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You’ve got spreadsheets tracking everything: downtime, inventory, and complaints. Your dashboards are updated weekly. The reports go out on time. But when a machine breaks or a deadline looms, you still revert to gut feeling.
Sound familiar?
Here’s the uncomfortable truth: most operations managers are drowning in data but starving for insight. We collect numbers like we’re preparing for an audit, but when it’s time to make a decision, we’re still guessing.
The gap between having data and being data-driven is wider than most of us want to admit. The 2024 Wavestone Data and AI Leadership Executive Survey reveals a stark reality. While 82% of organisations are increasing their investment in data and analytics, only 48% report they’ve actually created a data-driven organisation.
Even more telling, just 43% have established a data and analytics culture. The problem isn’t the software you buy; it’s the decisions you make (or don’t make) with the information you already have.
This article shows you how to stop collecting numbers and start using them for data-driven problem solving on your shop floor. No fancy tools required, just a shift in how you approach using data in decision making that leads to real business improvement strategies.
Why We Resist the Data (Even When We Have It)
Why is it so hard to check the spreadsheet before acting?
Daniel Kahneman’s work on decision-making gives us the answer. We operate in two thinking modes. System 1 is fast and intuitive. When the line stops, panic kicks in, and you yell, “Maintenance, get out here!” On the other hand, System 2 is slow and analytical. It pauses to ask, “What does the log say?”
Here’s the thing, your gut isn’t wrong. It’s built on 20 years of seeing patterns. Your instincts are excellent at smelling smoke. However, data is what you can use to specifically locate the fire.
The goal isn’t to replace your experience, it’s to calibrate it with evidence. Being data-driven means building that System 2 pause into your response … especially before you spend money.
Think about it: how many times has your gut been right about where the problem is, but you couldn’t prove why or when it happens?
Finding Your "Herbie" with Data
Eliyahu Goldratt introduced the Theory of Constraints through a simple story. “Herbie” was the slowest kid on a hiking trip who determined how fast the whole group could move. In your operations, Herbie symbolises your bottleneck — the constraint that limits your entire throughput.
You can’t improve output by upgrading fast machines. You can only improve it by fixing Herbie.
Most teams think they know where their bottleneck is. But thinking and proving are different things. Data is your only reliable flashlight to find the real constraint. It isn’t just the one everyone complains about loudest.
A Floor That Thought It Needed New Equipment
Consider this scenario: a production line kept stopping randomly throughout the day, creating scheduling chaos. The maintenance team was constantly firefighting. Management was ready to sign off on expensive equipment upgrades — classic System 1 thinking at work.
Instead of buying new equipment, the ops manager pulled six months of existing maintenance logs and analysed them by machine, time of day, and operator shift. Took about two hours.
The discovery? 70% of all stoppages happened on Machine 3, but only during afternoon shifts. Morning shifts on the same machine? Almost no issues. Other machines? Random, no pattern.
It wasn’t a “bad machine” or an operator problem. A temperature sensor was failing as the factory heated up in the afternoon, triggering false safety shutdowns.
The action? They replaced one sensor and added it to the preventive maintenance checklist instead of replacing the whole machine.
The result? 45% reduction in unplanned downtime over the next quarter. No expensive equipment replacement needed. Maintenance shifted from reactive to preventive. Scheduling became predictable again.
Data didn’t just “report” the problem — it prevented a $50,000+ capital expense and gave the ops manager something concrete to show leadership.
Ask yourself, what patterns are hiding in the data you’re already collecting? Where’s your real Herbie?
The Daily Habit: From "What Happened" to "Why It Happened"
Most teams collect data to prove compliance. “We tracked it” becomes the goal. Reports get filed, dashboards get updated, but nothing changes.
The shift happens when you move from descriptive to diagnostic thinking:
- Before: “Machine 3 broke again.”
- After: “Is there a pattern to when Machine 3 fails?”
The questions you ask change everything:
- From “What happened?” to “Why did it happen?”
- From “What should we do?” to “What does the evidence suggest we try?”
There’s a hidden benefit here: data depersonalises conflict. It stops being “Night shift is lazy” and becomes “Output drops at 3 AM … why?” You’re not pointing fingers; you’re pointing at patterns.
James Clear writes in Atomic Habits that outcomes are a lagging measure of your habits. If you want better operational outcomes, you need better decision-making habits. Data-driven operations aren’t built from one big initiative. They’re built from small, repeated habits.
Start small. Make checking the data your default first step, not your last resort.
The Reality Check
You don’t need AI, a data scientist, or expensive BI tools. The team in our example used Excel and standard maintenance logs they’d been keeping for months.
And let’s be honest: your data probably isn’t pristine. Maybe it’s in three different systems. Maybe some of it is handwritten. Maybe there are gaps.
So, don’t wait for perfect data to find a pattern. 80% accuracy today is better than 100% accuracy next month.
Being data-driven isn’t about sophistication, it’s about consistency. The question isn’t “Do we have the data?” It’s “Are we actually using it before we decide?”
Your Competitive Advantage
Your competitive advantage isn’t the data you collect; it’s how quickly you turn insight into action.
When you build the habit of pausing for evidence, you stop firefighting and start problem-solving. Your gut gets you close. Data gets you precise.
That’s what data-driven really means: using evidence to find your Herbie, fix the root cause, and prevent problems before they cascade.
Your Next Move
This week, pick one recurring problem on your floor. Before you try to fix it, pull the last 30 days of data (messy logs and all).
Ask one question: “Does the evidence support my gut feeling?”
You might be surprised by what you find — and what you save.
Looking to build a culture where data-driven problem solving becomes second nature? At Gagement, we help operations leaders transform their data into actionable insights that drive real business improvement. Let’s talk about what’s possible for your team.
References
Clear, J. (2018). Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones. Avery.
Goldratt, E. M., & Cox, J. (2014). The Goal: A Process of Ongoing Improvement (3rd ed.). North River Press.
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
Wavestone. (2024). The State of Data and AI in Leading Companies 2024: 2024 Data and AI Leadership Executive Survey. Retrieved from http://www.wavestone.com/en/news/2024-data-and-ai-leadership-executive-survey-41/
