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Big Data Visualizations in Organizational Science

机译:组织科学中的大数据可视化

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摘要

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.
机译:组织研究中的可视化主要用于传统调查数据的背景下,其中通常可以绘制单个数据点(例如响应),而定性(例如语言数据)和定量(例如频率数据)信息通常不被绘制结合。此外,可视化通常以假设演绎的方式使用,以展示重要的假设结果。随着大数据的出现,其特点是收集的数量,种类和速度特别高,可视化需要更加明确和正式地考虑以下问题:(a)识别(隔离或突出显示与现象有关的相关数据)兴趣),(b)集成(组合不同的数据模式以揭示有关兴趣现象的见解),(c)即时性(以时间敏感的方式检查实时数据)和(d)交互性(归纳地揭示)并确定新的模式)。我们讨论了解决这些问题的基本思路,并提供了可视化示例,这些示例结合并强调了解决这些问题的方式。本文中的示例包括可视化警察的多个绩效标准,组织研究人员的发布网络以及《财富》 500强公司的社交媒体语言。

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