Paper Number
ECIS2026-1245
Paper Type
CRP
Abstract
Prior research has shown that data provenance (DP) is an important aspect of data work, data governance, and data infrastructure. Yet its sociotechnical dimensions remain underexplored. As data increasingly shape organizational decisions, often through opaque AI-mediated processes, DP offers a means of making origins, transformations, and interpretations visible. Without understanding how DP is practiced, organizations risk losing sight of how data are produced and used. This paper examines DP as practiced along the upstream-downstream flow of data, drawing on interviews with 18 professionals working in or with banking and finance. We identify practices of documentation, validation, and delegation shaped by material constraints. We theorize the discounting of DP as the diminishing importance of DP while data flow from upstream to downstream contexts. By uncovering its sociotechnical dimensions, we reconceptualize DP as a practice of visibility and knowing, highlighting how it evolves as workflows become more automated.
Recommended Citation
Goswami, Suheli, "Data Provenance In Practice – A Case Study In Banking and Finance Sector" (2026). ECIS 2026 Proceedings. 1.
https://aisel.aisnet.org/ecis2026/datasc_isresearch/datasc_isresearch/1
Data Provenance In Practice – A Case Study In Banking and Finance Sector
Prior research has shown that data provenance (DP) is an important aspect of data work, data governance, and data infrastructure. Yet its sociotechnical dimensions remain underexplored. As data increasingly shape organizational decisions, often through opaque AI-mediated processes, DP offers a means of making origins, transformations, and interpretations visible. Without understanding how DP is practiced, organizations risk losing sight of how data are produced and used. This paper examines DP as practiced along the upstream-downstream flow of data, drawing on interviews with 18 professionals working in or with banking and finance. We identify practices of documentation, validation, and delegation shaped by material constraints. We theorize the discounting of DP as the diminishing importance of DP while data flow from upstream to downstream contexts. By uncovering its sociotechnical dimensions, we reconceptualize DP as a practice of visibility and knowing, highlighting how it evolves as workflows become more automated.
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