Communicating with Visualization: The Importance of Simplicity

Abstract

Thesis Defense:

With the growing ubiquity and capability of data science, the topic of making advanced analytics accessible and understandable to users with little to no analytic expertise is rapidly growing. However, most tools designed to increase accessibility of data science still require certain levels of proficiency in advanced analytics and programming from users. For users lacking this proficiency, there is a gap between available data science techniques and the ability to utilize them without outside help. In this talk, we seek to determine how visualization can bridge this gap. We begin with an overview of an interview study of client-facing data scientists that reveals a fundamental communication problem: helping non- analytic experts understand large amounts of data in a limited time and make sense of difficult analytic concepts. Overcoming this fundamental communication problem motivates three case studies, grounded in real world scenarios, that investigate specific facets of the problem. This talk will present highlights from each of these studies. Taken together, the findings of our studies provide a clear and repeated message: simple visualizations are more effective than complex visualizations when it comes to overcoming the fundamental communication problem. Moreover, our results imply that the key to overcoming the fundamental communication problem is simplifying visualizations in terms of the data shown, perceived complexity, and cognitive load. Our work highlights how heavily these factors influence each other and stresses the importance of considering all three when simplifying visualizations.

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