Dustin Johnson is the CTO at Seeq, responsible for the advanced technology infrastructure, vision and roadmap of Seeq software solutions.
Industrial organizations today are drowning in data yet starving for actionable decisions. Sensor streams, maintenance logs, lab results and operator notes sit in disconnected systems and dashboards, while the practical know-how to interpret them is scattered across people’s heads, reports and emails. As veteran engineers and subject matter experts retire, teams simultaneously struggle to connect siloed, multimodal data into a coherent picture of what happened and why. The result: critical decisions are delayed, second-guessed or never made at all.
The impact is real. Among data and analytics teams already grappling with poor data quality, more than a quarter estimate annual losses above $5 million and 7% put the figure at $25 million or more. At the same time, the sheer volume of data and the lack of trust in it leave many business leaders unable to make timely, confident decisions, exactly when speed and alignment matter most in today’s industrial environment.
The Rise Of Decision Intelligence
Closing this “insight-to-action” gap has given rise to the field of Decision Intelligence (DI). Gartner defines DI as “a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback. By digitizing and modeling decisions as assets, DI bridges the insight-to-action gap to continuously improve decision quality, actions and outcomes.”
Importantly, DI is not just about automation for automation’s sake. It’s about creating an environment where human expertise, AI, analytics, data and organizational knowledge all converge to drive better decisions.
DI in an industrial context means moving beyond hindsight reports to a proactive, integrated decision layer. Industrial decisions are rarely standardizable because each one depends on a unique mix of context, constraints and judgment.
Key Capabilities For Industrial Decision Intelligence
Implementing DI in operations requires a convergence of capabilities. Some key requirements include:
• Contextualization Of Data: Turning raw data into usable information requires operational context.
• Knowledge Capture And Reuse: Tapping into the expertise in people’s heads and collaborative documents.
• Transparency And Trust: In regulated and safety-critical industries especially, transparent reasoning is a must. In the age of AI, it’s more important than ever to establish an inspectable and reliable analytics pipeline.
• Integrated Workflows And Automation: DI platforms tend to unify monitoring diagnostics and case management in one environment. Workflow integration and even automated actions with human oversight are part of the design.
• Adaptability And Low-Code AI: A practical DI layer must be usable by a broad range of decision makers—from engineers and analysts to operations leaders and executives—and adaptable to evolving business and operational needs.
• Unified Data Access: The ability to access and work with disparate data sources in real time—from process historians and IoT sensors to MES, LIMS, CMMS and business systems—without requiring heavy, and ideally no, data migration.
When these elements come together, organizations can drastically shorten the cycle from data to insight to action.
Knowledge Mining
A cornerstone of effective DI is knowledge mining, the ability to weave together what is happening (data) with what we know (knowledge):
• Surface Meaning From Scattered Information: A temperature spike on a production line sensor means little without context. Knowledge mining connects that moment in time to an operator log noting an unusual motor noise, a maintenance ticket filed two days later, a similar event last quarter or a relevant section of the equipment manual.
• Building An Operational Memory: Knowledge mining is about capturing and organizing the hard-won knowledge that lives across assets, teams and time. Subject matter experts play a central role by tagging key events, annotating conditions and linking time-series patterns to known issues, explanations and proven responses.
• Context For AI Agents: AI agents become truly useful when they can retrieve and reason with precise, context-rich information on demand.
• From Alerts To Actions: Knowledge mining also enriches automated monitoring. Traditional systems might fire an alarm on a high temperature. A DI-informed system can go further, flagging alerts with context, likely causes and procedural guidance drawn from past incidents.
In short, knowledge mining ensures that when a condition appears in the data, the context appears alongside it.
Conclusion: From Reactive To Proactive Operations
Industrial organizations that weave together their human expertise, data and AI capabilities stand to gain a formidable edge. By implementing a DI layer, companies can move from reactive firefighting to proactive decision-making. They can reduce the latency from event to action, ensure decisions are based on a 360° view of information and preserve the wisdom of their workforce in a scalable way.
The journey to become a truly insight-driven, agile operation is as much about people and process as technology. It requires breaking down silos and fostering a culture where data and knowledge are shared freely. In an era where uncertainty is the only constant—whether due to supply chain disruptions, market volatility or rapid demand swings—organizations with superior DI will outperform those relying on gut feel or fragmented information.
As Gartner noted, DI is the framework that “elevates decision-making from a hidden liability into a competitive advantage.” By investing in this unified decision layer today, industrial leaders can ensure that when the next challenge or opportunity arises, their teams can act swiftly, wisely and in unison, turning insight into action and action into outcomes.
The future belongs to industrial leaders who treat decisions—not just data—as their most strategic asset. Those who embed DI today won’t just respond faster—they’ll anticipate better, act smarter and preserve institutional wisdom before it walks out the door.
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