Engineers, analysts, and data teams still spend anywhere from 35% to over 60% of their time hunting through siloed systems just to find what they need to do their jobs. Why? Data is everywhere, but rarely connected. Naming conventions are inconsistent, asset hierarchies do not match, and data structures vary widely. This fragmentation is compounded by the inherent complexity of industrial data, which spans a broad range of structured and unstructured types with many structured and unstructured data types such as time series, events, drawings, documents, images, and more.
The result? Lost time. Missed insight. Stalled POCs. And now, on top of these long-standing challenges, there's growing pressure to adopt generative AI, and it's moving fast. What started as a hype will become an operational reality, offering new ways to optimize workflows, reduce downtime, and improve decision-making. The question isn't if AI will be part of your operations, but whether your data and your approach are ready to support this change.
Contextualization is the key to rapidly delivering AI at scale.
What is Contextualization, really?
Let's keep it simple:
contextualization (noun)
Contextualization is the continuous process of identifying and connecting different pieces of information to show how they relate to each other in the real world.
In the industrial setting, contextualization is the continuous process of linking and organizing industrial data so it reflects the relationships between equipment, systems, and workflows as they exist on the factory floor, oil rig, refinery, and beyond.
Imagine an engineer trying to troubleshoot a failed valve. Without contextualization, they might look at a pressure gauge, check a paper diagram, hunt down the equipment tag, cross-reference that with the maintenance log, and hope they can decipher the many naming conventions across all of these different sources to find the relevant information. This becomes a standalone activity, so the next time a similar event occurs, the same process must be repeated. Contextualization overcomes this challenge by connecting different systems and creating a unified industrial data foundation.
Historically, this contextualization process has been time-consuming and difficult to scale. Data engineers manually defined the ingestion of this data with data pipelines and models that must be continually updated. But that approach is shifting.
More and more industrial organizations understand that to remain competitive, profitable, and sustainable, they must move beyond human and manual contextualization. For example, one of Cognite's pre-built tools, technicians, maintenance workers, and other frontline teams can now share invaluable data from the field in real time. So if an engineer discovers the root cause of a recurring pressure spike linked to a nearby equipment or any other observation, they can easily log findings back into the tool, automatically enriching your industrial data foundation.
This approach of continuous contextualization - an ongoing, evolving effort, and not a one-time task.
"The contextualization of the future is dynamic and democratized. Field operators and maintenance teams will contribute insights in real time, improving the intelligence of your operations day by day." Chirayu Shah, Cognite’s Chief Product Officer.
Continuous contextualization moves you from a siloed way of operating, allowing you to solve, scale, and streamline complex use cases like turnaround planning and execution, troubleshooting, field operations, and more. It builds the foundation that not only makes day-to-day tasks easier but also brings digital transformation to life on the ground and unlocks the true power of Industrial AI.
Ready to learn more about the power of contextualization, Cognite's approach to industrial data and AI?
- Jump to the next section, where we take a deep dive on how contextualization empowers industrial AI to move from isolated pilots to real, scalable impact across your operations.
- Watch our latest in-depth product tour showcasing contextualization services and AI agents supported by powerful customer testimonials.
- See Cognite in action.