SUPPLY CHAIN TECHNOLOGY
HOW BIG DATA ANALYTICS WORKS
Microsoft outlines a four-step process for the successful analysis of big data:
• Collect: Data is gathered from a range of sources across web, mobile and cloud platforms. It may take the form of structured, semi-structured or unstructured data. Once captured, it is stored in a central repository, such as a data lake or data warehouse, ready for processing.
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• Process: At this stage, the stored data is verified, sorted and filtered. These steps prepare it for further use while also enhancing the efficiency and accuracy of subsequent queries.
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• Scrub: After processing, the data is cleansed. Errors such as duplicates, inconsistencies, invalid or missing values and formatting issues are
identified and corrected to ensure accuracy and reliability.
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• Analyse: At this point, the data is ready for analysis. Data mining, AI, predictive analytics, machine learning and statistical modelling are applied to uncover insights, define patterns and predict behaviours.
Navigating integration challenges Despite its transformative potential, implementing big data solutions in supply chains presents significant challenges. The primary obstacle stems from siloed systems with inconsistent data definitions across organisations.
Arun highlights a common scenario:“ Organisations may call the same piece of data a‘ part number’ in manufacturing,‘ SKU’ in sales and‘ item code’ elsewhere, requiring extensive data harmonisation.”
The challenge is exacerbated as enterprises continuously add new systems faster than integration pipelines
184 October 2025