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If you're only using your database to keep track of your stuff, but not to decide what to do next with your stuff, you're falling behind. The "big data" hype has been droning on about the value of analyzing data for more than a decade, and there's no reason FileMaker can't play, too: statistics, business intelligence, data visualization, machine learning, or whatever other flavor of data analysis you want. I concede that the FileMaker community has some catching up to do relative to other software tools — and we should be ashamed of ourselves for it. Some folks might think it's FileMaker Inc.'s job to build better data analysis features before we can do anything interesting. While that would be helpful, it's unreasonable to expect FileMaker's in-house engineering team to catch up with other tools. The standard data analysis tools for other languages are not built-in features of those languages, but add-ons built by the communities of users of those languages. It's up to us to make FileMaker useful for non-trivial analyses. Some folks might think "big data" is too big for little FileMaker to handle. First, "big data" is not a well-defined concept, but it usually has something to do with any data analysis task on enough data to be difficult to handle with the tools at hand. "Big data" is hard for everyone else, too, no matter what tools they're using, or else it wouldn't be called "big data." Second, there's no reason your data have to be "big" for an analysis of your data to be valuable. Granted, more is often better, statistically speaking. But some of the best minds on Earth have spent the last century or so extracting as much insight as possible from as little data as possible. They came up with some really good techniques. The techniques that are still most common today were originally designed to be executed with pencil and paper at a time when "calculator" was a job title rather than a pocket-sized electronic device. We have FileMaker, so we have no excuse. The "small" and "medium" data in our FileMaker applications are just as worthy of analysis as anything Google has. "Big data" gets all the news coverage, but most data analysis is not big data. FileMaker's biggest competition, market-share-wise, isn't Python or R or C++ or Hadoop. FileMaker's biggest competition is our ancient arch-nemesis, Excel. You're not going to let the rival team have a leg up on us, are you!? We can do better than Excel. FileMaker presents some advantages for data analysis that have been neglected for long enough. The most sophisticated statistcal analysis in the world is useless to an organization that fails to act on the conclusions of that analysis. Analysis has to feed back into operations. Conventional analysis workflows have to export data out of operational systems and import it into separate analytically-oriented data warehouses after excruciating conversion processes, then data are analyzed by another menagerie of separate tools, and finally those results have a long trip back through layers of strategy meetings and bureaocracy to influencing action. Data clean-up and conversion as it passes between each of these systems is the overwhelming majority of the labor spent on "big data" projects. That's lame. Our operational data is already in FileMaker. Doing analysis in-place in FileMaker spares us the most expensive problems of "big data," making us more nimble. The results of our analyses can immediately feed back into operations. Some folks have suggested that FileMaker may just not be the right tool for most analyses. Why skin a cat with FileMaker's multi-tool when you could use R's bowie knife? Because "the right tool for the job" is often whatever tool happens to be in your hand. FileMaker is the tool in our hands (or else what are you doing here?), and we could do a lot worse. So what help do you need to add data analysis features to your FileMaker applications? Have you done anything cool to show off? The administrators of this site saw fit to grace us with this forum to discuss such issues. (Thanks!) So start talking already!
Anyone have any familiarity with using statistic java classes with SM? Specifically we would like to use the JSC (Java Statistical Classes) found here: http://www.jsc.nildram.co.uk We need some heavy stats work and having access to this jar would be a big help. Ideas? By the way, we are currently interested in Fisher’s Exact test. Thanks