The whitepaper highlights the shift from cloud-centric Industry 4.0 models to intelligent edge architectures due to challenges in latency, cost, and security as industrial data volumes grow. It emphasizes that successful industrial AI depends on strong data infrastructure at the edge rather than solely on cloud-based algorithms. A key focus is addressing the “Garbage In, Garbage Out” issue by improving data quality through normalization, contextualization, and filtering at the source. By processing data closer to where it is generated, organizations can ensure more accurate, reliable inputs for AI systems. This approach enables better performance, reduced costs, enhanced security, and more effective deployment of AI in industrial environments.

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