Tag Archives: truth

Hard Facts Are a Hard Thing

Hard Facts Are a Hard Thing

GUEST POST from Greg Satell

In 1977, Ken Olsen, the founder and CEO of Digital Equipment Corporation, reportedly said, “There is no reason for any individual to have a computer in his home.” It was an amazingly foolish thing to say and, ever since, observers have pointed to Olsen’s comment to show how supposed experts can be wildly wrong.

The problem is that Olsen was misquoted. In fact, his company was actually in the business of selling personal computers and he had one in his own home. This happens more often than you would think. Other famous quotes, such IBM CEO Thomas Watson predicting that there would be a global market for only five computers, are similarly false.

There is great fun in bashing experts, which is why so many inaccurate quotes get repeated so often. If the experts are always getting it wrong, then we are liberated from the constraints of expertise and the burden of evidence. That’s the hard thing about hard facts. They can be so elusive that it’s easy to believe doubt their existence. Yet they do exist and they matter.

The Search for Absolute Truth

In the early 20th century, science and technology emerged as a rising force in western society. The new wonders of electricity, automobiles and telecommunication were quickly shaping how people lived, worked and thought. Empirical verification, rather than theoretical musing, became the standard by which ideas were measured.

It was against this backdrop that Moritz Schlick formed the Vienna Circle, which became the center of the logical positivist movement and aimed to bring a more scientific approach to human thought. Throughout the 20’s and 30’s, the movement spread and became a symbol of the new technological age.

At the core of logical positivism was Ludwig Wittgenstein’s theory of atomic facts, the idea the world could be reduced to a set of statements that could be verified as being true or false—no opinions or speculation allowed. Those statements, in turn, would be governed by a set of logical algorithms which would determine the validity of any argument.

It was, to the great thinkers of the day, both a grand vision and an exciting challenge. If all facts could be absolutely verified, then we could confirm ideas with absolute certainty. Unfortunately, the effort would fail so miserably that Wittgenstein himself would eventually disown it. Instead of building a world of verifiable objective reality, we would be plunged into uncertainty.

The Fall of Logic and the Rise of Uncertainty

Ironically, while the logical positivist movement was gaining steam, two seemingly obscure developments threatened to undermine it. The first was a hole at the center of logic called Russell’s Paradox, which suggested that some statements could be both true and false. The second was quantum mechanics, a strange new science in which even physical objects could defy measurement.

Yet the battle for absolute facts would not go down without a fight. David Hilbert, the most revered mathematician of the time, created a program to resolve Russell’s Paradox. Albert Einstein, for his part, argued passionately against the probabilistic quantum universe, declaring that “God does not play dice with the universe.”

Alas, it was all for naught. Kurt Gödel would prove that every logical system is flawed with contradictions. Alan Turing would show that all numbers are not computable. The Einstein-Bohr debates would be resolved in Bohr’s favor, destroying Einstein’s vision of an objective physical reality and leaving us with an uncertain universe.

These developments weren’t all bad. In fact, they were what made modern computing possible. However, they left us with an uncomfortable uncertainty. Facts could no longer be absolutely verifiable, but would stand until they could be falsified. We could, after thorough testing, become highly confident in our facts, but never completely sure.

Science, Truth and Falsifiability

In Richard Feynman’s 1974 commencement speech at Cal-Tech, he recounted going to a new-age resort where people were learning reflexology. A man was sitting in a hot tub rubbing a woman’s big toe and asking the instructor, “Is this the pituitary?” Unable to contain himself, the great physicist blurted out, “You’re a hell of a long way from the pituitary, man.”

His point was that it’s relatively easy to make something appear “scientific” by, for example, having people wear white coats or present charts and tables, but that doesn’t make it real science. True science is testable and falsifiable. You can’t merely state what you believe to be true, but must give others a means to test it and prove you wrong.

This is important because it’s very easy for things to look like the truth, but actually be false. That’s why we need to be careful, especially when we believe something to be true. The burden is even greater when it is something that “everybody knows.” That’s when we need to redouble our efforts, dig in and make sure we verify our facts.

“We’ve learned from experience that the truth will out,” Feynman said. “The first principle is that you must not fool yourself—and you are the easiest person to fool.” Truth doesn’t reveal itself so easily, but it’s out there and we can find it if we are willing to make the effort.

The Lie of a Post-Truth World

Writing a non-fiction book can be a grueling process. You not only need to gather hundreds of pages of facts and mold them into a coherent story that interests the reader, but also to verify that those facts are true. For both of my books, Mapping Innovation and Cascades, I spent countless hours consulting sources and sending out fact checks.

Still, I lived in fear knowing that whatever I put on the page would permanently be there for anyone to discredit. In fact, I would later find two minor inaccuracies in my first book (ironically, both had been checked with primary sources). These were not, to be sure, material errors, but they wounded me. I’m sure, in time, others will be uncovered as well.

Yet I don’t believe that those errors diminish the validity of the greater project. In fact, I think that those imperfections serve to underline the larger truth that the search for knowledge is always a journey, elusive and just out of reach. We can struggle for a lifetime to grasp even a small part of it, but to shake free even a few seemingly insignificant nuggets can be a gift.

Yet all too often people value belief more than facts. That’s why they repeat things that aren’t factual, because they believe they point to some deeper truth that defy facts in evidence. Yet that is not truth. It is just a way of fooling yourself and, if you’re persuasive, fooling others as well. Still, as Feynman pointed out long ago, “We’ve learned from experience that the truth will out.”

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Tell the Truth, Even When It’s Hard

Tell the Truth, Even When It's Hard

GUEST POST from Mike Shipulski

Our behavior is a result of causes and conditions. One thing paves the way for the next. Elements of the first thing create a preferential path for the next thing. If someone gets praised for doing A, more people will do A, even when A is the wrong behavior. If someone gets chastised for doing B, B won’t happen again, even when B is the right behavior.

The most troubling set of causes and conditions are those that block people from telling their truth. When everyone knows it’s a bad idea, but no one is willing to say it out loud, that’s a big problem. In fact, it may be the biggest problem.

When people think they won’t be taken seriously, they keep their truth to themselves. When people know they will be dismissed, they keep quiet. When people feel the situation is hopeless because there’s no way they’ll be listened to, they say nothing.

When people see others not taken seriously, that creates conditions for future truths to be withheld. When people see others being dismissed, that creates conditions for future truths to be kept quiet. When people see others in others from not being listened to, that creates conditions for future truths to remain unsaid.

And causes and conditions are self-strengthening. The more causes and conditions are reinforced, the more the behaviors become ingrained. The more people are stifled, the more they will keep quiet. The more people are dismissed, the more they’ll shut up. The more people’s truths are ignored, the more they’ll remain unsaid.

Here are three rules for truth-telling that will help you and your company move forward:

  • Without truth-telling, there can be no truth-telling.
  • The longer truth-telling is stifled, the harder it is for truth-telling to reemerge.
  • Truth-telling begets truth-telling.

Image credit — Jinterwas

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Combining Big Data with Empathy Interviews

Triangulating Truth

Combining Big Data with Empathy Interviews

GUEST POST from Chateau G Pato
LAST UPDATED: January 15, 2026 at 10:23AM

Triangulating Truth: Combining Big Data with Empathy Interviews

By Braden Kelley

In the hallowed halls of modern enterprise, Big Data has become a sort of secular deity. We bow before dashboards, sacrifice our intuition at the altar of spreadsheets, and believe that if we just gather enough petabytes, the “truth” of our customers will emerge. But data, for all its power, has a significant limitation: it can tell you everything about what your customers are doing, yet it remains profoundly silent on why they are doing it.

If we want to lead human-centered change and drive meaningful innovation, we must stop treating data and empathy as opposing forces. Instead, we must practice the art of triangulation. We need to combine the cold, hard “What” of Big Data with the warm, messy “Why” of Empathy Interviews to find the resonant truth that lives in the intersection.

“Big Data can tell you that 40% of your users drop off at the third step of your checkout process, but it takes an empathy interview to realize they are dropping off because that step makes them feel untrusted. You can optimize a click with data, but you build a relationship with empathy.” — Braden Kelley

The Blind Spots of the Spreadsheet

Data is a rearview mirror. It captures the digital exhaust of past behaviors. While it is incredibly useful for spotting trends and identifying friction points at scale, it is inherently limited by its own parameters. You can only analyze the data you choose to collect. If a customer is struggling with your product for a reason you haven’t thought to measure, that struggle will remain invisible on your dashboard.

This is where human-centered innovation comes in. Empathy interviews — deep, open-ended conversations that prioritize listening over selling — allow us to step out from behind the screen and into the user’s reality. They uncover “Thick Data,” a term popularized by Tricia Wang, which refers to the qualitative information that provides context and meaning to the quantitative patterns.

Case Study 1: The “Functional” Failure of a Health App

The Quantitative Signal

A leading healthcare technology company launched a sophisticated app designed to help chronic patients track their medication. The Big Data was glowing initially: high download rates and excellent initial onboarding. However, after three weeks, the data showed a catastrophic “churn” rate. Users simply stopped logging their pills.

The Empathy Insight

The data team suggested a technical fix — more push notifications and gamified rewards. But the innovation team chose to conduct empathy interviews. They visited patients in their homes. What they found was heartbreakingly human. Patients didn’t forget their pills; rather, every time the app pinged them, it felt like a reminder of their illness. The app’s sterile, clinical design and constant alerts made them feel like “patients” rather than people trying to live their lives. The friction wasn’t functional; it was emotional.

The Triangulated Result

By combining the “what” (drop-off at week three) with the “why” (emotional fatigue), the company pivoted. They redesigned the app to focus on “Wellness Goals” and life milestones, using softer language and celebratory tones. Churn plummeted because they solved the human problem the data couldn’t see.

Triangulation: What They Say vs. What They Do

True triangulation involves three distinct pillars of insight:

  • Big Data: What they actually did (the objective record).
  • Empathy Interviews: What they say they feel and want (the subjective narrative).
  • Observation: What we see when we watch them use the product (the behavioral truth).

Often, these three pillars disagree. A customer might say they want a “professional” interface (Interview), but the Data shows they spend more time on pages with vibrant, casual imagery. The “Truth” isn’t in one or the other; it’s in the tension between them. As an innovation speaker, I often tell my audiences: “Don’t listen to what customers say; listen to why they are saying it.”

Case Study 2: Reimagining the Bank Branch

The Quantitative Signal

A regional bank saw a 30% decline in branch visits over two years. The Big Data suggested that physical branches were becoming obsolete and that investment should shift entirely to the mobile app. To the data-driven executive, the answer was to close 50% of the locations.

The Empathy Insight

The bank conducted empathy interviews with “low-frequency” visitors. They discovered that while customers used the app for routine tasks, they felt a deep sense of anxiety about major life events — buying a first home, managing an inheritance, or starting a business. They weren’t coming to the branch because the branch felt like a transaction center (teller lines and glass barriers), which didn’t match their need for high-stakes advice.

The Triangulated Result

The bank didn’t close the branches; they transformed them. They used data to identify which branches should remain as transaction hubs and which should be converted into “Advice Centers” with coffee-shop vibes and private consultation rooms. They used the app to handle the “what” and the human staff to handle the “why.” Profitability per square foot increased because they addressed the human need for reassurance that the data had initially misinterpreted as a desire for total digital isolation.

Leading the Change

To implement this in your organization, you must break down the silos between your Data Scientists and your Design Researchers. When these two groups collaborate, they become a formidable force for human-centered change.

Start by taking an anomaly in your data — something that doesn’t make sense — and instead of running another query, go out and talk to five people. Ask them about their day, their frustrations, and their dreams. You will find that the most valuable insights aren’t hidden in a server farm; they are hidden in the stories your customers are waiting to tell you.

If you are looking for an innovation speaker to help your team bridge this gap, remember that the most successful organizations are those that can speak both the language of the machine and the language of the heart.

Frequently Asked Questions on Insight Triangulation

Q: What is the primary danger of relying solely on Big Data for innovation?

A: Big Data is excellent at showing “what” is happening, but it is blind to “why.” Relying only on data leads to optimizing the status quo rather than discovering breakthrough needs, as data only reflects past behaviors and cannot capture the emotional friction or unmet desires of the user.

Q: How do empathy interviews complement quantitative analytics?

A: Empathy interviews provide the “thick data” — the context, emotions, and stories that explain the anomalies in the quantitative charts. They allow innovators to see the world through the user’s eyes, identifying the root causes of friction that data points can only hint at.

Q: What is “Triangulating Truth” in a business context?

A: It is the strategic practice of validating insights by looking at them from three angles: what people say (interviews), what people do (observations), and what the data shows (analytics). When these three align, you have found a reliable truth worth investing in.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credits: Pixabay

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