Analytics for Academic Libraries

Autorentext

Jennifer Ye Moon-Chung is the Assessment Coordinator at the University of Pittsburgh Library System, USA. An expert at utilizing organizational data, her role involves the systematic collection of data, data analysis, and the creation of analytics reports. She is committed to fostering informed decision-making and to enhancing the value of the library in its collections, services, and spaces. Previously, Jennifer was the digital projects and analytics manager at Duquesne University. She holds a master's of library and information science from the University of Pittsburgh and a master's of education from Yonsei University, South Korea.

Klappentext

Drawing on the latest approaches to behavioral and predictive analytics, this guide offers academic librarians pathways to implementing successful analytics programs and to navigating the complex landscape of analytics technology and platforms.

Covering the expectations of the field and modern data-collecting tools and techniques, readers will learn how to leverage their findings to make data-informed decisions in their library planning. The book also highlights what is unique to librarianship in the discipline of analytics by focusing on the ethics of the library profession, including issues related to data privacy and service standards and issues related to collection management and outreach efforts.

This book is an indispensable guide to all areas of academic librarianship. Analytics provide a shared area where data points from other functional areas can be viewed and compared, driving creative collaboration, identification of library-wide trends, and development of future initiatives.

Inhalt

1. Introduction
a. Introduction
b. Scope
c. Definition of Terms (Library analytics, Dashboards)

2. Transformation of libraries and key metrics
a. Changes in Academic Libraries
b. Key Metrics
c. Case Studies

3. Building a Strong Foundation for Analytics
a. Building a Culture of Using Data to Make Decisions
b. Share Data across all Library Units
c. Select Storage
d. Case Studies

4. Planning your Analytics Project
a. Identifying Candidates
b. Data Collection Planning: Purpose, Utilization, Management, and Maintenance
c. Ethical and Legal Responsibilities
d. Case studies

5. Data Communication
a. Defining your Communication Goals
b. Internal Communication Goals
c. Mid-level Communication Goals
d. Promotional Communication Goals
e. Case studies

6. Choosing the Right Reporting Tools
a. Evaluating Reporting Software
b. Understand the Pros and Cons
c. Best Tools for Your Needs
d. Case Studies

7. Building Your Analytics Pipeline: From Data tools, and people
a. Gathering and Selecting Data
b. Cleaning and Preparing Data
c. Feeding Data into Your Pipeline
d. Designing and Building Dashboards
e. Maintaining and Improving Your Pipeline
f. Case Studies

8. Interpretation of Data
a. The Importance of Accurate Data Interpretation
b. Understanding Data Bias and Misuse (Tips to Avoid Data Bias and Misuse)
c. Behavioral Analytics
d. Predicative Analytics
e. Case Studies

9. Thinking about the future of library analytics and conclusion
a. Documentation
b. Data Quality and Integrity
c. Learning Community
d. Evaluation
e. Case Studies

10. Thinking about the future of Analytics
a. Data Collection
b. Machine Learning
c. Optimizing Data Models
d. Concluding Remarks

Appendix
a. Tools Used in Analytics by Academic Libraries
b. Resources for Benchmarking and Selecting Peer Libraries

Bibliography