Paper Number
ECIS2026-1538
Paper Type
CRP
Abstract
Self-service analytics (SSA) is beneficial for organizations because it empowers business users to access, analyze, and make sense of data independently without relying on IT or data specialists. In this study, building based on expert interviews with 21 SSA practitioners, we develop a process model that explains how organizations enable business users to engage in three interrelated self-service sensemaking processes (exploring, employing, and embedding) when interacting with data and analytics for business use cases. The model shows that sensemaking resources enable these processes, while sensegiving practices shape them, and that their interplay mutually reinforces how they function together, supporting business users in assigning meaning to data cues and making data-informed decisions. By extending sensemaking theory into the SSA context, this study advances understanding of user-level analytics capability development and provides practical guidance for enabling SSA initiatives that support data-informed decision-making in organizations.
Recommended Citation
Patabandige, Gayani; Naseer, Humza; Trieu, Van Hau; Cooper, Vanessa; and Black, Stuart, "Enabling Self-Service Sensemaking For Data-Informed Decision Making: An Empirical Examination" (2026). ECIS 2026 Proceedings. 5.
https://aisel.aisnet.org/ecis2026/datasc_isresearch/datasc_isresearch/5
Enabling Self-Service Sensemaking For Data-Informed Decision Making: An Empirical Examination
Self-service analytics (SSA) is beneficial for organizations because it empowers business users to access, analyze, and make sense of data independently without relying on IT or data specialists. In this study, building based on expert interviews with 21 SSA practitioners, we develop a process model that explains how organizations enable business users to engage in three interrelated self-service sensemaking processes (exploring, employing, and embedding) when interacting with data and analytics for business use cases. The model shows that sensemaking resources enable these processes, while sensegiving practices shape them, and that their interplay mutually reinforces how they function together, supporting business users in assigning meaning to data cues and making data-informed decisions. By extending sensemaking theory into the SSA context, this study advances understanding of user-level analytics capability development and provides practical guidance for enabling SSA initiatives that support data-informed decision-making in organizations.