If You Can’t Trust Your Data, What Are You Actually Running Your Plant On?

In the past, manufacturing was all about gathering as much data as possible. Companies used historians, MES systems, IoT sensors, and analytics tools to get better visibility and make smarter decisions. The idea was simple: add more sensors, connect everything, and save all the data, hoping it would lead to better results. For a long time, this seemed to work.

However, increasing data volume no longer solves the problem. For many teams, it introduces new challenges. Most organizations now face issues with data trust, not data scarcity.

And that’s a much bigger issue than it sounds.

“Sensors Lie” Became the Default Mindset

If you’ve spent time in a plant, you know sensors can drift out of calibration, signals can drop, and values might spike or flatline unexpectedly. That’s why most analyses begin with checking the data, not with finding insights. A quick analysis often turns into a long process. Teams end up checking signals, validating values, and sometimes even going out into the field to see what’s really happening. What should take an hour can easily take a whole day. This approach may work on a small scale, but it is unsustainable when managing thousands or millions of data points.

One big challenge is that data quality problems usually don’t show up right away. Instead, they show up in other ways. Reports might look wrong, dashboards might not match what’s really happening, and models might give inconsistent results. People often think the problem is with the system or the process, but many times, it’s actually the data that’s at fault.

What makes this even more challenging is how frequently it happens. In many operations, data issues occur regularly, sometimes constantly, but they blend into day-to-day problem-solving. Teams spend time fixing symptoms without realizing they’re dealing with a data integrity issue underneath it all.

The effects aren’t just technical. They lead to wasted engineering time, slower decisions, and missed chances to improve performance.

Why This Is Becoming a Bigger Problem Now

Data quality has been discussed for years, but it’s becoming more urgent for a couple of key reasons. First, companies are now using their data on a much larger scale. AI, advanced analytics, and automated systems work with thousands of data points at once. So, if there’s a problem with the data, it spreads everywhere. If the data is wrong, the results are wrong too.

Second, the workforce is changing. The new generation of engineers and operators relies much more on data. They use dashboards, analytics, and automated insights every day. They don’t check every signal by hand; they trust the data they see. That changes the expectation. Instead of individuals verifying the data, the system itself must ensure it is trustworthy.

What It Takes for Data Quality

Fixing this problem isn’t about adding more dashboards or analytics. It takes a different approach that focuses on making sure the data is high quality before anyone uses it.

At a practical level, that means automatically detecting when something is wrong, whether it’s a sensor drifting out of calibration, a network issue causing data gaps, or an anomaly that doesn’t align with expected behavior. It also means being able to address those issues quickly, either by fixing the root cause or by applying the right data-cleansing approach.

The real challenge is scale. You can’t check data by hand when you have thousands or millions of signals. The only way to keep up is to automate the process so it’s consistent and reliable.

The Real Impact

One of the highest hidden costs of poor data quality is the amount of time teams spend working around it. Data scientists, for example, can spend up to 80% of their time preparing and cleaning data before they ever get to actual analysis.

When that effort is reduced through automation, the impact is immediate. Engineers and analysts can focus on solving problems instead of validating inputs. Decisions can be made faster, with more confidence. And the data itself becomes something teams can rely on rather than constantly question.

At some point, every organization investing in data reaches the same realization: you can’t build reliable analytics, AI models, or operational improvements on top of data you don’t trust. Data quality isn’t just a nice extra you add later. It’s the foundation. Without it, everything you build on top is at risk.

That’s why the conversation is changing. Instead of asking if data quality matters, more teams now want to know how they can make sure their data is trusted throughout their whole operation.

Listen to the full Podcast

We’ve only scratched the surface of the conversation. In the full podcast, we dig into real examples, practical challenges, and how organizations are addressing data integrity at scale.

<strong>Watch The Full Conversation Here</strong>