Impediment to insight to innovation: understanding data assemblages through the breakdown–repair process

ABSTRACT

As the era of ‘big data’ unfolds, researchers are increasingly engaging with large, complex data sets compiled from heterogeneous sources and distributed across networked technologies. The nature of these data sets makes it difficult to grasp and manipulate their materiality. We argue that moments of breakdown – points at which progress is stopped due to a material limitation – provide opportunities for researchers to develop new imaginations and configurations of their data sets' materiality, and serve as underappreciated resources for knowledge production. In our ethnographic study of data-intensive research in an academic setting, we emphasize the layers of repair work required to address breakdown, and highlight incremental innovations that stem from this work. We suggest that a focus on the breakdown–repair process can facilitate nuanced understandings of the relationships and labour involved in constituting data assemblages and constructing knowledge from them.

KEYWORDS:

• Big data
• ethnography
• data science
• materiality
• breakdown
• repair

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Anissa Tanweer is a Ph.D. student in the Department of Communication at the University of Washington and a research assistant in the Human-Centered Data Science Lab. She is interested in the ways people organize with and around data, and how the availability of increasingly large, heterogeneous data sets is transforming the way we construct knowledge and make decisions. [email: [email protected]]

Brittany Fiore-Gartland is a data science ethnographer in the eScience Institute and the Department of Human Centered Design and Engineering at the University of Washington. She is a Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation Data Science Postdoctoral Fellow and a Washington Research Foundation Innovation Fellow. Her research focuses on the emerging practices and culture around data-intensive science. She is interested in the social and organizational dimensions of data-intensive transformations occurring across multiple sectors of work. [email: [email protected]]

Cecilia Aragon is an Associate Professor in the Department of Human Centered Design & Engineering and a Senior Data Science Fellow at the eScience Institute at the University of Washington, where she directs the Human-Centered Data Science Lab. She earned her Ph.D. in Computer Science from UC Berkeley in 2004. Her research focuses on human-centered data science, an emerging field at the intersection of computer-supported cooperative work and the statistical and computational techniques of data science. In 2008, she received the Presidential Early Career Award for Scientists and Engineers (PECASE) for her work in collaborative data-intensive science. Web: http://faculty.washington.edu/aragon/. [email: [email protected]]

ORCID

Brittany Fiore-Gartland http://orcid.org/0000-0003-3883-5874

Notes

1. As part of the organization's effort to emulate a private sector start-up incubator model, they adopted certain terminology and procedures from the concepts of Agile software development, which is a set of software development methods aimed at fostering an adaptive project life cycle. In the DSC, aspects of some of these methods were imported into an academic context, including the stand-up meetings and the framing of problems as blockers.

2. RAM is the acronym for random access memory, which is the main type of computer memory that can be accessed randomly and can be quickly reached by the computer's processor.

Additional information

Funding

This work was supported by the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation.