking only about plots).
I don't think it's a hard leap to consider that if the big-box statistical package companies realized how much of their money came from industry, they'd do what they could to make their software seem like an alluring proposition. Statistical software costs an order of magnitude more than Excel, so they'd need pretty good arguments on how to sell upper management that the business team actually needed an 8000 dollar piece of software.
I'm not sold that there's nothing in between Excel and R. From my experience, they require a slight learning curve (nowhere near the learning curve of going from Excel to R), but not an insurmountable one. What these solutions lack is the name recognition that Excel has, or decent integration into a MS Office stack (exceptions, of course--I remember seeing a statistics toolbox for Excel once), or they cost too much.
I think part of the real problem is that for a lot of companies, Excel is "good enough". There's plenty of stuff it can't do well. It chokes on larger data sets, has limited statistical functionality, poor scripting capabilities, and shaky random number generators. But it's good enough for people who don't want to do much with their data.
If they wanted to do harder-core analysis, they'd outsource it to an analytics team. This perspective comes from a viewpoint inside BigCo. The prohibitive cost of some of these solutions might be a harder pill for a small firm to swallow.