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Making Sense of Data

Statistical Problem Solving - Data Integrity - Data Literacy

Cut through noise and misinformation, and reveal what the data is really saying.

How data is easily misused

"50% MORE INTENSIVE CARE PATIENTS"

I heard that headline in the media at the start of the coronavirus crisis.
An impressive figure: +50%. It sounded urgent, alarming.
But if you looked beyond the percentage, you saw a completely different story:
the absolute number had risen from 2 to… 3 patients.

An increase of 50% – statistically correct, yes. But in absolute terms? Only one more patient.
Still an important patient, but the impact was far less spectacular than the headline suggested.

The key issue: misusing or misrepresenting data quickly gives a distorted picture of reality.

As in this case, percentages on small numbers. This can completely frame reality.
An increase from 100 to 101 represents exactly the same absolute difference (one patient),
but seems almost insignificant with its 1% increase. And yet there are many more people in intensive care (101 versus 3).

Data seems to speak for itself, but does it always tell the truth?

Recognise these challenges?

In today’s data-driven world, not all data are created equal.
These examples of data pitfalls often lead to flawed conclusions and poor decision-making.

Misleading graphs

Charts can mislead when context is missing.

By cherry-picking timeframes, exaggerating small differences, or omitting data, charts can create stories the data never supported. When charts fail to show the full picture, they can mislead, sometimes with serious consequences.

Your major gain:
Avoiding teams going the wrong direction.

Opinions without facts

Opinions have value, but problem-solving needs facts.

Think of the hours lost chasing a “root cause” for a critical customer complaint, guided by assumptions and past experience. A systematic collection of objective data could have revealed the true mechanism, lowering the risk of customer-impacting failures.

Your major gain:
Far lower risk of lost sales.

Overconfident claims

Validity comes from study design, not confidence.

Many findings presented as scientifically proven fail to reproduce, as repeatedly observed in conflicting research. Statistical rigor is essential: it reduces false certainty, exposes flawed study design, and prevents conclusions drawn from unreliable data.

Your major gain:
Increased confidence in decision-making.

Acting on noise

Treating common cause variation as a signal.

Responding to natural variation leads to unnecessary adjustments and constant firefighting. But if you know exactly when a signal is real, things change: fewer false alarms, less wasted time and money, and far less operational frustration.

Your major gain:
Saving you tremendous amounts of time.

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How I can help

I help professionals and organisations understand what their data actually say, turning data confusion into clear insights.

Statistical Problem Solving

From recurring problems to lasting solutions.

Applying the practice of statistics with a problem-solving roadmap, so you act on facts and not on false patterns.

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Data Integrity Review

From false certainty to trustworthy decisions.

Applying critical review to data and underlying analysis and findings, so you avoid false certainty and misinformation.

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Masterclasses

From statistical confusion to informed judgement.

Strengthening data literacy, so you improve decision-making and hence performance.

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Client Feedback

“Luc combines expertise with a pragmatic mindset, making him a reliable partner for organisations seeking structure, clarity, and improvement.

I can highly recommend his services.”

Filip Waem— Operations Manager, Industrial Manufacturing

Masterclass
'Understanding Variation'

In just 1 day I will help you understand and use variation, making you distinguish noise from real signals. Stop wasting time, and know when to make confident data decisions.

September 2026

Recent insights

Read here about my insights on how data can be helpful, but when misused or misinterpreted can be very harmful – for business, society and science.

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Luc Poelman

Founder of FactWise

Throughout my career as a Management Consultant and Six Sigma Black Belt one thing became impossible to ignore: data are often used in ways that don’t reflect reality. Think of misinterpreting scorecards, acting on natural variation, misleading graphs, and so on.

Recently, a question about health claims led me into the scientific literature, where I ran into the same issues. Health claims in the media, reports, even peer-reviewed studies — too often conclusions are built on noise, flawed assumptions, or misleading statistics.

The result: massive waste of time and money, frustrated teams, reputation damage and even safety issues.

That realisation shaped my mission:

cut through noise and misinformation,

and reveal what the facts and data are really saying.

Today, I help professionals and organisations review unresolved questions, recurring problems and doubtful claims — clarifying what really happened, what the data support (and what not), and where conclusions exceed the evidence.

Curious to know more?
Let’s connect for a free call.

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