Twenty years into the IoT era, we’re finally seeing the promise of connected devices come to fruition, but not in the way we originally imagined. We are discovering that connectivity alone was never enough. What’s emerging now is something more powerful: an industrial data operations platform that transforms raw device data into contextualized insights that drive real business outcomes.
From connectivity to context: Why classical IoT fell short
The early vision for IoT was compelling: connect devices, provide unprecedented insights, and transform industries. Yet despite significant investment, the expected value never fully materialized. Research studies across the industry tell the consistent story that we collected vast amounts of data but struggled to turn it into meaningful action.
The problem wasn’t the technology itself, but what we failed to build around it. Data without context is just noise. A temperature reading from a power system means something entirely different than the same reading from a building HVAC system. When we collected data from devices across different domains such as power, buildings, or manufacturing, we stored it in isolated, domain-specific schemas that couldn’t talk to each other.
This is where the concept of industrial data ops platforms becomes critical. Rather than simply moving data from devices to the cloud, these platforms focus on enriching data with context, ensuring quality, and making it accessible to the people and AI systems that need it. The shift represents a fundamental evolution in thinking: from “how do we connect devices?” to “how do we serve contextual data to drive decisions?”
At Schneider Electric, we’ve experienced this challenge firsthand with EcoStruxure. After 15 years of collecting data from millions of connected devices, we realized that unifying this information across power meters to room controllers required more than just connectivity infrastructure. It required a deliberate strategy to contextualize data using multi-dimensional relationships, not forcing everything into a single rigid model, but making sure different domains can work together seamlessly.
Breaking down silos: Unifying IT, OT, and sustainability data
Today’s enterprises need to view their operations holistically. Energy consumption isn’t just a facilities problem. It spans manufacturing equipment, building systems, vehicle fleets, and supply chain partners. Understanding total energy footprint and optimizing across these domains requires breaking down traditional silos between IT and operational technology.
Modern platforms must allow organizations to pull data from primary vendors alongside third-party systems, creating a comprehensive view that transcends individual domains. This isn’t about creating one universal data model, as attempts at that have consistently failed. Instead, it’s about intelligent data harmonization that preserves domain-specific context while enabling cross-functional insights.
Consider predictive maintenance as an example. In the past, companies optimized individual assets within a single plant using local data. Today, they’re optimizing entire fleets across global locations by aggregating and analyzing data from all sites simultaneously. This reveals which facilities operate most efficiently and enables strategic decisions about resource allocation that would be impossible with siloed data.
This capability is particularly crucial as data regulations evolve. Requirements like the EU Data Act and GDPR create data sovereignty obligations that affect how information moves across borders and regions. Industrial data ops platforms must handle these compliance requirements automatically, ensuring data stays in appropriate jurisdictions while still enabling the analytics and insights that drive business value.
The convergence of unified data platforms with AI is what’s driving the dramatic revenue growth we’re seeing in hyperscalers. Companies increasingly recognize that AI’s potential is only useful when it has access to rich, contextualized data. The data backbone isn’t just supporting current operations, it’s feeding the next generation of intelligent systems.
Intelligence at the edge: Bringing insights closer to action
One of IoT’s unfulfilled promises was real-time intelligence anywhere it’s needed, whether that’s on oil platforms, factory floors, or wind farms. The assumption was that all processing would happen in the cloud, with insights flowing back down to operators. Reality has proven more nuanced.
The computational power available at the edge has grown dramatically. We can now train sophisticated models in the cloud using vast historical datasets, then deploy optimized versions directly onto edge devices. At Schneider Electric, we’ve demonstrated this by deploying small language models onto switchgear, enabling closed-loop control with AI running directly on the equipment.
This creates a mesh architecture where analytics happen wherever they make the most sense—some in the cloud for fleet-wide optimization, some at the edge for real-time control and low-latency response. The benefits are significant: reduced latency for time-critical decisions, continued operation during connectivity interruptions, and the ability to meet regulatory requirements around data locality.
Software-defined operations are making this possible by replacing traditional electromechanical systems with compute-enabled alternatives. As we build more intelligence into edge devices, we’re able to do work that previously required cloud connectivity directly on the equipment itself. This is particularly critical for remote installations or scenarios where connectivity is unreliable. A hurricane shouldn’t shut down critical operations because cloud services are temporarily unavailable.
The intelligent edge is not about abandoning cloud computing, but rather creating resilient, responsive systems that leverage both edge and cloud capabilities appropriately. Different situations call for different approaches, and modern platforms provide the flexibility to deploy intelligence where it delivers the most value.
How to assess the best path forward
As industries continue to digitize, the real competitive advantage lies not in simply connecting assets, but in orchestrating data that’s intelligent, contextual, and actionable. The digital backbone of tomorrow is already taking shape—bridging IT, OT, and AI to create systems that are as adaptive as they are efficient.
To move forward, organizations should start by assessing their data landscape—identifying where silos still exist between IT, OT, and sustainability data, and determining where harmonization could unlock deeper insights. From there, piloting edge intelligence in a focused use case can reveal tangible benefits, such as reduced latency, improved reliability, or smarter local decision-making. By taking these steps now, businesses can begin building a digital backbone that’s ready for the AI-driven future.
Add a comment