Archive for February, 2014

Watch It, Libraries

February 27, 2014

It seems like there’s been a lot of publicity about Netflix recently. About how effective their recommendation engine is … about how they’re leveraging their understanding of consumer tastes not only to distribute other people’s content more effectively, but to create their own original content aimed at specific markets, like House of Cards.

In fact, the title of this post is paraphrased from an article that ran in the Sunday Washington Post, Feb, 23, entitled, “Watch It, HBO.” The article is actually a version of an article that ran a few days earlier in Slate — you can read it here.

What strikes me above all in this article is the power of data analytics. Netflix, along with Amazon and other companies, is using what it knows about us, and about our aggregated behavior, to get smarter and smarter about serving us.

Meanwhile, there are libraries that throw away all their user behavior data every night — in the form of circulation records — in the interests of protecting user privacy. When they do that, they throw away the opportunity to use that data to improve their services. I think that’s a mistake.

I’m not advocating that librarianship abandon its commitment to  protecting individual privacy. But letting privacy concerns cut us off from implementing modern, more effective services is a primitive, ostrich-like response to the challenge. Wouldn’t a more mature approach be to recognize that we need to offer the benefits that come with retaining and analyzing user data — but at the same time we must manage the risks?

If we don’t, what will happen as people become more used to recommender systems, and the systems get better and better — and libraries are stuck in the mode of purely passive 1980s-era approaches to service?

There are several ways I can imagine that scenario playing out — none of them favorable to libraries. So, I hope that the leaders of libraries that still throw out their user data will rethink this, and develop sophisticated programs that address both the risks and rewards of retaining and analyzing user behavior data.

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