Why you can’t optimize a candle into a lightbulb
Expecting data to drive innovation is like trusting a speedometer to tell you where to go on vacation.
––
The Data Trap: Why Volume Doesn’t Equal Value
A data-driven CEO feels smart, modern, and sophisticated. It feels like your decisions are future-proof.
You feel like a seasoned pilot in a cockpit full of sophisticated instruments.
But here is the truth: data helps you optimize what you already have, not find something new. It creates an illusion of being ‘right.’ But to make a real quantum leap, you often need to make a counter-intuitive, seemingly ‘wrong’ decision.
I once visited a client and noticed a massive screen in the conference room. Numbers were shifting in real-time, charts were flowing, and indicators were changing colors.
“What is this?” I asked.
“That’s our dashboard,” the CEO replied with pride. “We track all our critical metrics in real-time. Every executive in the company sees these numbers and makes decisions based on them.”
“Impressive,” I agreed, turning away from the screen to face the CEO. “So, remind me, why am I here? How can I help?”
“We are growing too slowly,” the CEO replied. “We lack breakthrough ideas.”
“Despite all this data?” I asked, with a hint of irony. The CEO just let out a silent sigh.
Contrary to the promises of data-driven evangelists, data can only take you so far. It won’t help you craft a new strategy.
What’s wrong with data?
Data suffers from three “inherent pathologies”:
- It is harvested from a situation that has already passed and may never repeat.
- It is exclusively about the past.
- It tells you “what” happened, but never “why.”
Pathology #1
Data from the last quarter was relevant only to the reality of the last quarter. back then, your customers behaved a certain way because they were influenced by a myriad of factors, most of which were entirely outside your control.
For instance, your main competitor might have been out of stock, and you likely didn’t even know it.
Pathology #2
Data records what actually happened, but it stays silent about the future. The fact that customers bought more of Product A than Product B yesterday does not guarantee they will do the same tomorrow.
Until mid-2024, EV sales were booming. Then, in 2024, growth slowed sharply—in some countries, down to fractions of a percent.
The future is not a linear consequence of the past. The past is merely the soil; the future grows from it, but it follows its own unpredictable trajectory.
Pathology #3
If a customer didn’t buy your product—that is data. But within that data, you will never find the reason: perhaps they didn’t buy it because they saw a TikTok video mocking your product as “old-fashioned.”
Did the customer return the product because it was too expensive? Lacked features? Was it ugly? Did they find a better option?
You will almost certainly not learn this from the spreadsheet.
But the one thing data will never tell you is the answer to the ultimate question: “What product should we build so the customer buys it?” Numbers cannot replace the creative leap.
Data on candle performance can help you optimize the candle, but it will never help you invent the lightbulb.
In fact, the initial metrics for the lightbulb would look worse than those of a candle: it was far
more expensive, harder to use, and required complex wiring.
This is why, when Thomas Edison invented the lightbulb, he certainly wasn’t relying on historical data about candles.
What can we do instead?
When I work with my clients we replace traditional convergent thinking with divergent thinking.
Convergent thinking relies on solving problems using a pre-set algorithm.
The Example: We set a Strategic Goal -> Analyze Market Data -> Identify Promising Niches -> Make a Choice -> Write the Plan.
Divergent thinking, on the other hand, is about generating a multitude of potential solutions for a given challenge.
The Example of Divergent Strategic Thinking:
We deliberately step away from the data in the initial phase to hunt for raw insights. This is disciplined work, but it is fueled by intuition and experience.
We build hypotheses about customer pain points that could anchor a new strategy. This involves deep interviews, creative simulations, and strategic foresight.
Next, we validate these early ideas through qualitative research. Working with a shortlist of potential needs, we apply creative tools again—this is where hypotheses for new products or business models are born.
Only then, when we are ready to stress-test these ideas, do the numbers enter the stage.
Here, numbers are not the source of the decision, but a tool for validation. The true foundation is creative thinking and a deep understanding of the customer.
Reclaim the Creative Leap
Data is an excellent servant for validation, but a terrible master for creation. True growth requires the courage to formulate hypotheses that data cannot yet prove.
My methodology helps leadership teams build the internal capability for this divergent thinking. We use structured tools—like strategic foresight and innovation workshops—to turn intuition into verifiable business models.
This is not about “being more creative.” It is about installing a disciplined engine for growth.
If you are ready to lead this shift, drop me a line