Building a Data-Driven Academic Library

Academic libraries already generate a huge amount of data. Usage statistics, database searches, downloads, circulation, interlibrary loans, website activity, and user feedback can all reveal how researchers and students interact with library resources.

The challenge is turning that information into useful decisions.

A data-driven academic library uses evidence to understand demand, manage resources, improve services, and demonstrate value to university leadership.

Start With the Right Questions

Data becomes useful when it answers a specific question.

Instead of collecting every available metric, librarians can begin with decisions they need to make:

• Which resources should we renew?
• Which databases are underused?
• What are researchers searching for?
• Where are collection gaps?
• Which services need more investment?
• How can we demonstrate the value of library spending?

Starting with the decision makes it easier to identify the data that actually matters.

Look Beyond Usage Statistics

Downloads and database sessions provide useful information, but they only show part of the picture.

Search data can reveal what users are trying to find. Zero-result searches can point to discovery problems or collection gaps. Interlibrary loan requests can show demand for resources the library does not currently provide.

For example, a database with low usage may appear to be a candidate for cancellation. Search activity could reveal strong demand for its subject area. Interlibrary loan requests could show that researchers are looking for content the database provides.

The numbers need context.

Combining different sources of data gives librarians a clearer picture of user needs and collection performance.

Use Data to Strengthen Collection Decisions

Collection development involves professional judgment. Data gives librarians stronger evidence to support that judgment.

Useful measures include:

• Full-text downloads
• Database searches
• Cost per use
• Circulation
• Interlibrary loan requests
• Subject-level demand
• Resource access patterns

These measures can support decisions about acquisitions, renewals, cancellations, and new subscriptions.

Cost per use can also help libraries explain spending decisions. A highly specialized resource may have lower usage because it serves a small research community. A resource supporting thousands of students may have very different value.

The number alone does not make the decision.

Measure Services as Well as Resources

A data-driven library should also examine how people use its services.

Reference questions, research consultations, workshops, website visits, information literacy sessions, and online interactions can show where users need support.

Physical attendance is another example. A decline in visits does not necessarily mean declining library use. Researchers may be accessing databases remotely, downloading articles, using digital collections, and attending online consultations.

Measuring only physical visits would miss much of this activity.

Connect Library Data to University Priorities

Library data becomes more powerful when it connects with wider institutional goals.

University leadership may care about research output, student success, financial efficiency, digital transformation, or growth in particular academic disciplines.

A library can use its data to connect resources and services to these priorities.

Instead of reporting that a database generated 50,000 downloads, librarians can show which departments use it, which research areas depend on it, and how its usage relates to the university’s broader needs.

This creates a stronger case for library investment.

Build Better Data Practices

Having more data does not automatically produce better decisions.

Libraries often work with information from multiple systems, each using different formats and definitions. Inconsistent or incomplete data can make comparisons difficult.

A practical data strategy should focus on:

1. Consistent metrics – Define how usage, engagement, and cost are measured.
2. Reliable data sources – Know where each metric comes from.
3. Regular reporting – Track changes over time.
4. Data quality – Check for missing, duplicate, or inconsistent information.
5. Simple reporting – Make findings understandable to staff and university leaders.

Good data practices make analysis more useful and reduce the time spent preparing reports.

Develop Data Skills

A data-driven library does not need every librarian to become a data analyst.

Basic skills can make a significant difference. Librarians can learn to work with spreadsheets, interpret usage statistics, identify trends, create simple visualizations, and communicate findings clearly.

Data literacy also means understanding the limits of the numbers.

Ten thousand downloads may sound impressive, but the figure does not explain who used the resource, why they used it, or whether it supported successful research.

Librarians still provide the professional context that turns a metric into a meaningful insight.

Protect User Privacy

Data-driven decision-making also requires responsible data practices.

Libraries may have access to information about searches, authentication, resource use, and research activity. Protecting user privacy should remain central to how this information is collected and analyzed.

Libraries should establish clear policies around what data is collected, why it is needed, who can access it, and how long it is retained.

Better data should lead to better services without compromising user trust.

Turn Data Into Action

A dashboard is not the outcome.

The value of data comes from what happens after the analysis.

A useful process is:

Question → Data → Analysis → Decision → Action → Measurement

A library might identify growing demand for a research subject, examine searches and interlibrary loan requests, investigate available resources, make an acquisition decision, and then measure the results.

This creates a continuous feedback loop between user needs, library spending, and service improvement.

Building a Culture of Evidence

Becoming data-driven is not about collecting the most information or investing in the most sophisticated analytics tools.

It is about making evidence part of everyday library decision-making.

Academic libraries already have valuable information about what their users search for, access, borrow, request, and need. Bringing that information together can help librarians allocate resources more strategically, identify gaps, strengthen budget decisions, and demonstrate their contribution to the university.

Data shows what is happening. Librarians provide the context needed to decide what to do next