Analytics adoptionCulture & Human Readiness27 May 20263 min read
Why Engineers Resist Analytics
This article helps you understand why plant engineers often ignore new dashboards and analytics tools. What looks like resistance is usually experience: a number can be technically correct and operationally wrong. It explains how to involve engineers early, test findings against the shop floor, and make analytics worth trusting rather than asking people to trust it.
Your engineers don't hate analytics.
They hate analytics that doesn't understand the plant.
And I think that's an important distinction.
I've seen organisations spend heavily on dashboards, AI and data platforms...
Only to discover that the people closest to the process barely use them.
The usual conclusion is:
"They're resisting the technology."
I'm not so sure.
Engineers tend to ask different questions.
Does this data reflect what actually happens?
Can I trust the number?
What does this tell me to do differently?
Those aren't signs of resistance.
They're signs of experience.
Because an engineer who has spent years working with a process knows that a number can be technically correct...
and still be operationally wrong.
The best analytics projects understand this.
They:
• Involve engineers before the dashboard is built.
• Let them challenge the data.
• Connect insights to real process problems.
• Test the findings against what's happening on the shop floor.
• Give people a reason to use the system.
The mistake is treating analytics as a technology rollout.
It isn't.
It's a change in how experienced people make decisions.
And experienced engineers aren't going to trust a beautiful dashboard simply because IT says it's accurate.
They'll test it.
Question it.
Compare it with reality.
That's not resistance.
That's quality control.
The goal isn't to make engineers trust analytics.
The goal is to make analytics worthy of their trust.
And that changes the question entirely.
Instead of asking:
"Why aren't our engineers using the analytics?"
Ask:
"What would make the analytics useful enough for them to use?"
That question might uncover a very different problem.
Written by Dipankar Ghosh, Founder, SKYLN Consulting.
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