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Scalable Textile Business Intelligence With Operational Data Collection and Analytical Processing

Scalable Textile Business Intelligence With Operational Data Collection and Analytical Processing
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Author(s): Kamalendu Pal (University of London, UK)
Copyright: 2024
Pages: 20
Source title: Data-Driven Business Intelligence Systems for Socio-Technical Organizations
Source Author(s)/Editor(s): Pantea Keikhosrokiani (University of Oulu, Finland)
DOI: 10.4018/979-8-3693-1210-0.ch004

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Abstract

Many textile companies have come to the realization that data is crucial for improving sales and revenue margins. Clothing brands and retailers must develop manufacturing and sales styles that appeal to customers. In recent decades, with advancements in various categories of data analytics and artificial intelligence techniques (e.g., machine learning), the value of data-driven applications has been well acknowledged by textile clothing retailers. They use predictive software outputs for regular operational decision-making. This chapter reviews retail businesses and their products' manufacturing data analytics. It presents a scalable business intelligence framework using a graph data model and its management system. The chapter also highlights that big data technologies and related supporting resources (e.g., the Internet of Things) enable real-time data capture, storage, processing, and sharing. This helps businesses make operational decisions faster and more effectively. This chapter presents an algorithm for extracting knowledge from stored business data to exemplify the analytical value of the graph database model for business intelligence.

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