Paper Number
ICIS2025-1546
Paper Type
Complete
Abstract
Data democratization makes data accessible to a wider range of users across an organization, enabling individuals beyond traditional data experts to make informed, data-driven decisions. However, how organizations employ self-service analytics (SSA) to enable democratization remains underexplored. Drawing on work systems theory, we develop a conceptual model that explains how organizations enable data democratization by curating data, modeling data elements, and consuming analytical insights through the SSA work system. We also identify trust in data and trust in analytics as boundary-spanning mechanisms: trust in data strengthens confidence in curated data used during modeling, while trust in analytics enhances confidence in the insights generated, supporting their consumption and informed decision-making. Our study contributes to research on data democratization, trust in IT, and work system theory. From a practical perspective, our findings offer helpful guidance for organizations striving to overcome the obstacles they currently face in democratizing data through SSA.
Recommended Citation
Patabandige, Gayani; Naseer, Humza; Trieu, Van Hau; Cooper, Vanessa; and Black, Stuart, "Enabling Data Democratization through Self- Service Analytics Work Systems" (2025). ICIS 2025 Proceedings. 11.
https://aisel.aisnet.org/icis2025/is_transformwork/is_transformwork/11
Enabling Data Democratization through Self- Service Analytics Work Systems
Data democratization makes data accessible to a wider range of users across an organization, enabling individuals beyond traditional data experts to make informed, data-driven decisions. However, how organizations employ self-service analytics (SSA) to enable democratization remains underexplored. Drawing on work systems theory, we develop a conceptual model that explains how organizations enable data democratization by curating data, modeling data elements, and consuming analytical insights through the SSA work system. We also identify trust in data and trust in analytics as boundary-spanning mechanisms: trust in data strengthens confidence in curated data used during modeling, while trust in analytics enhances confidence in the insights generated, supporting their consumption and informed decision-making. Our study contributes to research on data democratization, trust in IT, and work system theory. From a practical perspective, our findings offer helpful guidance for organizations striving to overcome the obstacles they currently face in democratizing data through SSA.
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