data mining, pattern recognition, prediction, statistics, tracking

For such a specific-sounding topic, "analytics" is a very wooly term. At a nuts-and-bolts level, we're referring statistical and data analysis tools, and Pomona ITS does supports several of them. So if you're looking for Qualtrics or R step this way.

But we're also using this topic for things like data mining, big data, natural language processing, predictive modeling — each a complex subject it its own right. One of the reasons "digital humanities" is so hard to pin down is that many of the techniques involve these rapidly evolving fields of data analysis. And as nearly all education today has a digital component, from LMSes to flipped classrooms to fully online classes, the difference between educational assessment and data analytics is getting increasingly thin.

If that wasn't enough for this overdetermined topic, "analytics" in the broader sense is also the basis for what is often referred to as artificial intelligence. At a certain point, the difference between a system that can instantly return a relevant, well-informed answer to any question and an AI might come down to whether the user interface looks like a search box or a friendly robot.

Innocuous sounding "analytics" is also a catch-all term for the many ways marketers and aggregators have to track all of our behaviors, storing every ephemeral act, and parsing it all with ever more powerful tools. The result is a pattern recognition ability which, at certain resolutions, looks disturbingly like prediction.

That is a bit scary. But the really frightening thing is a having faith that analytic systems can have a neutral, predictive power which does not reflect and magnify the biases of their creators. Humans still write the algorithms, select the data, and decide what "accurate" means, making the seemingly dry realm of analytics dense with ethical questions.

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