Local decision support based on collective intelligence

Local decision support based on collective intelligence

Is there anything more thrilling than finding unexpected, useful answers during an information treasure hunt? While working on a shared print program years ago, I got the chance to use a variety of data sets and programming skills to uncover hidden strengths within library collections. It was important work. That knowledge helped showcase how each library contributed and revealed new ways to leverage collective materials.

The ability to use analytics tools to investigate relationships between the collections of many libraries and come away with actionable, local insights was exhilarating. If that sounds like your kind of treasure hunt, I have good news for you.

How big is your map?

After those early projects, I was happy to contribute to and use decision support tools like GreenGlass and WorldShare Collection Evaluation. Being able to compare massive amounts of metadata through WorldCat has enabled many libraries to accurately evaluate collections for deselection, preservation, and shared print. WorldCat also provides our researchers with data to study trends and evaluate assumptions about the global library collective collection. These tools provide information that supports and enables useful collaboration—the more detailed and richer the map you explore on behalf of your library, the more interesting and useful your findings will be.

Shared print initiatives provide a good example of where collection analysis is used to support collective stewardship. Efforts like the CDL, CRL & HathiTrust (CCH) Shared Print Collaboration extend the notion of shared print to encompass the full collections life cycle. Other cross-library collaborations can be found around discipline-specific topics, like language and area studies. These efforts have yielded many positive outcomes. But they are often based on specific goals across a limited number of libraries.

Today, we’re able to do more than describe and characterize collective collections. New collaborative options support the management of both local and shared collections, leveraging the network intelligence of libraries around the world.

Telescope, compass, and shovel—all in one

If your library had access to the right data sets and comparisons, what could you learn that would provide hidden value? What if you had a set of tools that explored peer libraries, academic programs you’d like to emulate, collaboration opportunities, and institutional data?

Our recently announced Choreo Insights product gives you those capabilities. And it reminds me of that feeling of exploration I had during my early days of shared print—but with more flexibility and ease-of-use. Without any programming knowledge, you can compare local collections to any library, or set of libraries, in WorldCat. For example, we’ve linked library data with broader educational program data. This connects collection decisions to academic programs, grants, and educational outcomes—centering your library within these discussions.

You can also use place-based data elements—such as location of publication, language, and geographic subject descriptors—to identify under-represented voices within larger collection profiles. Whether it’s materials that need to be preserved or ones that should be acquired, these data points can help uncover materials to support local diversity, equity, and inclusion (DEI) initiatives.

Newer, bigger, mutual goals

These are the kinds of queries that early Choreo Insights users are investigating as part of their decision-support processes:

  • Who should we be collaborating with as we try to build a best-in-class collection on a given topic?
  • Who are my natural peers, both in terms of collections and curricula?
  • Which libraries have unexpected strengths that we should be aware of?
  • How can I identify and highlight my library’s unexpected areas of strength?

Authoritative title lists and core collection requirements are not new to us. But library leaders I speak with want to apply the same rigor to new institutional goals, specific audiences, and educational needs. And since the criteria for those materials aren’t always in alignment with traditional data sets or collection policies, we need both better analytics tools and bigger, shared metadata maps.

The possibilities for this tool are extraordinary, and I’m excited to see how libraries leverage it to gain insights and make meaningful changes. Our hope is to continue to add more sophisticated functionality to help libraries provide better access, justify budgets, and show how collections affect institutional outcomes.

I hope you share my excitement for where this is heading. Decisions shouldn’t (and don’t have to) be made in isolation. We can work together to share meaningful local data in ways that help all libraries make a bigger impact locally.

Watch a demonstration of Choreo Insights.