Too bad we can’t put to work the delicious usage data gathered by libraries.
Research libraries may not know as much as click-obsessed Amazon does about how people interact with their books. What they do know, however, reflects the behavior of a community of scholars, and it’s unpolluted by commercial imperatives.
But privacy concerns have forestalled making library usage data available to application developers outside the library staff, and often even within. And the data are the definition of incompatible: Libraries collect them in different formats at different levels of granularity and at different time scales, making them hard to work with.
But suppose we could get at it. Library search engines could be tuned to what’s shown itself to be relevant to their communities. Researchers could explore usage patterns over time and across disciplines, schools, geographies, and economies. Libraries could be guided in their acquisitions by what they’ve learned from the behavior of communities around the corner and around the globe.
We can dream, but solving the policy and technical problems intelligently would take many years and probably more will than we can muster. If only there was a big, dumb way to start putting community-usage data to work quickly.