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LIBRARY ANALYTICS: AN OVERVIEWPAARL NATIONAL SUMMER CONFERENCE・20 APR 2016
REINA REYES, PH.D.
DATA SCIENCE
INFORMATION SCIENCE
DATA ANALYTICS
BIG DATA!
LIBRARY SCIENCE
LIBRARY ANALYTICS
LIBRARY ANALYTICS
• the discovery and communication of meaningful patterns in data
• should lead to “actionable insights”— information that leads directly to an action or actions
• often communicated through data visualizations
Reference: Ben Showers (Ed.), “Library Analytics and Metrics: Using data to drive decisions and services”, Facet Publishing (2015)
•catalogue searches
•item check-outs
•log-ins to online resources & services
•swipes through the entrance gates
•space usage
•student satisfaction
•external visitors to the library
Reference: Ben Showers (Ed.), “Library Analytics and Metrics: Using data to drive decisions and services”, Facet Publishing (2015)
WHAT KIND OF DATA?
•collections development & management
•impact assessment
•learning analytics
•improving services & meeting user requirements
•recommendation services
WHAT TO USE ANALYTICS FOR?
Reference: Ben Showers (Ed.), “Library Analytics and Metrics: Using data to drive decisions and services”, Facet Publishing (2015)
ANALYTICS FRAMEWORKTHE BIG PICTURE
ANALYTICS FRAMEWORKTHE BIG PICTURE
USE CASES
Harvard Library Explorer (http://librarylab.law.harvard.edu/toolkit/)
Harvard Library Explorer (http://librarylab.law.harvard.edu/toolkit/)
OHIOLINK–OCLC COLLECTION AND CIRCULATION ANALYSIS PROJECT 2011
• Goals:
• Understand the usage and collecting patterns within OhioLINK libraries
• Enable creation of collecting rubrics that will:
• help reduce unnecessary duplication
• allocate resource dollars more effectively, and
• increase diversity of collections across the state
(http://www.oclc.org/research/publications/library/2011/2011-06r.html)
OHIOLINK–OCLC COLLECTION AND CIRCULATION ANALYSIS PROJECT 2011
• the largest and most comprehensive study of academic library circulation
• 600,000 students, faculty, and staff at 90 institutions
• 16 public/research universities including:
• 5 ARLs
• 23 community/technical colleges
• 50 private colleges and
• the State Library of Ohio
• 50 million books and other library materials
(http://www.oclc.org/research/publications/library/2011/2011-06r.html)
OHIOLINK–OCLC COLLECTION AND CIRCULATION ANALYSIS PROJECT 2011
(http://www.oclc.org/research/publications/library/2011/2011-06r.html)
OHIOLINK–OCLC COLLECTION AND CIRCULATION ANALYSIS PROJECT 2011
Arts%&%Recreation,%8.0%
Business%&%Economics,%8.2%
History%&%Geography,%13.7%
Language%&%Literature,%21.2%
Science%&%Technology,%15.1%
Social%Science,%22.1%
Medicine,%4.2% Law,%7.4%
SUBJECT DISTRIBUTION OF ITEMS
OHIOLINK–OCLC COLLECTION AND CIRCULATION ANALYSIS PROJECT 2011
DUPLICATION RATES BY SUBJECT
0
3
6
9
Arts)&)Recreation
Business)&)Economics
History)&)Geography
Language)&)Literature
Science)&)Technology
Social)Science
Medicine Law
OHIOLINK–OCLC COLLECTION AND CIRCULATION ANALYSIS PROJECT 2011
DUPLICATION RATES BY PUBLICATION DATE
Publication+Date
Average+No.+of+Copies
4.5
TOOLSET SKILL SET MINDSET
WHAT? WHO?WHY? HOW?
• CURIOSITY: Ask questions! • Be wary of the “Streetlight Effect”:
• resist tendency to look for answers where it is easiest to find information and data (akin to looking for keys only under the street lamp)
• focus on asking the right questions & finding new ways to expose and analyse the data that can lead to the answers (& to help improve and refine the questions themselves)
ANALYTICS MINDSET
Reference: Ben Showers (Ed.), “Library Analytics and Metrics: Using data to drive decisions and services”, Facet Publishing (2015)
I AM A DATA-SAVVY LIBRARIAN!
• Data transformation process • Data retrieval/queries • Basic Statistics • Effective visualization design
ANALYTICS SKILL SET
I AM A DATA-SAVVY LIBRARIAN!
• Data transformation process • Data retrieval/queries • Basic Statistics • Effective visualization design
ANALYTICS SKILL SET
WHO CAN I WORK WITH?
WHAT TOOLS CAN I USE?
• Microsoft Excel (all-around) • Tableau, visualisingadvocacy.org (viz) • OpenRefine (for data cleansing) • Unix shell, git (programming/hacking) • SQL, noSQL, etc. (database queries) • SPSS, SAS, Python, R (all-around+)
ANALYTICS TOOLSET
WHAT TOOLS CAN I USE?
• Microsoft Excel (all-around) • Tableau, visualisingadvocacy.org (viz) • OpenRefine (for data cleansing) • Unix shell, git (programming/hacking) • SQL, noSQL, etc. (database queries) • SPSS, SAS, Python, R (all-around+)
ANALYTICS TOOLSET
HOW DO I START?
BACK TO THE ANALYTICS FRAMEWORKBEGIN WHERE YOU ARE
