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The Data’s There. Are You Listening to It?

Michael Lee, MBA
47 minutes ago
3 min read

We’ve all been there.

The meeting is wrapping up. The big decision is looming.

Everyone looks at the slide deck. A few charts, some averages, a bullet point summary.

Someone asks, “Do we know this will work? "And someone replies, “Well… it looks promising.”

In that moment, the choice gets made. Not because the data confirmed it. But because no one wanted to say, “We’re not sure.”

It’s not that the team was careless. It’s that no one felt confident asking the harder question: What does the data really say?

The Danger of Assumptions

We like to believe we’re data-driven. We cite numbers. We show charts. We quote percentages.

But dig just a little deeper, and the cracks appear:

The sample size was tiny.
The data was incomplete.
The correlation was mistaken for causation.
The “average” included two extreme outliers.
The team skipped testing because it felt “too technical.”

And just like that, the decision that looked evidence-based… wasn’t.

Why This Happens (and Why It’s Not Your Fault)

Most professionals aren’t trained in analysis.

They’re given dashboards and expected to know what to trust. They’re handed survey data and asked to draw conclusions. They’re told to “back it up with data”—but never shown how.

And so, many fall back on instinct—gut feel wrapped in a chart.

But instinct alone isn’t enough anymore.The world is moving too fast. The stakes are too high. We need a way to dig deeper, without getting overwhelmed.

What We Mean by “Data Mining”

At FYT Consulting, we don’t mean machine learning or Python scripts when we say “data mining.”

We mean:

Looking beyond surface numbers
Asking questions that clarify, not confuse
Testing assumptions instead of taking them for granted
Exploring patterns without jumping to conclusions

It’s about learning to see what the data might be trying to tell you—and being honest when it’s not saying anything at all.

Where Most People Get Stuck

The fear isn’t unfounded. Words like “t-test,” “regression,” or “statistical significance” have a way of making people feel unqualified—even those who manage millions in budgets or lead entire departments.

But the truth is: you don’t need to be a statistician to analyse data well.

You just need a framework. A common-sense guide. And a space to practice where no one’s judging.

That’s what we designed our course around:

FYTBA02 – Data Mining & Analysis

What It Actually Feels Like to Learn This

We don’t teach formulas first. We start with questions:

How can we be sure this pattern is real?
What might be causing the change we’re seeing?
Is this insight strong enough to act on—or should we test further?
What’s missing from this data set?

From there, we show how to use Microsoft Excel—a tool you already know—to:

📌 Check relationships (correlation, regression)

📌 Test your hunches (t-tests, chi-square)

📌 Understand variability (standard deviation, confidence intervals)

📌 Avoid classic traps like false causality and over-interpreting small samples

You won’t just learn to calculate. You’ll learn to interpret—with clarity, caution, and confidence.

The Real Goal: Better Thinking

Most analysis tools will give you numbers.

What they won’t give you is judgment—the ability to know when your insight is solid, when it’s shaky, and when it’s just noise.

Our course doesn’t promise to make you a data scientist. It promises to help you think like a responsible decision-maker—the kind who can say:

“This result is meaningful.”“This one’s interesting, but not conclusive.”“Let’s test this idea before we scale it.”“We don’t have enough data to call this a success yet.”

That’s leadership.