Natto: Rapid Visual Iteration of Analytic Data Models with Intelligent Assistance

Abstract

Data analysts need to routinely transform data into a form conducive for deeper investigation. While there exists a myriad of tools to support this task on tabular data, few tools exist to support analysts with more complex data types. In this study, we investigate how analysts process and transform large sets of XML data to create an analytic data model useful to further their analysis. We conduct a set of formative interviews with four experts that have diverse yet specialized knowledge of a common dataset. From these interviews, we derive a set of goals, tasks, and design requirements for transforming XML data into an analytic data model. We implement Natto as a proof-of-concept prototype that actualizes these design requirements into a set of visual and interaction design choices. We demonstrate the utility of the system through the presentation of analysis scenarios using real-world data. Our research contributes novel insights into the unique challenges of transforming data that is both hierarchical and internally linked. Further, it extends the knowledge of the visualization community in the areas of data preparation and wrangling.