Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices

Abstract

We report the results of interviewing thirty professional data analysts working in a range of industrial, academic, and regulatory environments. This study focuses on participants' descriptions of exploratory activities and tool usage in these activities. Highlights of the findings include: distinctions between exploration as a precursor to more directed analysis versus truly open-ended exploration; confirmation that some analysts see "finding something interesting" as a valid goal of data exploration while others explicitly disavow this goal; conflicting views about the role of intelligent tools in data exploration; and pervasive use of visualization for exploration, but with only a subset using direct manipulation interfaces. These findings provide guidelines for future tool development, as well as a better understanding of the meaning of the term "data exploration" based on the words of practitioners "in the wild."

Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau

“… Papers that analyze or design for EVA contexts were selected. Papers described or referenced by papers from step 1 as also analyzing or designing for EVA were selected. Tasks or topics from papers from step 1 that were described as relevant to EVA were identified. Task relevance suggested by paper authors or study subjects was considered (e.g., subjects' comments observed by Alspaugh et al [AZL∗19]). Then relevant, well‐known papers that also discuss these tasks and topics were identified, such as the work by Kandel et al [KPHH12].…”

“…Lam et al [LTM18] describe the goal of exploration as “Discover[ing] Observation[s]”; however, this goal is vague in comparison to other visual analytic goals. Liu & Heer argue that EVA often “does not have a clear goal state” [LH14], which is a popular sentiment in both the visualization [Kei01,AZL∗19,RJPL16] and database [IPC15] communities. For example, Idreos et al [IPC15] describe EVA as a situation where analysts may not know exactly what they are looking for, but they will know something interesting when they see it.…”

“…Keim makes a stronger argument: that EVA is more effective when the goals are less clear [Kei01]. Alspaugh et al [AZL∗19] take this idea even further by saying that exploration does not have well‐formed goals; once clear goals are formed, the analysis is no longer exploration.…”

“…Bottom‐Up Versus Top‐Down Exploration : Exploration is often described as “open‐ended”, where many of the papers we reviewed equate exploration with at most vaguely defined tasks (e.g., [LH14,RJPL16,AZL∗19,ZGC∗16,ZZZK18]): visual analysis performed without an explicit objective, perusing a dataset for interesting observations. Open‐endedness seems to be tightly coupled with the notion of opportunistic analysis [Tuk77, LH14, AZL∗19, RJPL16]. For example, Alspaugh et al [AZL∗19] argue that during EVA, “… actions are driven in reaction to the data, in a bottom‐up fashion…”.…”

“…Furthermore, we find several “schools of thought” on EVA, some of which may contradict one another. For example, it is argued that exploration can only be open‐ended, without clear a priori goals or hypotheses (e.g., [AZL∗19]). In contrast, we also see specific examples where analysts come to an EVA session with a clear goal or hypothesis in mind (e.g., [FPDsl2, SKL∗16]).…”