Passing the Data Baton : A Retrospective Analysis on Data Science Work and Workers

Academic data science: Transdisciplinary and extradisciplinary visions

As a nascent field within the academy, the contours, attributes, and bounties of data science are still indeterminate and contested. We studied how participants in an initiative to establish data science at a large American research university defined data science and articulated their relationships to the field. We discuss two contrasting visions for data science among our research participants. One vision is a transdisciplinary view portraying data science as a phenomenon with transcendent, appropriative, and impositional qualities that sits apart from academic domains. Another view of data science-one that was far more prevalent among our research subjects-casts data science as grounded, relational, and adaptive, emerging from crosspollination of numerous academic domains. We argue that this latter formulation represents a more quotidian reality of data science and positions the field as an extradiscipline, defined as a field that exists to facilitate the exchange of knowledge, skills, tools, and methods from an indeterminate and fluctuating set of disciplinary perspectives while conserving the boundaries of those disciplines. We argue that the dueling transdisciplinary and extradisciplinary visions for data science have important implications for how the field will mature, and that the extradiscipline concept opens novel directions for studying academic knowledge production in STS, contributing additional precision to the literature on disciplinarity and its permutations.

Transitioning to a Commercial Dashboarding System: Socio-Technical Observations and Opportunities

Many long-established, traditional manufacturing businesses are becoming more digital and data-driven to improve their production. These companies are embracing visual analytics in these transitions through their adoption of commercial dashboarding systems. Although a number of studies have looked at the technical challenges of adopting these systems, very few have focused on the socio-technical issues that arise. In this paper, we report on the results of an interview study with 17 participants working in a range of roles at a long-established, traditional manufacturing company as they adopted Microsoft Power BI. The results highlight a number of socio-technical challenges the employees faced, including difficulties in training, using and creating dashboards, and transitioning to a modern digital company. Based on these results, we propose a number of opportunities for both companies and visualization researchers to improve these difficult transitions, as well as opportunities for rethinking how we design dashboarding systems for real-world use.

A Model for Types and Levels of Automation in Visual Analytics: A Survey, a Taxonomy, and Examples

The continuous growth in availability and access to data presents a major challenge to the human analyst. As the manual analysis of large and complex datasets is nowadays practically impossible, the need for assisting tools that can automate the analysis process while keeping the human analyst in the loop is imperative. A large and growing body of literature recognizes the crucial role of automation in Visual Analytics and suggests that automation is among the most important constituents for effective Visual Analytics systems. Today, however, there is no appropriate taxonomy nor terminology for assessing the extent of automation in a Visual Analytics system. In this article, we aim to address this gap by introducing a model of levels of automation tailored for the Visual Analytics domain. The consistent terminology of the proposed taxonomy could provide a ground for users/readers/reviewers to describe and compare automation in Visual Analytics systems. Our taxonomy is grounded on a combination of several existing and well-established taxonomies of levels of automation in the human-machine interaction domain and relevant models within the visual analytics field. To exemplify the proposed taxonomy, we selected a set of existing systems from the event-sequence analytics domain and mapped the automation of their visual analytics process stages against the automation levels in our taxonomy.

Untidy Data: The Unreasonable Effectiveness of Tables

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets - the quintessential table tool - remain a critical part of their information ecosystem, allowing them to interact with their data in ways that are hidden or abstracted in more complex tools. This is particularly true for data workers [61], people who work with data as part of their job but do not identify as professional analysts or data scientists. We report on a qualitative study of how these workers interact with and reason about their data. Our findings show that data tables serve a broader purpose beyond data cleanup at the initial stage of a linear analytic flow: users want to see and "get their hands on" the underlying data throughout the analytics process, reshaping and augmenting it to support sensemaking. They reorganize, mark up, layer on levels of detail, and spawn alternatives within the context of the base data. These direct interactions and human-readable table representations form a rich and cognitively important part of building understanding of what the data mean and what they can do with it. We argue that interactive tables are an important visualization idiom in their own right; that the direct data interaction they afford offers a fertile design space for visual analytics; and that sense making can be enriched by more flexible human-data interaction than is currently supported in visual analytics tools.

From Jam Session to Recital: Synchronous Communication and Collaboration Around Data in Organizations

Prior research on communicating with visualization has focused on public presentation and asynchronous individual consumption, such as in the domain of journalism. The visualization research community knows comparatively little about synchronous and multimodal communication around data within organizations, from team meetings to executive briefings. We conducted two qualitative interview studies with individuals who prepare and deliver presentations about data to audiences in organizations. In contrast to prior work, we did not limit our interviews to those who self-identify as data analysts or data scientists. Both studies examined aspects of speaking about data with visual aids such as charts, dashboards, and tables. One study was a retrospective examination of current practices and difficulties, from which we identified three scenarios involving presentations of data. We describe these scenarios using an analogy to musical performance: small collaborative team meetings are akin to jam session, while more structured presentations can range from semi-improvisational performances among peers to formal recitals given to executives or customers. In our second study, we grounded the discussion around three design probes, each examining a different aspect of presenting data: the progressive reveal of visualization to direct attention and advance a narrative, visualization presentation controls that are hidden from the audience's view, and the coordination of a presenter's video with interactive visualization. Our distillation of interviewees' responses surfaced twelve themes, from ways of authoring presentations to creating accessible and engaging audience experiences.