SCALING DATA PRACTICES IN MULTINATIONAL FIRMS: ESSAYS ON DATA GOVERNANCE AND DATA DEMOCRATIZATION

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Serval ID
serval:BIB_95C1F4BCF791
Type
PhD thesis: a PhD thesis.
Collection
Publications
Institution
Title
SCALING DATA PRACTICES IN MULTINATIONAL FIRMS: ESSAYS ON DATA GOVERNANCE AND DATA DEMOCRATIZATION
Author(s)
LEFEBVRE Hippolyte Antoine Marie
Director(s)
Legner Christine
Institution details
Université de Lausanne, Faculté des hautes études commerciales
Publication state
Accepted
Issued date
2024
Language
english
Abstract
In recent decades, the perception of data within corporations has evolved significantly, elevating from a mere operational resource to a strategic asset pivotal for value creation. However, despite some progress, industry reports show that many firms still struggle with scaling the necessary data practices for using data effectively, which hampers their ability to achieve data-driven innovation. This challenge often arises because data practices are not sufficiently developed beyond data experts, limiting the ability of a broader range of employees to competently engage with data in their daily work. Research has yet to explain how companies develop data practices among a widening audience — a capability denoted as democratization — especially how employees can effectively integrate data with domain expertise through context-specific data practices. Simultaneously, the role of data governance, traditionally viewed as a control function overemphasizing data protection, needs to evolve into a coordinating role that facilitates data practices to realize data-driven innovation. Therefore, this thesis elucidates how data democratization and data governance co-evolve toward strategic value creation from data, through two interrelated streams of research. Through three essays, the first research stream grounds data democratization in Information Systems (IS) research, identifying it as a capability rooted in practice and highlighting its socio-technical nature. We emphasize the critical necessity of integrating both generic and situated data practices to achieve true data democratization. We illustrate how these data practices are cultivated through situated learning and practice exchange. Through two essays, the second research stream explains how to govern data effectively to achieve both control and innovation. We introduce systems thinking to position data governance at the intersection of data strategy and data operations within a Viable System Model. We describe the reconfiguration of data governance into archetypes that reflect the evolving strategic role of data. Altogether, our findings significantly advance data management research by providing a clearer understanding of how to scale data practices through the interplay between data democratization and data governance, highlighting their synergistic efforts in driving value creation from data.
Create date
02/10/2024 9:50
Last modification date
15/10/2024 7:57
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