Additional Resources & Further Reading
This section provides additional references, definitions, and support resources for librarians who want to deepen their understanding of data use in public libraries. It includes a glossary of common data terms, research relevant to Wisconsin libraries, recommended readings, and contacts for further assistance.
Understanding basic data terminology can help build confidence when working with datasets, reports, and dashboards. The following definitions focus on terms commonly used in public library data and reporting.
| Term | Definition |
| Aggregated Data | |
| Anonymization | Removing identifying information so data can be safely analyzed or shared (for example, reporting total checkouts without patron names). |
| Benchmarking | Comparing your library’s data to similar libraries, regional averages, or national standards to understand performance and identify areas for improvement. |
| Cohort | A group of items or people that share a common characteristic and are analyzed together, such as libraries of similar population size or patrons in the same age group. |
| Dashboard | A visual display of data, often charts or graphs, that summarizes key metrics in one place. |
| Data | Information collected by the library, such as circulation numbers, program attendance, or survey responses |
| Data Analysis | Examining data to identify patterns, trends, or insights. |
| Data Ecosystem | |
| Data Governance | Policies and procedures that guide how an organization collects, stores, uses, and protects data. |
| Data Lifecycle | The stages data goes through: collection, storage, use, sharing, and eventual deletion. |
| Data Literacy | |
| Data Privacy | Protecting personal information about patrons so it is not shared or misused. |
| Data Quality | The accuracy, completeness, and reliability of data. For example, whether patron records are up to date. |
| Data Security | Tools and practices used to protect data from unauthorized access, such as secure systems and passwords. |
| Datapoint | |
| Dataset | A collection of related data, such as a spreadsheet of all checkouts from the past year. |
| Field | |
| Filter | A tool used in spreadsheets or dashboards to display only certain data, such as a specific year, library branch, or program type. |
| Inputs | The resources a library invests in services, such as funding, staff time, collections, and facilities. |
| Key Performance Indicator (KPI) | A specific metric used to evaluate progress toward a goal. In libraries, examples might include circulation per capita, program attendance, or number of active cardholders. |
| Metadata | |
| Metrics | Specific measurements used to track performance, such as monthly circulation totals or new library card registrations. |
| Normalization | Adjusting data to allow fair comparisons between different groups, often by using measures such as per-capita rates. |
| Outputs | The direct results of library services, such as circulation numbers, program attendance, or database usage. |
| Peer Libraries | Libraries with similar characteristics—such as population served, budget, or size—used for comparison and benchmarking. |
| Per Capita | The average amount of something per person in a population. It is calculated by dividing a total by the number of people and allows for meaningful comparisons across different population sizes. |
| Personally Identifiable Information (PII) | Information that can identify a specific person, such as name, address, library card number, or email. |
| Raw Data | |
| Table | |
| Trend Analysis | Examining data over time to identify patterns, increases, decreases, or long-term changes. |
In 2022, the Southwest Wisconsin Library System (SWLS), in partnership with WiLS, conducted an LSTA-funded research project to better understand the current data skills and needs of public library staff across Wisconsin.
The project examined the statewide library data ecosystem—the people, organizations, and resources involved in collecting, using, and supporting library data. The goal was to identify how libraries currently use data, what barriers exist, and what training or tools might help librarians use data more effectively.
The resulting Data Landscape Report, published by the Wisconsin Department of Public Instruction (DPI), provides insights into librarian confidence with data and offers recommendations for improving training, access, and support for data use in Wisconsin public libraries.
The following books, articles, and training materials provide additional guidance on data use, visualization, and storytelling.
Books
Storytelling with Data: A Data Visualization Guide for Business Professionals, Cole Nussbaumer Knaflic (Wiley, 2015)
A widely used guide to creating clear and effective data visualizations and communicating insights through charts and graphics.
Training and Articles
- Public Library Association (PLA) Census Data Literacy training series
- Research Institute for Public Libraries (RIPL) webinars on data planning and evaluation
These external resources provide worksheets, templates, and learning materials for improving data literacy skills.
Data Literacy Toolkit
https://data-literacy-toolkit.github.io/index.html
Wisconsin Department of Public Instruction – Bureau of Libraries
Library system staff and data coordinators
Check with your system to see if they have a data expert on staff!