Why Most IoT Data Is Useless—And What Manufacturers Can Do About It

Why Most IoT Data Is Useless—And What Manufacturers Can Do About It

Without high-quality asset master data, manufacturers risk wasting 90% of their IoT insights. Here’s how to change that.

Manufacturers are racing to implement AI, digital twins, and predictive analytics—but most are leaving value on the table. According to IBM, 90% of industrial data collected goes unused. That’s not a tech failure. It’s a data foundation failure.

During the session “Revolutionizing Asset Management” at IIoT World Manufacturing Day 2025, experts from Prometheus Group and IBM made one point clear: if you don’t fix your asset master data, you’re just adding sensors to noise.

Garbage In, Garbage Out—Still Applies

Asset master data defines what each piece of equipment is, what it does, and how it’s maintained. Without accurate metadata—say, that a given sensor is attached to a centrifugal pump measuring pressure—AI models can’t contextualize incoming data. That means alerts are wrong, predictions are off, and dashboards become misleading.

Even worse, when equipment is replaced or modified, and asset metadata isn’t updated, the error compounds. Data flows, but it’s disconnected from reality.

The Data You Already Have Is Enough—If It’s Structured

Most manufacturers already collect vast amounts of sensor data. But only 20% of it is used in analytics. The barrier isn’t hardware. It’s semantic alignment.

Fixing this starts with prioritizing critical equipment and verifying the data structures tied to those assets. A clean data model enables predictive maintenance, energy optimization, and simulation—all without requiring new sensor deployments.

Clean It Once, Keep It Clean

Prometheus Group introduced a methodology called “Get It Clean, Keep It Clean.” The idea: asset data cleanup shouldn’t be a one-off project. Instead, it should be managed as a service—constantly updated as equipment changes, processes evolve, and AI models improve.

This kind of “Master Data as a Service” (MDaaS) approach ensures that frontline data remains aligned with operational reality, enabling strategic decisions based on truth—not assumptions.

The Hidden Cost of Poor Data

Outdated or incorrect master data doesn’t just impact analytics—it drives up maintenance costs, extends downtime, and undermines safety. For example, mislabeling equipment in your CMMS or ERP can delay repairs, send technicians with the wrong parts, or fail to catch patterns that would otherwise indicate failure.

Digital Transformation Starts at the Asset Level

For organizations investing in digital twins, generative AI, and autonomous systems, foundational data quality is not optional. You can’t model what you don’t define.

The takeaway? Before launching another AI initiative or deploying another IoT pilot, take a hard look at your asset master data. It might be the most strategic decision you make this year.

Source: Insights from the session “Revolutionizing Asset Management: Harnessing Asset Master Data, IoT, and AI for Strategic Decision-Making,” presented during IIoT World Manufacturing & Supply Chain Day 2024.

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