Alternative metrics and measures can also be too simplistic. It maintains defining what we think is good, and then measuring it. “We think X activity is good, we have counted lots of X and therefore we are good.” OK, but what now? What do we do with that data?

Most library activities we already know are good, and should be able to find out easily whether they are increasing or decreasing. Ask most services whether issues are going up or down on an annual basis and they’ll likely know. But ask about usage patterns on a day of the week basis, or hour of the day, or borrower age, and you’ll be far less likely to get answers. And yet that’s the data that is actually interesting, and can provide operational insight into a functioning library.

The increase in fine-free libraries has mainly been through some kind of domino affect, but the data to inform it has always existed, it’s just very few people actually looked at it. In fact it’s only really library services seeing that books are are still returned in other services that has led to them adopting it. When in reality analysing the data would have informed libraries to do this decades ago, as the data would show the amount of books (and members) LOST through imposing fines. Damage has been done by not looking at simple, traditional library data, and ensuring that policies are data-informed.

It’s data that tells stories. It can inform if opening hours are working, or need adjusting. About what item types are borrowed in different areas of the county, or city. About streets where membership is down (or up). About how footfall and usage is affected by local events and shopping patterns, or even weather. Detailed data needs to be monitored, analysed, watched for anomalies and fluctuations which can then be investigated.