Uncovering synergies in a fragmented data landscape: A data mapping framework for robust data lineage and dismantling data silos at Atlas Copco

Data often flows through disconnected systems and departments in large organizations, making it difficult to trace its origin, transformations, and ownership. This fragmentation leads to duplicated efforts, inconsistent reporting, and limited confidence in analytics. These challenges became increasingly apparent within the Purchase-to-Pay (P2P) process of a global industrial firm migrating from legacy platforms to a consolidated data lake architecture. This thesis investigated whether a data mapping approach can capture and maintain reliable data lineage, as well as dismantle data silos in a fragmented data landscape. Guided by the research question, “How can a standardized data mapping approach be employed to capture and maintain reliable data lineage across Atlas Copco’s heterogeneous systems?”, the thesis followed a Design Science Research (DSR) methodology. Through 24 stakeholder interviews, site observations, analysis of organizational documents, and prior literature on similar artifacts, a visual framework was developed to map end-to-end data flows. Triangulating these sources enabled the developed artifact to identify lineage breaks, highlight data silos, clarify ownership, and quality controls. Applied in a real operational setting, the artifact revealed hidden inefficiencies, reduced manual reconciliation, and improved alignment between business processes and technical systems. The thesis concluded that making data lineage and silos visible and actionable in complex organizational settings can improve operational coherence and contextualization. It provides a repeatable blueprint with for large firms pursuing data-centric transformation.