Librarians in STEM settings work with research professionals who often conduct their own online searches. Demonstrating the value of the library means going beyond searcher expertise to the impact that professionals with an information science background can bring to advanced technology projects.
Sometimes, librarians bring insight by mining the library’s operational data—identifying patterns of which analytical tools and data sets were acquired for which project and for how long, for example. The librarian at a pharmaceutical company described an impactful use for this kind of data. “We created a task force that looked at the information patterns around the lifecycle of a drug. Who is the first group in the pipeline who will need research? When do we need to start a specific subscription? What can we expect to be spending a year from now to support this product? I can use our metrics to help product groups plan for additional investments in information resources, based on what we’ve seen with prior products.”
Information professionals look for ways to demonstrate to R&D user groups the value of incorporating the library when negotiating a subscription to an information resource. One librarian I spoke with was in a meeting recently with a team that was evaluating an open access database and AI platform for a project. The team was ready to sign a contract but the librarian kept asking the kinds of questions that information professionals are accustomed to raising when evaluating an information tool, regarding licensing issues, customer support and integration of internal resources. As a result of her probing questions, it became clear that the product being considered was not well suited for the project—and that team gained a new appreciation of the expertise that a librarian can bring to a negotiation.
The value of us as super-users of information is that we can help teach others how to use tools in more strategic ways
Just as librarians have had to explain to users why value-added information services have not been replaced by Google, recent developments in AI have led to unrealistic expectations of extracting insights from data. A librarian in the energy industry described a recurring conversation at her company. “Everybody wants to find the one tool that does everything—they want to just type in a question and have it spit out the solution. We have to explain to our users what they can expect from a machine learning tool. That’s why we value the analytical tools that some of our information providers offer; in fact, a few vendors include licensed data from other providers in their analytics. While these tools are usually not granular enough for all our needs, we find them helpful in giving a high-level view of what we might be able to accomplish with a particular data set, and to help users understand what they can and can’t expect from an AI tool. In fact, I think the value of us as super-users of information is that we can help teach others how to use tools in more strategic ways.”
