Data-driven sensegiving and sensemaking: a phenomenological investigation

Purpose Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools. Design/methodology/approach This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies. Findings This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles. Originality/value This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

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Publication Details

Journal
Information Technology and People
Published
2024-08-29
DOI
https://doi.org/10.1108/itp-05-2023-0452
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Data-driven sensegiving and sensemaking: a phenomenological investigation

Claris Yee Seung Chung, Ghiyoung Im, Jiaoyan Li, Morteza Namvar
Information Technology and People
Big Data and Business Intelligence
article

Data-driven sensegiving and sensemaking: a phenomenological investigation

Claris Yee Seung Chung, Ghiyoung Im, Jiaoyan Li, Morteza Namvar
article en

Abstract

Purpose Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools. Design/methodology/approach This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies. Findings This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles. Originality/value This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

Information Technology and PeopleVol. 38(7)
University of Louisville (US), The University of Queensland (AU), University of Canterbury (NZ)
Openalex Percentile: Top 79%
Big Data and Business Intelligence
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