Reconciling Numbers and Narratives: Making Meaning from a Mismatch

You’ve got a dashboard full of numbers, a pile of focus group notes, and a dozen employee exit surveys echoing conflicting opinions. You’ve done the work, gathered the data, combed through the inputs, maybe even built a slide deck… but something still doesn’t sit right. 

The numbers say one thing. 

The stories say another. 

And now you’re stuck in that awkward in-between: What do you do when the data doesn’t match the story you’ve been hearing OR what you thought to be true?

This isn’t rare. In fact, it’s one of the most common (and perhaps most misunderstood) tensions we see in our work with corporate and education clients alike. Data misalignment doesn’t mean your research failed; it means you’re doing the real work of sensemaking. 

Let’s unpack why mismatches happen, why they’re actually useful, and how to reconcile them in a way that leads to better decisions. 

Mismatches Are a Feature, Not a Flaw

When professionals encounter conflicting data – say, a survey that indicates high satisfaction and interviews that surface deep frustrations on the same topic – the instinct is often to search for the “right” answer. But truth in complex systems is rarely binary. 

Instead, mismatched data usually reveals one of three things: 

• Multiple truths are coexisting (and maybe competing). 

• One source lacks nuance, depth, or context. 

• There’s a gap between perception and lived experience. 

Rather than trying to eliminate the inconsistency, embrace it as a prompt: What’s the root cause of the symptoms? Why might different groups be seeing or feeling different things? 

Example: “The Survey Says We’re Fine, But No One Feels Fine”

Let’s say your latest employee engagement survey shows 85% of staff are “satisfied” with their job, but in 1:1s, people express burnout, misalignment, and lack of clarity with their supervisor. 

This isn’t a red flag or a major cause of concern. It’s an insight. 

Maybe the survey questions were too broad. Maybe people were satisfied with their team but frustrated with their organization. Maybe they didn’t feel safe being fully candid in a survey format. 

The dissonance between numbers and narrative tells you something the raw data simply can’t: context matters. 

The Real World is Messy… and So is Good Analysis

At transform.forward, we call this work triangulation – using multiple data points and perspectives to paint a fuller, more accurate picture. 

But unlike geometry class, triangulation isn’t always tidy. Here’s what we recommend when the data doesn’t line up neatly: 

1.Zoom Out Before You Zoom In

Start by taking a step back. Who did you hear from? What methods were used? What biases or limitations might be embedded in each source? How big was the sample size, or what was the response rate? 

Discrepancies often emerge when one group is overrepresented or when different formats privilege different kinds of expression (e.g., extroverts in focus groups vs. introverts in surveys). 

2.Map the Contradictions

Visually lay out what each data source is telling you. Where do they align? Where do they diverge? What’s being emphasized, and what’s conspicuously missing?

This is less about finding gaps to “correct” and more about surfacing layers you might not have noticed. Patterns in misalignments are data, too. 

3. Use Tension as a Diagnostic

When themes feel in conflict, treat that tension as a signal. For example:

• High program satisfaction + low retention → Are people happy… but still leaving? 

• Strong leadership ratings + poor communication feedback → Is the vision clear at the top, but murky in translation? 

• Declining participation in a program + positive feedback in exit survey responses → Are people answering aspirationally, rather than truthfully? 

These aren’t problems to hide; rather, they’re springboards for deeper inquiry. 

4. Center Humans. Not Just Hypotheses 

Quantitative data is excellent at showing “what.” Qualitative data helps explain “why.”
When in doubt, prioritize listening. A low survey score might point to an issue. A story, especially one echoed across groups, helps define it and create momentum for change. 
And if you’re leading decision-making? Ground your recommendations in both: the scale of the challenge and the nuance of the experience. 

How We Define Themes When the Data is Conflicted

When we synthesize findings for a strategic plan, organizational study, or stakeholder report, we may face misalignment across inputs. Here’s how we navigate it in practice:

• Name the tension clearly: “While most survey respondents expressed satisfaction with internal communications, focus group participants described significant variation in how updates are received and understood.”

• Describe what might be contributing: “This difference may stem from inconsistent messaging across departments, or differing expectations based on role and proximity to leadership.”

• Avoid flattening complexity into a single takeaway: Instead of saying “communications are fine” or “communications are broken,” we might define a theme like: “Communication experiences vary widely across teams, indicating both bright spots and breakdowns in internal messaging.”

• Offer nuance without ambiguity: The goal isn’t to be vague but to reflect the real world. Present a clear takeaway and acknowledge variation where it exists. 

When Stakeholders Want a Neat Answer

One of the hardest parts of reconciling conflicting data is when decision-makers want a quick fix or a clear-cut answer. 

That’s understandable – but it’s not always realistic. 

Instead of hiding complexity, we recommend being transparent about it. Highlight where the data agrees, where it doesn’t, and what your recommended path forward is given the full context. 

Sometimes that means saying: “We heard X from students and Y from faculty. Both perspectives are valid, and any solution will need to address both.” or “The quantitative data suggests this initiative is working; however, the qualitative data suggests the implementation feels uneven. Let’s dig deeper before scaling.”

The best leaders aren’t afraid of complexity. They want clarity within that complexity, and that’s where thoughtful analysis shines. 

So Mismatched Data Isn’t a Problem; It’s an Opportunity

If you’re looking at your data and wondering, “Why doesn’t this all line up?” – you’re not alone, and you’re probably doing it right. 

Real-world insights are rarely linear. Numbers can be clean; experiences rarely are. Your job isn’t to force alignment where it doesn’t exist. It’s to hold space for complexity, identify signals across the noise, and move forward with eyes wide open. 

So next time the story doesn’t match the spreadsheet? Don’t panic. Get curious. That’s where the real insight lives. 

Facing conflicting data in your own strategy work? We help organizations reconcile inputs, make meaning from messiness, and move forward with confidence. You know where to find us!