DATA HARVESTING AND DATA HARNESSING MODEL
(DH2 MODEL)

In an increasingly data-driven industrial landscape, the ability to convert raw, disparate data into actionable insights has become a cornerstone of competitive advantage. This research introduces the DH² Model – a novel, integrated framework for Data Harvesting and Data Harnessing – designed to bridge the gap between data acquisition and value realization, particularly within manufacturing and smart industry contexts. Grounded in the Data Lifecycle Theory, Resource-Based View, and Knowledge Management principles, the model proposes a structured, dual-phased approach: (i) Data Harvesting, which emphasizes multi-source data collection, preprocessing, and contextual filtration; and (ii) Data Harnessing, which involves storage architecture, advanced analytics, and decision-centric applications. A key feature of the DH² Model is its feedback-driven, socio-technical design that aligns machine intelligence with human interpretation to drive continuous improvement. Technological implementation includes IoT integration, AI-based analytics, and ethical data governance. The model’s validation through industrial case studies demonstrates its potential to reduce operational inefficiencies, enhance predictive maintenance, and foster innovation. The DH² Model thus offers a scalable blueprint for organizations seeking to transition from data-rich to insight-driven operations.