Data Science as an Interdiscipline: Historical Parallels from Information Science

Abstract

Considerable debate exists today on almost every facet of what data science entails. Almost all commentators agree, however, that data science must be characterized as having an interdisciplinary or metadisciplinary nature. There is interest from many stakeholders in formalizing the emerging discipline of data science by defining boundaries and core concepts for the field. This paper presents a comparison between the data science of today and the development and evolution of information science over the past century.Data science and information science present a number of similarities: diverse participants and institutions, contested disciplinary boundaries, and diffuse core concepts. This comparison is used to discuss three questions about data science going forward: (1) What will be the focal points around which data science and its stakeholders coalesce? (2) Can data science stakeholders use the lack of disciplinary clarity as a strength? (3) Can data science feed into an "empowering profession"? The historical comparison to information science suggests that the boundaries of data science will be a source of contestation and debate for the foreseeable future. Stakeholders face many questions as data science evolves with the inevitable societal and technological changes of the next few decades.

20 Years of Data Science: An Editorial

“…Matthew Mayernik (2023) provides an overarching context with a review he initially drafted before we conceived of this collection. He takes a long-term view that contrasts the evolution of data science and information science.…”