Industrial data integration is the process of gathering, combining, and analyzing data from various sources to create a single, cohesive picture of the data for analysis and decision-making. Many industries, such as manufacturing, energy, transportation, and healthcare, depend on this integration because it helps businesses maximize productivity, cut expenses,and raise quality.
Industrial data can come from various sources, including sensors, machinery, devices, and databases. It can also be of different types, including unstructured, semi-structured, and structured data. Creating a coherent and significant whole out of all these disparate data sources is the challenge of industrial data integration.
Industrial data integration requires the application of expert methodologies and methods. The aim is to build a data infrastructure that can manage massive amounts of data, process it fast and precisely, and deliver real-time insights into industrial process performance.
This paper explains industrial data integration techniques and their challenges. Some case studies reported in the literature draw attention to the insights gained from successful data integration initiatives and use their benefits and best practices.

Auteurs : Lydia Lakhdari , Leila Zemmouchi-Ghomari

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