Big Data

Big Data refers to datasets so large, fast-moving or varied that traditional tools cannot practically store, process or analyse them, requiring specialised technologies and techniques. It is often characterised by the “three Vs”: Volume (huge amounts of data), Velocity (generated and needing processing at high speed) and Variety (many types — structured records, text, images, sensor streams) — sometimes extended with Veracity and Value.

Logistics and shipping generate enormous data: shipment and transaction records, GPS and AIS tracking, IoT sensor streams from cargo and machinery, port and customs data, weather, and more. Harnessing this big data — with cloud storage and processing, and analytics and machine learning — unlocks visibility, forecasting, route and network optimisation, predictive maintenance, demand planning and risk detection. Big data is the fuel for AI and advanced analytics in supply chains: models are only as good as the data they learn from, and the scale and richness of logistics data is precisely what makes powerful prediction and optimisation possible. Big data (stored and processed in the cloud, gathered increasingly via IoT) is a foundational concept behind data-driven, intelligent logistics, and closely tied to machine learning and analytics. It represents the raw material of modern supply-chain intelligence.