Managing quality-oriented big data: a conceptual architecture for acquisition, fusion, and analytics in data-driven organizations

Data-driven organizations increasingly depend on large, heterogeneous, and fast-moving data to sustain the quality of their products, services, and processes. The transition toward data-driven organizational quality, often discussed under the label of Quality 4.0, has so far been examined mainly through a managerial lens. As a result, the data foundations of that transition remain fragmented and under-theorized. This Conceptual Analysis reframes quality-oriented big data as a first-class data management problem and proposes a layered conceptual architecture that organizes the lifecycle of such data across five interconnected layers: multi-source acquisition, integration and fusion, data quality assurance, analytics and knowledge extraction, and data governance and ethics. The central argument is that data quality operates as a central mediating construct linking big data capability to organizational quality outcomes: analytics improves organizational quality in large part to the extent that the underlying data are accurate, complete, consistent, timely, and well governed. Drawing on the data management, data quality, and machine learning systems literatures, the article specifies the constituent elements of each layer, the dependencies among them, and the failure modes that arise when the data foundation is neglected. Five propositions and a structured research agenda position the acquisition, fusion, quality assurance, and governance of quality-oriented big data as priorities for data mining and management research. The architecture offers a data-centric vocabulary for designing and evaluating the data infrastructures on which data-driven organizational quality ultimately depends.