Education

What comes to mind when you read or hear the words “data visualization (DV)?” Some might imagine a sort of data “placebo” that works on the eye rather than on the palate. Indeed, it is a relatively little-known fact that facts and figures can be likened to a bitter dose of medicine to those who are averse to, well, to numbers and to statistical analyses! One might envision some type of stylized pictorial that makes data and statistics more digestible and more meaningful to core stakeholders, but what soon becomes obvious after a quick dive into the literature is that data visualization goes beyond that which merely meets the eye. If one picture is worth a thousand words, think what pictures can do to a set of dry and boring data! In fact, some librarians involved in educational outreaches in health sciences and other institutions have created workshops surrounding good data visualization practices—and that is not all [1].

DV is being used by librarians and by administrators alike as a marketing tool [2], as a means of informing decision-makers [3], as a method of translating data into thought-provoking narratives [3] [4], as a bargaining device [4], and more. Stephanie J. Spratt writes about bringing data visualizations to talks between library managers and vendors haggling over subscription services and other negotiations [4]. Plus, many students and researchers utilize DV projects to illustrate research or to highlight portions of assigned papers. DV can even be an interactive, living component of the actual research process [5].

While it is true that a number of sources on the subject of DV do not necessarily agree on rules of construction, most recommend strategic manipulation of white space as well as clarity over busyness. Colors are important, but, again, not all sources concur on what color schemes or hued combinations are the most harmonious. Types of visualizations or the form graphics should take is yet another area that lacks consistency across the literature—although, perhaps the target group or the chosen audience is key to informing this aspect of the DV formation process. For instance, if a vendor is the intended viewer, then it may be that data visualizations can be more technical and formulaic to get the attention of someone who is business and numbers oriented. People involved in sales are presumably not unaccustomed to reading bar charts, pie charts, line charts, and the like. If, on the other hand, the target audience is a group of civic leaders from various backgrounds and professions, the DV could be more organic and user-friendly. In addition, you want to keep American Disabilities Act compliance in mind when mapping out graphics, color, and textual elements associated with any DV.

Of course, the underlying theme projected through several sources promoting DV is the need to communicate something of which everyone can relate—not just numbers, data, and facts, but something vital and valuable about the library’s overall impact on its constituency. It is not enough to plot and to translate data into graphic form. To be effective, data visualization should support library goals and objectives. The best guide to drawing up solid and attractive DV may simply be matching up knowledge of the target audience with good design techniques coupled with basic marketing savvy. Let the visualization bring the facts to life in such a way that the message transcends the figures being conveyed. Be fearless in unleashing (or leashing) your creativity!

References

1. Zametkin LaPolla FW, Rubin D. The “Data Visualization Clinic”: a library-led critique workshop for data visualization. Journal of the Medical Library Association. 2018;106(4):477-482. doi:10.5195/jmla.2018.333
2. Bouquin D, Epstein H-AB. Teaching Data Visualization Basics to Market the Value of a Hospital Library: An Infographic as One Example. Journal of Hospital Librarianship. 2015;15(4):349-364. doi:10.1080/15323269.2015.1079686
3. Eaton M. Seeing Library Data: A Prototype Data Visualization Application for Librarians. Journal of Web Librarianship. 2017;11(1):69-78. doi:10.1080/19322909.2016.1239236
4. Spratt SJ. Datavi$: Negotiate Resource Pricing Using Data Visualization. Serials Librarian. 2018;74(1-4):111-115. doi:10.1080/0361526X.2018.1428002
5. Miller A. Data Visualization as Participatory Research: A Model for Digital Collections to Inspire User-Driven Research. Journal of Web Librarianship. 2019;13(2):127-155. doi:10.1080/19322909.2019.1586617