Unpacking The Link Between Data And Industrial AI
Heiko Claussen is Chief Technologist at Emerson's Aspen Technology business, leading its AI research and technology strategy.
In most conversations, data and AI are inextricably linked. The narrative tends to be that organizations are not using AI well if they don't have quality data from the field feeding into AI models.
While this may be true in many industries and organizational contexts, it's far from universal. In fact, in industrial contexts, purpose-built industrial AI can be highly effective based solely on first principles and simulation models.
But while purpose-built industrial AI designed for industries like oil and gas as well as chemicals may not require data from the field, that's not to say data is unimportant. Ensuring engineers, plant personnel and IT/OT leaders have access to centrally managed, contextual data is critical to deriving data-driven insights across the business.
With the proliferation of modern digital tools and sensors, industries are collecting more data than ever before. As the volume of industrial data continues to grow, data management tools are fast becoming a key technology, helping companies sort through vast pools of data to better understand what information they have—all of which translates to higher levels of operational excellence.
Forward-looking industrial organizations understand the growing data landscape, how well-managed data can improve their business and even the nuanced relationship between data and industrial AI.
For decades, companies have collected data in one form or another, beginning with paper records collected by hand for sensors and integrated systems. What was once a trickle of information has become a deluge in recent years.
Estimates from the World Economic Forum suggest that industries generated as much as 130 zettabytes of data in 2023. Despite collecting huge amounts of data, surveys show relatively little data actually gets used. A Forrester Research study found that companies use just 12% of their data for analysis and less than 30% of companies say they can translate the data into action.
One reason why data is not used as well as it could be can be traced back to the fact that data is often highly siloed. In industrial environments, it's collected and stored across different plants and systems with different formats, tags and protocols, making it difficult to coordinate the use effectively.
One way organizations are mitigating siloes and simplifying the process of aggregating vast amounts of data is through agnostic centralized data management tools with fewer connectors and extractors across various systems. With the ability to be hosted anywhere, ingest data from anything and feed data into anything, such tools help make data available to users when, where and how they need it.
But advanced data management tools' arguably most important capability is adding crucial context to raw data. Context offers companies a more holistic understanding of their data—where it comes from, what sensors and units of measure it represents, when it was collected and more. Today's industrial use cases require not just a stream of binary values, but also a lot of metadata, such as specifications, plans, schedules and work orders. All this data needs to be stored in the right context for it to be useful.
Data contextualization can be automated with an industrial data management tool. Because these tools inherently know the data's location and associated context, industrial organizations can eliminate the challenging, manual task of moving data and applying context afterward.
By eliminating this step, industrial organizations can more quickly achieve efficiencies and fuel digital transformation initiatives that help them overcome increasingly complex technological and environmental challenges.
AI is one example of a digital transformation initiative that has risen to the top of organizations' priority lists. Purpose-built industrial AI solutions can be applied to a host of operational challenges, from monitoring and analyzing emissions to automating mundane tasks to aiding engineers' decision making.
As previously described, much of this can be achieved without field data, thanks to first principles, simulation models and deep industry domain expertise that enables guardrails that keep AI results safely within real-world constraints. In fact, an asset shouldn't be deployed in the field without such an approach. Industrial organizations eager to begin reaping the benefits of AI often don't have the luxury of waiting as field data is collected over time before training a model and controlling the asset.
Despite industrial AI's effectiveness as is, asset data can help take AI results to the next level. By refining an AI model with in-context operating data from an industrial data management platform, organizations are beginning to close the simulation-reality gap and help predict future outcomes based on past observations. For instance, AI applications for predictive maintenance can be improved by identifying asset abnormalities and building models based on both normal and abnormal operations.
The powerful combination of industrial AI and the right data from the field can effectively supercharge companies' operational excellence initiatives, making it easier to create and sustain meaningful AI models that give companies a significant competitive edge. Whether it's helping to optimize existing processes, identifying ways to increase efficiency or informing the design of new processes, there should be little doubt that data is—and will continue to be—a critical resource for industry.
While data's benefits for AI and other business improvements may be known, the question that lies ahead is how companies will make the transition to more effective industrial data management tools. Replacing a web of siloed, global data connections that were likely set up by different personnel years earlier can feel overwhelming. Among other challenges, companies must maintain business continuity and avoid disrupting established processes that require traceable product information, test results and more.
By initially configuring a new data management tool as a real-time backup or redundant control system alongside current connectivity architecture, then removing previous connectivity incrementally after a validation period, industrial organizations can safely implement centralized data management tools. Ultimately, this approach is faster, easier and more secure than setting up many peer-to-peer connections
With the right data management tool and strategy in place, organizations can effectively use their data, enhance AI applications and fuel ever-more advanced use cases to help them remain ahead in an uncertain macro-environment.
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