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CARL Data Visualization Toolkit

Welcome to the Canadian Association of Research Libraries portal for resources and information on creating dashboards and data visualizations. The purpose of this resource is to provide guidance for academic libraries seeking to incorporate dashboards and other data visualization tools into their assessment practice.

Dashboards and Data Visualization

Basic types of dashboard:

Operational Dashboard – A regularly updated answer to a question or line of inquiry that frequently monitors operational concerns in response to events or on an ad-hoc basis.
Strategic/Executive Dashboard – A high level view of a question or line of inquiry that is usually answered in a routine, specific way and usually presents Key Performance Indicators in a minimally interactive way.
Analytical Dashboard – A highly interactive view that provides a variety of investigative approaches to a specific central topic with a few corollary contextual views.

From Unilytics

Dashboard and Data Visualization Definitions

“A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance“ (Stephen Few).

“…a dashboard is a user interface that, somewhat resembling an automobile’s dashboard, organizes and presents information in a way that is easy to read” (TechGadget).

“A business dashboard is a screen that consolidates critical performance metrics all in one place, making it easy for users to stay constantly updated on the information most important to their business.Dashboards can be designed to suit a variety of needs, and will therefore take on a variety of forms, from business intelligence dashboards (BI dashboards) to executive dashboards/enterprise dashboards and key performance indicator dashboards (KPI dashboards)” (DashBoard Insights).

“Roughly speaking, data visualization is drawing a picture with your data instead of leaving it in a spreadsheet or table” (Datapine Blog).

“Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns” (SAS).

“An effective dashboard is one that offers a consolidated, visual display of an organization’s most critical data. By integrating data from multiple sources, a dashboard allows the user to achieve a big picture understanding of key performance indicators at a glance. It also gives them to power to easily drill down for a more thorough examination of the data” (Yurbi).

Notes:

  • A scorecard can be incorporated into a dashboard, but it isn’t a dashboard on its own
  • A dashboard is similar to an infographic; however an infographic is geared more towards communicating information to the masses, while a dashboard communicates key performance indicators (KPIs) to a specific target audience. Both can be textual, visual, or a combination.

Basic Rationale for Creating Dashboards and Data Visualization

Gartner RAS Core Research Note
“The main purpose of a dashboard is to enable managers to quickly and routinely comprehend how they are performing against their KPIs, not to provide an environment for complex data analysis” (James Richardson).

Wikipedia – Dashboard
Some of the benefits as listed include: Visual presentation of performance measures; Ability to identify and correct negative trends; Measure efficiencies/inefficiencies; Ability to generate detailed reports showing new trends; Ability to make more informed decisions based on collected business intelligence; Align strategies and organizational goals; Save time compared to running multiple reports; Gain total visibility of all systems instantly; Quick identification of data outliers and correlations (Wikipedia).

Stephen Few
Data visualization makes “stories visible and bring[s] them to life” (Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics Press).

Yellowfin Dashboard Best Practice Webinar
Dashboards allow for faster insight (all metrics are in one place), better decisions (you can see a summarized view of the world), strategic focus (they get everyone on the same page), and enable monitoring business success (tracks progress over time) (Shawn Deegan & Brett Churchill).

Paris Technologies
When data is rendered visually, we can easily spot trends and patterns that could have gone undetected in just a statistical form. It makes total sense that data visualization will give us more meaning, as 90% of information transmitted in the human brain is visual. We also process visual imagery 60,000 times faster than text, which the brain finds easier to remember (Melinda Santos).

Working Group

This guide was created by the members of the Data Visualization Project Group of the CARL Assessment Committee Continuing Education Working Group.

Dashboards can be a powerful, impressive, and effective way to collect, convey, and publish relevant information about your academic library. They can help an academic library to establish its importance in the institution’s strategic goals, to demonstrate its value, to tell its story. Depending on your circumstances, they are well worth the investment of staff time and budget.

Factors impacting the rationale and uses of data visualization and dashboards:

  • Why do you need data visualization/dashboards?
  • Who is your audience?
  • What are you trying to convey via data visualization/dashboards?
  • What data do you need to collect?
  • What is the best format to use?
  • How much money/time will be invested?

Factors to Consider

Why do you need data visualization/dashboards?

  • Internal assessment and development
  • External communication (e.g., demonstration of value, show strategic alignment with institutional goals, distribution of statistics and information)

Who is your audience?

  • Internal (e.g., library administration, librarians, other library staff)
  • External / Institutional (e.g., institution administration, faculty and other teaching staff, students)
  • External / Public (e.g., government officials and employees, journalists, members of the public)

What are you trying to convey via data visualization/dashboards?

  • Basic data for public bookkeeping, access to information, or general interest
  • Change over time (e.g, qualitative or quantitative)
  • Promotion of library, collections, services, etc.
  • Value of the library [e.g., contribution to research, contribution to teaching, non-academic value (safe space for students, social gathering place, venue for events, etc.)]

What data could be collected?

  • See the “Data Sources and Collection” tab for more details
  • Quantitative (e.g., gate counts, circulation numbers, contact hours, reference statistics, instruction rates, etc.)
  • Qualitative (e.g., survey results, comments and feedback, etc.)

What is the best format to use?

  • See the “Best Practices” tab for more details
  • Consider your purpose, audience, and intended message
  • Aim for simple, direct, and uncluttered data visualization/dashboards

How much money/time will be invested?

  • See the “Tools and Technology” tab for information on the monetary and skill investments required for different data visualization/dashboards software

Uses of Data Visualization and Dashboards

Public website

Promotional material

  • Posters and brochures
  • Available to all public, or targeted (e.g. to students, to a specific Faculty or department, etc.)
  • Promote library services, programs, collections, etc.

Research and publication

  • Use of images by librarians or library administrators as part of research for conference papers and publications
  • Show basic data, change over time, etc., in the context of a research project involving the relevant data being represented
  • Also promotes librarians as academic researchers

Presentations to institutional administrators

  • Images in electronic (e.g. PowerPoint, .pdf) or printed (e.g. brochure, handout) formats
  • For budget presentations
  • For assessment and unit reviews
  • Demonstrate value, alignment with institutional goals/strategy, present basic information needed for decisions

Internal (locked) website

  • Available to librarians and library staff; may be made available to other institutional staff
  • Assessment, information

Further Reading

Tay, A. (2016, November 18). 5 Reasons why library analytics is on the rise. [Blog post]. Retrieved from http://musingsaboutlibrarianship.blogspot.ca.

  • Good, quick overview.

Cox, B., & Jantti, M. (2012, July 17). Discovering the Impact of Library Use and Student Performance. Educause Review.

  • Using data to demonstrate “a strong correlation between students’ grades and use of information resources” as a way of showing how academic libraries add value.

Oakleaf, M, Whyte, A., Lynema, E., & Brown, M. (2017). Academic libraries & institutional learning analytics: One path to integration. The Journal of Academic Librarianship, 43(5).

  • Using data visualization to effectively demonstrate the library’s added-value and alignment with institutional goals around student learning.

Orlando, T. M., & Sunindyo, W. D. (2017). Designing dashboard visualization for heterogeneous stakeholders (case study: ITB central library). Proceedings of 2017 International Conference on Data and Software Engineering (ICoDSE).

  • How to ensure usability of dashboards by various user groups and audiences.

Murphy, S. A. (2015). How data visualization supports academic library assessment: Three examples from The Ohio State University Libraries using Tableau. College & Research Libraries News, 76(9), 482-486.

  • A basic rationale for using more advanced software to create more sophisticated dashboards (compared to basic data visualization); shows how effective this can be in promotion of collections and services, in developing collection-development priorities, and in allowing librarians to effectively use student survey results.

Factors to Consider when Selecting Visualization Software

Skill level:

  • Do you already have people skilled with a particular software? Learning any new tool will require time and effort.
  • Does the software require coding or programming skills? Do you have those skills? Or access to training?

Your data:

  • How much data do you have?
  • Where is your data stored? Cloud or local storage? Web-based or desktop? Can it be public, or does access need to be restricted?

End product:

  • What kind of user interface do you want? Real-time data? Synchronizations? Stand-alone or integrated within a business intelligence platform?

Features:

  • Does the software have the type of visualization that makes the most sense for your data and the point you are trying to convey?
  • What features do you really need? 2D or 3D? Variable axis scales? Geospatial configurations? …Sometimes simpler is better.
  • Can you easily update your dataset without having to redo the visualization? Can you easily convert from one visualization to another to explore which visualization works best?

Budget:

  • Do you have any money set aside to pay for visualization software?  Or does your IT department have any existing contracts with companies with visualization software?
  • Be careful – there are several ‘free’ versions available, but when deploying a visualization or report, exposes your data to the public.

Types of Visualizations

Two great sites that illustrate and describe types of visualizations, and provide suggestions for appropriate software for each type:

Software Reviews

Baker, P. (2019, March 5). The best data visualization tools of 2018. PC Magazine. Retrieved from PC website.
Dingeldein, T. (2019, October 28). Top 5 free data visualization tools to grow your business [Blog post]. Retrieved from Capterra website.
Marr, B. (2017, July 20). Seven best data visualization tools in 2017. Forbes. Retrieved from Forbes website.
Suda, B., & Hampton-Smith, S. (2019, February 12). 38 best tools for data visualization. Creative Bloq. Retrieved from Creative Bloq website.

Software Options Most Often Used by Libraries

Excel

  • Spreadsheet software that provides calculations, graphing tools, pivot tables & charts, and a macro programming option.
  • Best used for conditional formatting and building great charts to add meaning to basic tables, pivot charts, PowerPoint-friendly.
  • Limitations include size of dataset, conversion problems with scientific data.
  • Skill Level: No coding
  • Max Size: 2 GB
  • Data Stored: Locally
  • Interactive: Limited
  • Free Version?: Discounts often available for university faculty, students and staff.

Tableau

  • Ability to connect to multiple data sources, combine disparate data without writing code and create interactive visualizations and dashboards on the fly.
  • Best used for fast actionable insights, statistical analysis, interactive maps, and sharing dashboards.
  • Can be cost prohibitive, requires SQL knowledge if wanting to connect to a database and has no concept of versioning.
  • Skill Level: Mostly no coding, but minimal SQL may be required.
  • Max Size: Does not have any enforced row or column limits for the amount of data that can be imported.
  • Data Stored: Locally or Cloud
  • Interactive: Yes
  • Free Version?: Yes, but when publishing reports or dashboards, data is made public.

Google Data Studio

  • A dashboard and reporting tool that is very user-friendly with drag and drop features, and is easy to connect to other Google data sources (ie. Google Analytics and Google Sheets).
  • Best used for interactions with visualizations including filtering through content with dimension and date range selectors, and sharing and setting permissions for viewing reports and dashboards.
  • Limitations include being available only in beta, will only connect to a limited number of data sources, limited types of visualizations, and doesn’t allow you to bring together more than one data source at a time.
  • Skill Level: No coding
  • Max Size: Most charts now allow up to 20 dimensions and 20 metrics. Time series support up to 20 metrics. Tables support 10 dimensions and up to 20 metrics.
  • Data Stored: Cloud
  • Interactive: Yes
  • Free Version?: Yes, with a Google account or Google Cloud Platform customers.

Microsoft Power BI

  • A suite of business analytics tools for analyzing data and sharing insights.  Allows for simple uploading of data from many sources, including .xls, .csv, and .json. Can create custom reports and dashboards, with over 20 built-in visuals and an active community for custom visualizations.
  • Best used for combining data from multiple data sources, custom and interactive visualizations, and the ability to access data and reports from anywhere.
  • The free version only allows public sharing, and reports and dashboards can only source data from a single dataset.
  • Skill Level: No coding
  • Max Size: Please refer to: link
  • Data Stored: Locally or cloud
  • Interactive: Yes
  • Free Version?: Yes, but when publishing reports or dashboards, data is made public.

D3.js

  • A Javascript library for producing interactive data visualizations in web browsers.  It uses scalable vector graphics (SVG), HTML5 and CSS standards.
  • With minimal overhead, D3 is extremely fast, capable of handling large data sets and dynamic behaviour for interaction and animation.
  • D3.js can be rather cumbersome for small amounts of data.  In addition, it cannot easily conceal original data nor generate pre-determined visualizations for you.
  • Skill Level: Javascript required, SVG, HTML and CSS optional
  • Max Size: Rendering is based on number of SVG objects used.  Please refer to link for some more details.  Pre-rendering on the server side is recommended.
  • Data Stored: Locally or cloud
  • Interactive: Yes
  • Free Version?: Yes

Python

  • Python is a high-level programming language that is free to use and is designed to be easy to read and implement.  A few Python libraries that are designed in particular for data visualizations include matplotlab and ggplot.
  • Python is widely used in the whole data science workflow (including related technologies like machine learning, natural language processing, etc.) and is becoming a very popular language.
  • Skill Level: Python coding skills are required.
  • Max Size: N/A
  • Data Stored: Locally or cloud
  • Interactive: Yes
  • Free Version?: Yes

Plot.ly

  • A charting library for creating interactive graphs and charts containing some 20 chart types, including 3D charts, statistical graphs, and SVG maps, built on top of open source software.
  • Best used for when you need to build highly interactive graphs, has a large number of available visualizations, and  includes geo data and maps.
  • More of a demand on internal system resources than other products.  Some previous knowledge of Python or Matlab would be an asset.
  • Skill Level: Python and Matlab experience would be an asset.
  • Max Size: Too much visualized data can cause browsers to freeze or fail to load.
  • Data Stored: Locally or cloud
  • Interactive: Yes
  • Free Version?: Yes but there is a maximum limit of 100 image exports and chart saves per day.

Google Charts

  • Google Charts is another way to present interactive data visualizations on your websites using simple Javascript and pre-defined charts/objects provided by Google.
  • The graphs are customizable and rendered using HTML5/SVG allowing cross browser compatibility.  It is considered easy to learn and is completely free with a Google account.
  • Some downsides include limited customization, minimal statistical processing, and Google having your data.
  • Skill Level: No coding (except if you want to customize using Javascript)
  • Max Size: Based on server and webpage loading on client side.
  • Data Stored: Cloud
  • Interactive: Yes
  • Free Version?: Yes (with Google account)

Before you start creating a dashboard and visualizations, there is some pre-work that should be completed in advance.

  1. Define the purpose of your dashboard. What is it supposed to do/convey? (Eg., educate, influence, communicate, make decisions, take action, etc.)
  2. Determine the Key Performance Indictors or Benchmarks that will best accomplish your purpose.
  3. Choose the metrics necessary to demonstrate your KPIs or Benchmarks. Which metric will help achieve your goal or solve your problem?
  4. Choose the visualization or series of visualizations that can best convey these metrics. A full dashboard, a standalone chart, an infographic?
  5. Now you can begin to build your visualization/dashboard.
 

Commonly cited best practices include:

  1. Consider your audience:
      • Who is going to be using this and why?
      • What do they need? What is the intended function for this dashboard?
      • What is their level of expertise?
      • Do not try to make it one size fits all. It should be targeted to a specific goal. Answer the need from the right perspective.
  2. Use the right visualization tool (graph, chart, etc.) for the data and message that you are trying to convey:
      • Know what type of data you are presenting and the tool that is best able to present this type of data.
      • Know how the visualization will help your audience understand your data.
  3. Keep it simple:
      • 3D is not your friend.
      • Only include critical data.
      • Present high level data, and numbers. Round off numbers and limit the number of numbers (i.e. 32 MIL versus 32,000,000). Tables are the best place for the actual numerical data.
      • An axis should always start at zero.
      • People should not have to struggle to understand your data/math.
  4. Avoid visual clutter. Don’t add any elements that don’t need to be there.
  5. Don’t try and get everything on the one screen:
      • Convey the high level information on the first page.
      • Use subtabs and drill-downs as necessary to get into more detail.
      • Use navigational aids to let people know where they are.
      • One concept per visualization; Busy graphics are harder to read and understand.
  6. Have a colour scheme; don’t go overboard with too many colours.
      • Six or fewer is best.
      • Remember that some people are colour-blind.
  7. Colour should only be used to enhance the information you want to convey; beware of distracting the eye from the data.
  8. Be consistent: naming conventions, colours, icons, styles …
  9. Consider the layout: the most important information should be at the top left for English speaking readers.
  10. Dashboards are more meaningful when they provide the answers needed to take action or solve problems, such as strategies, trends, deviations from the norm.  
  11. Provide context to the visualization. Present the data in the context of your organization’s goals.
  12. You are telling a story with data. Make sure your story is clear and meaningful. Keep asking “What’s the Point?”
  13. People should be able to understand your data in one glance, within eight seconds. Someone not connected to your data should be able to view the dashboard from a distance and identify trends or outliers  in general terms.

Duarte, N. (2014) The quick and dirty on data visualization. Harvard Business Review.

  • 5 questions to ask yourself when visualizing data
  • Includes examples

International Development Research Centre. (n.d.) 10 data visualization tips. Retrieved from the IDRC website.

  • Tip sheet highlighting 10 fundamental points, with some examples

LAC Group Media & Archive Services. (n.d.). Data visualization tip for presenting your data [Blog post]. Retrieved from the Media Lac Group website.

  • Summarizes advice from difference sources (with links)

Fusion Brew. (2017, December 22). 10 dashboard design errors [and how to avoid them] [Blog post]. Retrieved from the Fusion Charts website.

  • Examples of ten common errors with dashboards

Richardson, J. (2009, November 19). Tips for implementers: The basics of good dashboard design. Retrieved from the UMSL website.

  • Lists 12 fundamental points for dashboards
  • Gartner RAS Core Research Note G00171685

Wolny, T. (n.d.). Build a visual dashboard in 10 steps. [Blog post]. Retrieved from the iSixSigma website.

  • Outlines the steps a six sigma company took to design and build a dashboard, from asking the questions, gathering the metrics, creating the design, and delivering the product.

Kaushik, A. (2014, July 15). Digital dashboards: Strategic & tactical: Best practices, tips, examples [Blog post]. Retrieved from Occam’s Razor website.

  • Describes why the best dashboards are not just visualizations of lots of data summarizing performance, but put it into context with three sections: Insights, Actions, and Business Impact.

National Network of Libraries of Medicine. Tools and resources: Analysis and visualization. Retrieved from the Network of the National Library of Medicine website.

  • Tool kit linking to a variety of resources such as blog posts, data analysis, visualization techniques, how to choose the right chart, and examples.

Yellowfin. (n.d.). Data visualization best practices guide. Retrieved from the YellowFin website.

  • Highly visual 17 page guide, one concept per page
  • Illustrations of concepts from choice of chart to targeting your message

Abela, A. (2008). How to choose a chart type. In Advanced presentations by design: Creating communication that drives action. San Francisco: Pfeiffer.

  • A flowchart to help you decide what type of visualization best fits your intentions

Henry, A. (2012). How to choose the best chart for your data [Blog post]. Retrieved from the Life Hacker website.

  • Reviews the steps involved picking the right chart to convey your message
  • Does not go into specific design details but looks at it from a higher level perspective

Sharma, H. (n.d.). Best excel chart types for different kinds of data. Retrieved from the Optimize Smart website.

  • Free 40-page ebook can be read online or downloaded
  • Shows when to best use the charts included in Excel

Hamberg, S. (n.d.). Why you shouldn’t use pie charts [Blog post]. Retrieved from the Funnel website.

  • Presents types of graphs and charts, and when to use them

Cherdarchuk, J. (2014, September 26). Salvaging the pie [Blog post]. Retrieved from: DarkhorseAnalytics.com.

  • How to make your typical pie chart into a better visualization

Microsoft Power BI. (2019, October 27). Best design practices for reports and visuals. Retrieved from the Microsoft Learn website.

  • Looks at the basics of design and layout
  • Walks through a clean-up of a bad visualization
  • Fairly general but does make specific reference to what can be done / how to do it in Power BI

Tableau. Visual analysis best practices: Sample techniques for making every data visualization useful and beautiful. Retrieved from the Tableau website

  • 41-page Position Paper that requires a free download
  • Has many explanations and examples, ranging from choosing a chart, fonts, sizes
  • Is not Tableau specific

“Management consultants frequently point out that ‘what gets measured is what gets done.’ Critics fear that this means organizations will be misled by metrics. To avoid this outcome, leaders are faced with the obligation of determining at the highest level the key questions their organizations must confront, rather than just react to those items easiest to measure and track” (Marcum & Schonfeld, 2014, p. 3).

Connaway, L. S., Harvey, W., Kitzie, V., & Mikitish, S. (2017). Academic library impact: Improving impact & essential areas to research. Chicago: ACRL.

  • Summarizes the incredible strides made and best practices developed by the profession in capturing and emphasizing academic libraries’ contributions to student learning, success, and experience.

Farney, T. (2018). Using digital analytics for smart assessment. Chicago, ALA Editions.

  • Your library collects massive amounts of data related to this journey—probably more than you realize, and almost certainly more than you analyze. Farney shows you how to maximize your efforts: you’ll learn how to improve your data collection, clean your data, and combine different data sources.

Franklin, T., Harrop, H., Kay., D., & van Harmelen, M. (2011). Exploiting activity data in the academic environment. Retrieved from the Activity Data website.

  • This web site synthesises the work of the JISC funded activity data programme in order to help you to understand how you might benefit from exploiting activity data. Includes a special section on library data sources.

Marcum, D., & Schonfeld, R. C. (2014). Driving with data: A Roadmap for evidence-based decision making in academic libraries. New York, NY: Ithaka S+R.

  • COUNTER-compliant usage statistics, service assessments, peer benchmarking—librarians have been gathering different types of data for some time, using data to measure the usage of their resources, the quality of their services, and how they stack up against similar institutions.  But could library leaders collect data differently? In this Issue Brief Deanna Marcum and Roger Schonfeld suggest an approach where library leaders start not with the data that are easy to gather, but with the problems they are trying to solve.  What does it take to create an evidence-based decision-making environment within the academic library?

Orcutt, D. (2009). Library data: Empowering practice and persuasion. Santa Barbara, CA: Libraries Unlimited.

  • Numerical evidence is everywhere and how best to handle and leverage it is a growing concern in the academic world in general and the academic library world in particular. Libraries are not only storehouses and key contacts for library patrons in accessing numbers, but are also collectors and users of their own data, which is integral to the functioning of the library itself.

  • What key performance indicators (KPIs) have been identified as important? How do they relate to our academic institution’s key strategic priorities?
  • Do we have the organizational structure, technical skills, and system in place to assemble, manage, and analyze the data?
  • Do we have the data we need? Do our systems and processes collect it? How much time and expense will be required to collect evidence?
  • If we don’t have the data, how do we start collecting it? Can we get it from somewhere else in the academic institution? What are the risks and what are the benefits of gathering evidence from external stakeholders?
  • Is there enough of it to make any sense? How long have we been collecting data and how much data is collected per year? What are the risks and benefits of simply making the decision oneself or internally?
  • Should we combine a number of data sources to paint a fuller picture? If so, are there reliable data elements held in common across the data sources to make combination possible (e.g. Student ID number)? If not, can we begin adding this data element to a data set to make linkage possible?
  • Will the analysis serve the analytical purpose (i.e. provide KPI)  we have in mind? Or could it trigger new analyses?
  • Remember that data, like statistics, can lend itself to different interpretations. What does “high website traffic really mean”?
    • the site is valuable and used?
    • the same few people make high use of it but most people don’t use it?
    • people are trying different things because they can’t find what they are looking for?
  • Data can be quantitative or qualitative. Data can be pre-existing or can be gathered to meet a specific need. Data can be at the local level, whether branch, library system, or institution, or can be at a regional, national, or association level.
  • There are many different methods of collecting data, from manual collection of tick marks on a sheet of paper to automated downloads from an online source, from formal research to published reports.
  • Automated data can be acquired by manual data entry, by capturing data during an activity (i.e. circulating a book captures data on the book, the user, the time period), or by data logs generated as traffic moves though a system (i.e web page views).
  • There are many different programs that can be used to compile and manipulate your data, from simple calculations on a sheet of paper, to excel spreadsheets or more data intensive statistical programs like SPSS or SAS, from analytic modules within your library services software to purchased data services.

“Many academic institutions use benchmarks to identify their strengths and weaknesses in comparison to similar institutions. For example, benchmarking can be used to demonstrate whether an institution or its library is funded or staffed at levels comparable to similar institutions in a geographic area, with a similar enrollment, or with other related characteristics. An institution or library can use benchmarking to inform the strategies it develops to enhance its institutional quality and effectiveness” (ACRL, 2018, p. 21).

Basic Examples

  • Institutional expenditure on library relative to peers
  • Number of students and faculty relative to peers
  • Library expenditures per student and faculty relative to peers
  • Collection budgets (physical and electronic) relative to peers
  • Online resources (journals, books, and databases) relative to peers
  • Number of library staff relative to peers

More Complex Example

Lewin, H. S., & Passonneau, S. M. (2012). An analysis of academic research libraries assessment data: A look at professional models and benchmarking data. The Journal of Academic Librarianship, Volume 38(2), 85-93.

Benchmarking Data Sources

Key Report​

ACRL. (2018). Standards for Libraries in Higher Education. Chicago, IL: ACRL.

  • The Standards for Libraries in Higher Education are designed to guide academic libraries in advancing and sustaining their role as partners in educating students, achieving their institutions’ missions, and positioning libraries as leaders in assessment and continuous improvement on their campuses. Libraries must demonstrate their value and document their contributions to overall institutional effectiveness and be prepared to address changes in higher education, including accreditation and other accountability measures.

Basic Examples

  • Number of titles (physical + electronic)
  • Total library materials expenditures percentages. E.g.: Monograph expenditures as percentage of total library materials expenditures
  • Materials expenditures to total library expenditures percentages. E.g.: Total library materials expenditures as percentage of total library expenditures
  • Total library materials expenditures per student. E.g.: Per full-time undergraduate student. Per full-time graduate student.
  • Total library materials expenditures per faculty. E.g.: Per full-time faculty. Per part-time faculty

More Complex Example

Key Report

Harker, K. R., & Klein, J. (2016, September). Collection assessment. SPEC Kit 352. Washington, DC: Association of Research Libraries.

  • This study gathered information on which library staff collect and analyze data, for what purposes the results are used, with whom data is shared, how well assessment questions are answered. The survey also investigated whether the available methods, data, and tools are aligned with the purposes for assessing collections.

“Libraries enable users to discover information in all formats through effective use of technology and organization of knowledge” (ACRL, 2018, p. 9).

Basic Examples

  • Number of users accessing library’s web site
  • Number of users accessing licensed electronic resources: COUNTER provides the Code of Practice that enables publishers and vendors to report usage of their electronic resources in a consistent way. This enables libraries to compare data received from different publishers and vendors.
  • Number of items downloaded
  • Number of book loans

More Complex Example

Key Report

  • MINES for Libraries: As libraries implement access to electronic resources through portals, collaborations, and consortial arrangements, the Measuring the Impact of Networked Electronic Services (MINES) for Libraries protocol offers a convenient way to collect information from users in an environment where they no longer need to physically enter the library in order to access resources.

“Libraries partner in the educational mission of the institution to develop and support information-literate learners who can discover, access, and use information effectively for academic success, research, and lifelong learning” (ACRL, 2018, p. 9).

Basic Examples

  • Number of group training sessions (physical and virtual) offered
  • Number of attendees (physical and virtual) at group training sessions
  • Number of courses in which librarians are embedded
  • Number of students reached through embedded information literacy initiatives

More Complex Example

Key Report

Oakleaf, M. (2010). Value of academic libraries: A comprehensive research review and report. Chicago, ACRL.

  • The ACRL publication Value of Academic Libraries: A Comprehensive Research Review and Report is a review of the quantitative and qualitative literature, methodologies and best practices currently in place for demonstrating the value of academic libraries, developed for ACRL by Megan Oakleaf of the iSchool at Syracuse University.

“Libraries are the intellectual commons where users interact with ideas in both physical and virtual environments to expand learning and facilitate the creation of new knowledge” (ACRL, 2018, p. 9).

Basic Examples

  • Ratio of library seats to FTE student population
  • Type of learning spaces and accompanying technology available to user community
  • Number of hours open each week during academic sessions
  • Number of days open each fiscal year
  • Gate counts. E.g.: Per FTE student. During extended hours of fall and spring semesters.

More Complex Example

Key Report

Lippincott, J. K., & Duckett, K. (2013). Library space assessment: Focus on learning. Research Libraries Issues: A Report from ARL, CNI, and SPARC, (284), 21-21.

  • The “Library Space Assessment: Bringing the Focus to Teaching and Learning” 3 workshop, held at the Library Assessment Conference in November 2012, was designed to help participants think more deeply about connecting completed space renovation assessment to student learning.
  • From Dalhousie, using Tableau
  • From Waterloo, using Microsoft Power BI
  • From Waterloo, using D3.js
  • Created example, using Excel
  • From Dalhousie, using Tableau
  • Created example of word cloud, showing subject analysis of books purchased in 2000, using wordclouds.com
  • Duke University – Excel to MySQL
    Five course specialization on Coursera. Learn to frame business challenges as data questions. Use Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights. Weekly commitment varies by course.
  • Data Camp
    Hosts a variety of data science courses focused on a variety of tools and skillset-building.
  • University of Illinois – Data Mining Specialization
    Six-course introduction to data mining techniques available as a Coursera specialization. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization.
  • CARL Canadian Library Assessment Workshop (CLAW)
    CARL is committed to supporting and developing outcomes-based measures to assist libraries in moving beyond inputs and outputs in order to better demonstrate library impact on research, teaching and learning. Held biannually.
  • Data Science and Visualization Institute (DSVIL)
    The Data Science and Visualization Institute for Librarians is a week-long course for librarians who are passionate about research and scholarship. Develop knowledge, skills, and confidence to communicate effectively with faculty and student researchers about their data and be able to provide initial consultancy on course topics. Acceptance to the annual Data Science and Visualization Institute for Librarians is made through a competitive application process.
  • Tableau In-Person Training
    Tableau training workshops are available in Edmonton, Alberta, as well as other cities worldwide. Click on “In-Person Training” to search for nearby upcoming workshops.
  • ARL – Library Assessment Conference
    The conference goal is to build and support the wide-reaching community of library assessment practitioners and researchers who have responsibility or interest in this broad field. The multi-day event is a mix of invited speakers, workshops, and contributed papers and posters that stimulate discussion and provide feasible ideas for effective, practical, and sustainable library assessment.
  • University of Illinois – Urbana-Champaign – MS in Information Management
    The MS/IM degree consists of 40 hours of coursework including three required courses. Additional, elective courses are chosen in consultation with the student’s advisor. Four pathways of focus: data science and analytics; privacy, trust, security and ethics; information architecture and design; and knowledge management and information consulting.

Guides Created by Others on Data Visualization

Further Reading

Cox, B., & Jantti, M. (2012, July 17). Discovering the impact of library use and student performance. Educause Review.

Leitner P., Khalil M., & Ebner M. (2017). Learning analytics in higher education: A literature review. In A. Peña-Ayala (Ed.), Learning analytics: Fundaments, applications, and trends (pp. 1-23). Cham, Switzerland: Springer.

Roberts, L. D., Howell, J. A., & Seaman, K. (2017). Give me a customizable dashboard: Personalized learning analytics dashboards in higher education. Technology, Knowledge and Learning, 22(3), 317-333.

De Laet, T., Charleer, S., Verbert, K., Langie, G., & Van Soom, C. (2017). Learning analytics dashboard to support the live interaction between student advisor and student. Presented at the European First Year Experience Conference 2017, Birmingham, UK.

Briney, K., Asher, A., Goben, A., Jones, K. M., Perry, M., Robertshaw, M. B., & Salo, D. (2018). Student learning analytics in libraries – thoughts and resources.

Callahan, S. P., Freire, J., Santos, E., Scheidegger, C. E., Silva, C. T., & Vo, H. T. (2006). VisTrails: Visualization meets data management. Proceedings from SIGMOD ’06: The International Conference on Management of Data (pp. 745-747).

Wang, M., & Fong, B. L. (2015). Embedded data librarianship: A case study of providing data management support for a science department. Science & Technology Libraries, 34(3), 228-240.

Martin, E. R. (2014). What is data literacy? Journal of eScience Librarianship, 3(1), e1069-e1070.

Tenopir, C., Talja, S., Horstmann, W., Late, E., Hughes, D., Pollock, D., Schmidt, B., Baird, L., Sandusky, R., & Allard, S. (2017). Research data services in European academic research libraries. LIBER Quarterly, 27(1), 23-44.

Ball, A., Darlington, M., Howard, T., McMahon, C., & Culley, S. (2012). Visualizing research data records for their better management. Journal of Digital Information, 13(1).

Palpanas, T., & Wu, Z. (Eds.). Big Data Research.

W.K. Kellogg Foundation Logic Model Development Guide [Website]

Social Solutions. (n.d.) Three keys to improving your program effectiveness evaluation. [Blog post] Retrieved from Social Solutions website.

ALA. (2016, August). Performance measurement: Introduction to project outcome. Retrieved from ALA website.

Anthony, C. A. (2014, July 7). Moving toward outcomes [Blog post]. Retrieved from Public Libraries Online website.

McNamara, C. (n.d.). Basic guide to program evaluation (including outcomes evaluation). Retrieved from Management Help website.

Troll, D. A., (2001). How and why are libraries changing? Digital Library Federation.

School Libraries Impact Studies [website]

Kowalcyzk, P. (2018, March 18). Libraries matter: 18 fantastic library infographics and charts. Retrieved from Ebook Friendly website.

Operational Dashboard – A regularly updated answer to a question or line of inquiry that frequently monitors operational concerns in response to events or on an ad-hoc basis.

Strategic/Executive Dashboard – A high level view of a question or line of inquiry that is usually answered in a routine, specific way and usually presents Key Performance Indicators in a minimally interactive way.

Analytical Dashboard – A highly interactive view that provides a variety of investigative approaches to a specific central topic with a few corollary contextual views.

From Unilytics

Data Visualization = a visual representation of large amounts of numerical data. The objective is to make sense of the data, or make the data more accessible.

Infographic = a visual representation of facts, events or numbers. Infographics try to tell a story; the focus is on informing or educating. The information is presented with context, with a narrative, that elaborates on the topic; it is often subjective. Infographics generally contain multiple data visualizations.

Dashboard = the equivalent of the automotive dashboard, displaying real-time changes to strategic or tactical data, usually displayed visually. Often has the ability to drill down through high level data into increasing detail. Focus is on operations and driving decision making. Time period is usually real-time. Tries to pinpoint exceptions, problems, or trends.

Scorecard = a visual representation of a business strategy, distinguished by a regimented top down process. Identifies a selection of key performance metrics to meet the organization’s goals and drills down through supporting departments and their metrics. Focus is on strategy. Time period is usually a historic point in time. Tries to pinpoint if you are meeting your goals.

The lines between dashboards and scorecards are being blurred as there is considerable overlap. Scorecards and dashboards are both concerned with measuring performance against KPIs, and communicating it in an easily “understood at a glance” format, that allows for immediate action. The two terms are often interchanged.

Archambault, S. G. (2016, April). Telling Your Story: Using Dashboards and Infographics for Data Visualization. Computers in Libraries (April 2016). Retrieved from the Information Today website.

  • Brief article with example of the difference between the two.

Savkin, A. (2019, June 24). What’s the difference between a dashboard and a balanced scorecard? [Blog post]. Retrieved from the BSC Designer website.

  • Contains a table explaining the difference from BSC Designer.
 
  • Explanation of why you would use one or the other.

BI Dashboards. (n.d.). Scorecards vs. dashboards. Retrieved from the BI Dashboard website.

Dili. (2017, Apri 17). Difference between dashboard and scorecard [Blog post]. Retrieved from the Difference Between website.

 
  • Overview, key differences, and comparison.

Benchmark = A standard of excellence against which similar items are measured.

BSC = Balanced Scorecard – a structured strategy performance management report with three critical elements: 1) a focus on strategic goals; 2) the monitoring of a small selection of data points; 3) the mix of both financial and non-financial data.

KPI = Key Performance Indicator – A measure that allows a company to determine how effectively they are achieving their objectives.

Qualitative data = When observations are classified, judged or categorized.
Ex.: Whether a user was satisfied with library service.

Quantitative data = When observations are counted or measured. Also called numerical data.
Ex.: Gate counts.

Discrete data = Numerical data that can be counted but not measured.
Ex.: Number of librarians employed at the library.

Continuous data = Numerical data that can be measured, or if counted can be broken down into smaller and more precise numbers.
Ex.: Time spent per reference encounter.

Nominal data = No natural order between categories.
Ex.: Tracking reference questions in different subject areas (ex. social sciences, humanities, fine arts, STEM, etc.).

Ordinal data = Ordered categories.
Ex.: Tracking reference questions from undergraduates, graduates, faculty.

For more types of visualizations, see also the “Types of Visualizations” box on the “Tools & Technology” tab.

For information on how to choose a type, see this flowchart and also “Specific Kinds of Charts/Visualization” under “Links” on the “Best Practices” tab.

  • Line Charts: show trends and how data changes over  time
  • Bar Graphs (Horizontal, Vertical, Stacked): comparative rankings, growth over specific periods
  • Pie Charts: proportional composition out of 100%
  • Gauge Charts: display progress against key performance indicators
  • Scatter Plot: correlate large data sets
  • Maps: visualize data geographically
  • Heat Maps: use colour to show highs and lows
  • Word Clouds:  weights words relative to one another
  • Time Lines: series of events in chronological order
  • Tree Maps: display hierarchical data
  • Bubble Map: display proportional data by location
  • Infographics: visual representation of data and text generally intended for mass communication
  • Tables: for when you need to know the underlying data

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