FactWise investigates the data, statistical reasoning and evidence behind important claims and conclusions — to clarify what holds up, what does not, and what remains uncertain.
That headline ran at the start of the coronavirus crisis.
Urgent. Alarming. A 50% surge in ICU patients.
But look beyond the percentage: the absolute number had risen from 2 to 3 patients. Statistically correct the 50% math, yet the real-world impact was one additional person in intensive care.
This is how percentages on small numbers distort reality. An increase from 100 to 101 carries the same absolute difference, but reads as a modest 1%. While 2 to 3 reads as a dramatic 50%. Same math. Opposite impression.
The core problem: misrepresenting data, even unintentionally, distorts our picture of reality.
Numbers are neutral. The interpretation rarely is.
Flawed data, weak analysis and questionable reasoning don’t just lead to wrong conclusions.
They lead to poor decisions, wasted resources, and misplaced confidence.
Context is everything.
Cherry-picked timeframes, truncated axes, omitted baselines — charts can tell stories the underlying data never intended. When visuals drive decisions, the framing matters as much as the numbers.
What this costs you:
Teams mobilised around a problem that didn’t exist — or missed one that did.
Consensus is not evidence.
When experience and assumption fill the gap that data should occupy, decisions feel confident but aren’t grounded. The root cause stays hidden. The same problems keep returning.
What this costs you:
Recurring issues, misdiagnosed causes, and solutions that don’t stick.
Confidence isn’t validity.
Many findings presented as proven fail under scrutiny. Flawed study design, selective reporting, and statistical overreach are common, and rarely visible to the non-specialist eye.
What this costs you:
Decisions built on research that doesn’t hold up. Legally, commercially, or scientifically.
Not all variation is a signal.
Reacting to routine variation as if it were a meaningful signal creates unnecessary interventions, constant firefighting, and a team that loses trust in the data it’s supposed to rely on.
What this costs you:
Wasted time, eroded trust, and an organisation in permanent reaction mode.
When an important problem remains unresolved or a conclusion needs to hold up, FactWise provides an independent examination of the underlying data, statistical thinking, reasoning and evidence.
FactWise looks beyond the headline number:
What do the data actually show?
Is the apparent change a real signal or routine variation?
Are the comparisons and statistical reasoning sound?
Are important assumptions or alternative explanations being overlooked?
Does the evidence genuinely support the conclusion?
The objective is not more analysis for its own sake.
“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
“During our collaboration at Bekaert, Luc consistently demonstrated strong analytical skills, turning complex data into clear insights and enabling well-founded, objective decision-making.”
Patrick De Keyzer— Former SVP Technology Bekaert Steelcord
FactWise masterclasses provide practical training in statistical thinking, variation, data interpretation and evidence-based problem solving.
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.
Stop chasing ghosts – Understanding routine variation in business metrics
Ready to take action? Make sure to book your free 30-minute discovery call today.
More than 25 years in quality management, operational excellence and Lean Six Sigma taught me one recurring lesson: having data does not automatically lead to accurate insights.
I repeatedly saw how easily data can be underused, misread or overinterpreted.
Reacting to natural process variation. Graphs that tell a story the numbers don’t support. Misread scorecards.
When a personal question led me deeper into the scientific literature on health claims, I encountered the same challenge: conflicting findings, results that did not always replicate, and statistical reasoning that deserved closer scrutiny.
That’s why I founded FactWise — to cut through noise and misinformation, and uncover what the facts and data really say.
I am particularly interested in unresolved questions, issues where the pieces do not form a convincing whole, and claims or conclusions that need to be trusted — or challenged.
Ready to turn data confusion into clear insights? Share your challenge and let’s find a solution together.