A non-programmers guide to enhancing and making sense of EZ Proxy logs

Purpose Libraries throughout the world use OCLC’s EZproxy software to manage access to e-resources. When cleaned, processed, visualized and enhanced, these logs paint a valuable picture of a library’s impact on researcher’s lives. The purpose of this paper is to share techniques and procedures for e...

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Bibliographic Details
Published in:Performance measurement and metrics Vol. 20; no. 3; pp. 186 - 195
Main Author: Murphy, Sarah Anne
Format: Journal Article
Language:English
Published: Bradford Emerald Publishing Limited 03.12.2019
Emerald Group Publishing Limited
Subjects:
Academic libraries
Automation
Best practice
Careers
Cleaning
College faculty
Confidentiality
Data
Data analysis
Datasets
Graduate students
Internet resources
Librarians
Libraries
Library resources
Library staff
Researchers
Safeguards
Schedules
Software
URLs
Visualization
United States > US
Ohio
EZproxy
Tableau Prep
Tableau
Library assessment
Data visualization
Data cleaning
ISSN:1467-8047, 1758-6925
Online Access:Get full text
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Summary:Purpose Libraries throughout the world use OCLC’s EZproxy software to manage access to e-resources. When cleaned, processed, visualized and enhanced, these logs paint a valuable picture of a library’s impact on researcher’s lives. The purpose of this paper is to share techniques and procedures for enhancing and de-identifying EZproxy logs using Tableau, a data analytics and visualization software, and Tableau Prep, a tool used for cleaning, combining and shaping data for analysis. Design/methodology/approach In February 2018, The Ohio State University Libraries established an automated daily process to extract and clean EZproxy log files. The assessment librarian created a series of procedures in Tableau and Tableau Prep to union, parse and enhance these files by adding information such as user major, user status (faculty, graduate or undergraduate) and the title of the requested resource. She last stripped the data set of identifiers and applied best practices for maintaining confidentiality to visualize the data. Findings The data set is currently 1.5m rows and growing. The visualizations may be filtered by date, user status and user department/major where applicable. Safeguards are in place to limit data presentation when filters might reveal a user’s identity. Originality/value Tableau used in concert with Tableau Prep allows an assessment librarian to clean and combine data from various sources. Once procedures for cleaning and combining data sources are established, the data driving visualizations can be set to refresh on a set schedule. This expedites the ability of librarians to derive actionable insights from EZproxy data and to share the library’s positive impact on researcher’s lives.
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ISSN:1467-8047
1758-6925
DOI:10.1108/PMM-08-2019-0034