Triangulating Truth
GUEST POST from Chateau G Pato
LAST UPDATED: January 15, 2026 at 10:23AM
Triangulating Truth: Combining Big Data with Empathy Interviews
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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