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Library Improvement through Data Analytics

Jul 2016 | 192pp

Paperback
9781783301614
Price: £54.95
CILIP members price: £43.96


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Library Improvement through Data Analytics

Lesley S J Farmer and Alan M Safer

This book shows how to act on and make sense of data in libraries. Using a range of techniques, tools and methodologies it explains how data can be used to help inform decision making at every level.

Sound data analytics is the foundation for making an evidence-based case for libraries, in addition to guiding myriad organizational decisions, from optimizing operations for efficiency to responding to community needs. Designed to be useful for beginners as well as those with a background in data, this book introduces the basics of a six point framework that can be applied to a variety of library settings for effective system based, data-driven management. 

Library Improvement Through Data Analytics includes:

  • the basics of statistical concepts
  • recommended data sources for various library functions and processes, and guidance for using census, university, or government data in analysis
  • techniques for cleaning data
  • matching data to appropriate data analysis methods
  • how to make descriptive statistics more powerful by spotlighting relationships
  • 14 practical case studies, covering topics such as access and retrieval, digitization, e-book collection development, staffing, facilities, and instruction.

This book’s clear, concise coverage will enable librarians, archivists, curators and technologists of every experience level to gain a better understanding of statistics in order to facilitate library improvement.

PART I: OVERVIEW

1. Introduction
2. Planning with Six Sigma

PART II: SIX SIGMA STEPS

3. Defining the Project
4. Measure the Current Situation
5. Analyze Existing Processes
6. Improve or Introduce the Process
7. Control the Process

PART III:  A STATISTICS PRIMER

8.  Cleaning Data
9. Getting Started with Statistics
10.  Matching Data Analytic Methods to Data
11. Statistical and Survey Software for Libraries

PART IV: CASE STUDIES

12. Access and Retrieval: Case Study
13. Benchmarking Library Standards: Case Study
14. Data Sets: Case Study
15. Digitization: Case Study
16. Ebook Collection Development: Case Study
17. Facilities: Case Study
18. Information Audit: Case Study
19. Instruction: Case Study
20. Knowledge Management: Case Study
21. Lending Devices: Case Study
22. Marketing Virtual Reference Services: Case Study
23. Optimizing Online Use: Case Study
24. Reference Staffing Patterns: Case Study
25. True Costs of Acquisitions: Case Study with Implications for Selection Practice

“Data-driven decision-making is essential for effective library management in the 21st century. But the tools to develop that analysis are not readily available for library administrators. Library Improvement through Data Analytics is a practical guide with clear and detailed steps for applying Six Sigma, an effective model for targeted library improvement analysis. Applying this technique to library processes and programs can improve performance and productivity, reduce expenses and increase satisfaction of users and staff. The compelling case studies will support library administrators in deploying these important tools to make the case successfully for their libraries.” 
- Susan Hildreth, Professor of Practice, UW ISchool

“Farmer and Safer make the case for deliberate, rigorous use of data to evaluate library programs and procedures … While smaller libraries may not be able to use the Six Sigma Steps and Pearson correlations in their entirety, they can gain useful insight from this book on how to gather, clean, and analyze data in order to improve their processes, services, or facilities.” 
- VOYA 

Dr. Lesley S. J. Farmer, professor at California State University, Long Beach (CSULB), coordinates the Librarianship Program. She earned her MS in library science at the University of North Carolina at Chapel Hill, and received her doctorate in adult education from Temple University. She has worked as a librarian in K–12 school settings, as well as in public, special, and academic libraries. She chairs the International Federation of Library Associations and Institutions’ School Libraries Section and is a Fulbright Scholar. Dr. Farmer received the American Library Association Beta Phi Mu Award for distinguished service and library education, as well as several other professional association awards, and national and international grants. Farmer’s research interests include information literacy, assessment, and educational technology, especially digital citizenship. A frequent presenter and writer for the profession, Farmer has published over 30 professional books and more than a 100 professional book chapters and articles.

Dr. Alan M. Safer is a professor at California State University, Long Beach (CSULB) in the Department of Mathematics and Statistics. He received his PhD in Statistics from the University of Wyoming and his MS in Marketing Research from Southern Illinois University Edwardsville. He first came to CSULB as an assistant professor in 2000 and has been a full professor since 2010. Early in his career at the university, he created a MS degree in Applied Statistics and later a professional accelerated MS degree in Applied Statistics for industry students from companies such as Boeing, Raytheon, and Northrop Grumman. He served as the graduate advisor for 7 years, and in 2009 was awarded university advisor of the year at CSULB. Dr. Safer’s research has been very interdisciplinary; he has over 25 publications in diverse statistical areas such as finance, library science, marketing, health science, linguistics, and forensics. His primary statistical research focus is data mining and quality control. In 2012, he was appointed coordinator of a national conference on quality control sponsored by the American Statistical Association. In the last few years, Dr. Safer helped create the Orange County/Long Beach chapter of the American Statistical Association and served as its vice president

PART I Overview

1. Introduction

The Introduction explains the basis for data analytics, highlighting quantitative aspects, and states how it can benefit library management. A library case study concretizes the use of data analytics to show library value (Soria, Fransen, & Nackerud, 2014). This book is intended to serve as a practical introduction to data analytics as a means for library improvement. With this book in hand, librarians can venture into the world of data, and leverage its use for informed, effective library improvement that positively impacts its stakeholders.

2. Planning with Six Sigma

The power of data analysis lies in its leverage to take effective action. As such, data analysis comprises one aspect of library planning and management. Deciding which data to collect, how to collect data, and what to decide as a result of the data analysis all impact the success of data analysis. Chapter 2 focuses on one of the most effective data-driven models for targeted library improvement: Six Sigma. It explores the critical conditions for its appropriateness and success, and ways to adapt some of its practices that fit local circumstances.

PART II Six Sigma Steps

3. Defining the Project

Substantial pre-planning and negotiation occurs even before deciding on the specific goal and defining the project. What is worth improving? That is a major question that library staff should ask when they monitor and review how they use their resources or when developing a strategic plan.  Chapter 3 outlines how Librarians should examine their internal practices as well as their external environment, including competing organizations. The chapter sets out internal data points to consider, how to choose the relevant data and how to make important decisions by identifying factors that are essential for success.

4. Measure the Current Situation

Chapter 4 looks at how a project team develops metrics and specifies project goals through data analytics. The chapter focuses on how to match the objective with the available data to problem solve and identify effective interventions and how to select the right methods and resources to collect appropriate data.

5. Analyze Existing Processes

Chapter 5 involves analyzing collected data to identify patterns and reveal the root causes of problems. The analysis leads to making recommendations about ways to solve the problem and improve practice. Several methods may be used to clarify the data.

6. Improve or Introduce the Process

Chapter 6 concerns the application of interventions or improvement processes. Once the project team has identified the probable root causes, and hypothesized ways to correct them, the team can reexamine the targeted processes or practices and figure out how to introduce the specific interventions or changes. The goal is to have an improved process that is stable and predictable, and that meets users’ needs. The baseline data helps the team measure the impact of the changes on the critical output; usually, the same type of statistical analysis can be applied.

7. Control the Process

Chapter 7 looks at how to control the performance of newly implemented processes. The team seeks to maintain consistent high-quality performance. To this end, the improved practice is documented and affected staff are trained to implement that process competently and consistently. Performance needs to be closely monitored at this time to identify and rectify deviations. The team also creates a process for updating procedures and anticipating future improvements. They also review efforts, and share lessons learned to recommend further action and opportunities.

PART III A Statistics Primer

8. Cleaning Data

There is a common saying in analytics about data: garbage in, garbage out, which is also referred to as GIGO. In essence, if the data are not cleaned, then no matter how fancy or complex the statistical analysis, the results will be garbage. Chapter 8 considers the many components to cleaning data and the importance of spending an appropriate amount of time on the data before doing the full analysis and then interpreting of results.

9. Getting Started with Statistics

Once the project team has a clean raw data set the question arises about which statistical techniques are possible, and what kind of resulting interpretation can be done. Chapter 9 considers what kind of statistic is appropriate, and what kind of analysis can be applied? Ideally, the research questions drive the kind of data to collect and how to analyze that data.

10. Matching Data Analytic Methods to Data

Chapter 10 explains how to match a research goal with an appropriate statistical technique and characteristics of the variables.

11. Statistical and Survey Software for Libraries

Chapter 11 reviews the available software packages that may be useful to help achieve research goals. Some of the common choices are Minitab, SPSS, R, SAS, and Tableau.

PART IV Case Studies

12. Access and Retrieval: Case Study

Chapter 12 uses a case study to demonstrate how the organization of resources for optimum access and retrieval is a core function of libraries.

13. Benchmarking Library Standards: Case Study

Many state library systems and organizations have developed standards for libraries, but these are seldom date-driven. Because standards can be used to benchmark library effectiveness, providing ones that are data-based legitimizes such benchmarking efforts. Chapter 13 illustrates this in a case study of the California State Department of Education and the California School Library Association (CSLA).

14. Data Sets: Case Study

Chapter 14 looks at storage and access to data sets and the role of librarians in establishing and maintaining data repositories.

15. Digitization: Case Study

Chapter 15 details the process of digitisation of resource materials in libraries.

16. Ebook Collection Development: Case Study

Chapter 16 explores the cost-benefit of developing ebook collections in libraries, offering a balanced scorecard and decision matrix as a guide.

17. Facilities: Case Study

Chapter 17 considers the physical space of the library and the associated costs of property maintenance: renovation, taxes, utility bills and so on in order to answer the question of how that space can be most effectively utilized? It demonstrates the how data helps to clarify these issues.

18. Information Audit: Case Study

An information audit is a “process that examines how well the organization’s information needs and deliverables connect to the organization’s mission, goals and objectives” (St. Clair, 1997, p. 5). Chapter 18 reviews Henczel’s (2001) seven-stage information audit.

19. Instruction: Case Study

Chapter 19 provides an overview of the need users to develop their own information literacy skills to enable them to evaluate the resources available to them, particularly online information.

20. Knowledge Management: Case Study

Chapter 20 discusses the importance of knowledge management in libraries and the role of librarians in ensuring the proper capture and organisation of knowledge such as through in-house repositories.

21. Lending Devices: Case Study

Chapter 21 looks at a case study capturing data on lending patterns to enable librarians to deliver better quality, more consistent service.

22. Marketing Virtual Reference Services: Case Study

To optimize usage of library resources and services, librarians usually need to market their programs. Chapter 22 provides a case study on how virtual reference services can exemplify marketing efforts.

23. Optimizing Online Use: Case Study

Chapter 23 discusses how collecting data about library clientele’s online use can inform decisions about collection development and its use, instruction, and other potential value-added services such as supporting faculty research.

24. Reference Staffing Patterns: Case Study

Chapter 24 looks at the ways that data analysis can facilitate cost-effective allocation of human resources demands such as by measuring performance and allocation of resources and personnel. Data analysis can aid in decision making when it comes to staffing patterns.

25. True Costs of Acquisitions: Case Study with Implications for Selection Practice

Chapter 25 investigates the cost of acquiring books/resources using the time-driven activity-based costing model (Kaplan & Anderson, 2004).

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