My research interest in brief: how do information analysts sift through and make sense of large amounts of data?
More broadly, this research involves comparing analyst behaviour and processes across domains (intelligence analysts, journalists, bioinformaticians, aerospace engineers). Thankfully there are now countless industries and research areas working with large datasets.
“The purpose of visualization is insight, not pictures” – Ben Shneiderman (1999)
These tools are built for providing insight into the data. In the following visual conceptual map, I’ve decided to unpack this term and its related concepts (a work-in-progress!).
Given these relationships, I’m beginning to frame my research with the following high-level questions:
- What constitutes insight, or a unit of discovery? When is insight the means hypothesis generation) or the ends (hypothesis validation)? Can we establish a shared interpretation of insight that traverses domains?
- How do existing data analysis and visualization practices facilitate insight via different types of problem-solving strategies? Where do they break down? How is collaborative analysis and problem-solving supported?
- How does one develop expertise as an data analyst?
- How does a novice analyst initially explore a dataset? How is this different from how an expert analyst would explore a dataset?
- How does collaboration between novice and expert analysts, or between novice analysts, effect the development of expertise?
- Are there commonalities across domains with regards to how one learns to use information analysis tools and techniques?
- How does one learn domain-independent analysis tasks (those that span multiple tools and techniques)?
I’m now five months into my PhD. The next step involves putting these high-level questions into action, using them to frame concrete questions that I will put to data analysts in different domains.