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How to fix three data management stumbling blocks

All industrial businesses are eager to turn their data into value and most have a digital transformation project underway. But according to a global survey of CIOs by Gartner Inc, fewer than half meet their targets. Here's how to avoid the three most common stumbling blocks.

How to unlock the full value of linking your IT and OT

All industrial businesses are eager to turn their data into value and most have a digital transformation project underway.

But, according to a global survey of CIOs by Gartner Inc, fewer than half meet their targets¹.

Connecting the dots between IT (Information Technology) and OT (Operational Technology) is fundamental.

With your eyes on the horizon, here's how to avoid the three most common stumbling blocks that derail organizations' ability to unlock the full value of linking their operational and business systems.

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Adam Cartwright

Stumbling block 1: an unclear destination

AI often becomes a hammer looking for a nail. AI cannot compensate for poorly defined objectives. Don't let the clever responses of large language models make you think that AI understands what you should do or that other AI technologies will then make that happen.

If you don't understand what success looks like and how it will be measured, then you will fail. Use the same rigor you would apply to specifying a consultant to defining your AI project.

The fix: Every digital or AI project must be a change project. Bring your people with you and help them be excited to bring all their knowledge into the project so they help to make it a success and integrate into your business as usual.

Stumbling block 2: don't be scared to break the silo

Data silos are the legacy of decades of disconnected systems — hardware and software added over time or managed by separate teams without a unifying digital thread. Machines might still run smoothly, but their data sits locked in standalone systems that can't speak to each other. Legacy systems and protocols are a particular issue; IT teams want OT data to 'be like an API' and it is not.

This fragmentation is a major obstacle to gaining meaningful, real-time insights that can be acted on by the organizations' existing systems.

The fix: Industrial IoT platforms like Siemens's Industrial Edge help unify these environments by creating a single interface for data sharing that can plug into OT. They connect sensors, machines and legacy infrastructure to the cloud — giving the standardized interfaces IT are looking for.

Stumbling block 3: not labelling data correctly

Raw data without context is just noise. To derive insight, that data must be properly labeled with metadata like time stamps, source descriptions or asset identifiers.

But manual labeling is labor-intensive, and many companies lack the internal resources to do it at scale.

The fix: Label as you go. This must be a discipline and needs to be baked into your standard operating procedures. If you have huge legacy datasets, AI-powered tools like Siemens SALT (Semi-Automatic Labelling Tool) are designed to take on this challenge.

Don't let your data go to waste

True digitalization depends on bringing together OT and IT — getting machines and systems to speak the same language. But it also depends on smart data practices: tearing down silos, labeling data correctly, and applying AI with purpose and care.

By avoiding these three data management stumbling blocks, industrial businesses can stop missing out on the value already hiding in their operations and start making their data truly work for them.