Big Data Visualizations in Organizational Science

Abstract

Visualizations in organizational research have primarily been used in the context of traditional survey data, where individual data points (e.g., responses) can typically be plotted, and qualitative (e.g., language data) and quantitative (e.g., frequency data) information are not typically combined. Moreover, visualizations are typically used in a hypothetico-deductive fashion to showcase significant hypothesized results. With the advent of big data, which has been characterized as being particularly high in volume, variety, and velocity of collection, visualizations need to more explicitly and formally consider the issues of (a) identification (isolating or highlighting relevant data pertaining to the phenomena of interest), (b) integration (combining different modes of data to reveal insights about a phenomenon of interest), (c) immediacy (examining real-time data in a time-sensitive manner), and (d) interactivity (inductively uncovering and identifying new patterns). We discuss basic ideas for addressing these issues and provide illustrative examples of visualizations that incorporate and highlight ways of addressing these issues. Examples in our article include visualizing multiple performance criteria for police officers, publication network of organizational researchers, and social media language of Fortune 500 companies.

Enhancing Rigor in Quantitative Entrepreneurship Research

“…This means that in addition to standard presentations (e.g., interaction plots), creativity is encouraged to develop novel graphical approaches for evidence presentation (e.g., heat maps). This is particularly valuable and relevant when analyzing big data (Tay et al, 2018). Facilitated by new visualization tools in data science, such as ggplot2 for R, Matplotlib, Seaborn, Plotly, and Bokeh for Python, PROC SGPLOT for SAS and many others, there are increasing opportunities for creating novel insights through better visual presentations in quantitative entrepreneurship research.…”

Multilevel Methods and Statistics: The Next Frontier

“…Data visualization techniques may also be helpful to develop alternative ways of characterizing more complex emergent phenomena (cf. Tay et al, 2018). More broadly, statistical tests or cut values are needed to help guide researchers in assessing when these complex constructs have emerged to a higher level.…”

Advancing Meta-Analysis With Knowledge-Management Platforms: Using metaBUS in Psychology

“…In several areas of psychology, the number of meta-analytic estimates, crossed with moderator levels, often yields results tables that span many pages and may not present a clear, digestible picture of the results. Just as visualizations are useful for big-data studies (Tay et al, 2017), innovative visualizations can benefit meta-analyses. Our approach, which we label exploratory meta-analytic visualization ( EMV ), may be applied at the meta-analytic effect-size level to provide a picture of all of a given construct’s bivariate relations.…”