How Data Management Tools Can Help Unpack The Unique Link Between Data And Industrial AI

Heiko Claussen, Forbes Council Member and Chief Technologist at Emerson’s Aspen Technology business, share an article explaining the the evolving role of data and AI in industrial sectors. While traditionally, effective AI relies on high-quality, field-collected data, purpose-built industrial AI—especially in industries like oil, gas, and chemicals—can operate effectively using simulation models and first principles without extensive field data. Nonetheless, access to well-managed, contextualized data remains crucial for deriving actionable insights and optimizing operations. The article highlights the explosion of industrial data, citing estimates of up to 130 zettabytes generated in 2023, yet notes that only a small fraction is actively utilized due to data silos and inconsistent formats across systems. To address this, centralized data management tools are increasingly vital for aggregating, contextualizing, and automating the integration of diverse data sources, thereby enabling faster and more effective use of data. In terms of AI applications, purpose-built industrial AI can tackle operational challenges even without initial field data, relying on models and domain expertise. However, incorporating real-time, contextual data enhances AI accuracy and predictive capabilities—particularly for maintenance and operational optimization—thus bridging the gap between simulation and real-world conditions. Claussen concludes by discussing strategies for transitioning to improved data management systems, emphasizing incremental implementation to maintain business continuity. Overall, well-managed, contextualized data combined with advanced AI tools is key for industrial organizations aiming to boost operational excellence and maintain competitive advantage amid an increasingly complex technological landscape.

 

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