When the Dashboard Is Lying (and You’re Helping It)
- James Partsch Jr
- May 15
- 3 min read
Misused Evidence: The Fallacy of Data That Sounds Smart but Misleads
You’ve got the charts.
The heat maps.
The KPIs that light up like a Christmas tree.
And yet… nothing’s improving. Revenue’s flat.
Customers are leaving quietly.
Employees are burning out while “efficiency” hits record highs.
Welcome to the fallacy of misused evidence, where data doesn’t lie, but the way we use it does.
The Data Trap: Confident, but Wrong
Bad decisions don’t always come from bad data. They come from good data used poorly, cherry-picked, stripped of context, or weaponized to defend a decision already made.
We’ve seen this play out a hundred ways:
A process “improved” because cycle time dropped, but quality tanked
A customer journey “optimized” because clicks went up, while conversions stayed flat
A new platform “successful” because tickets decreased, only because users gave up asking for help
The metrics looked great. The outcomes? Not so much.
Where Misused Evidence Shows Up
1. Cherry-Picking Metrics
Only showing the numbers that support your argument while ignoring everything else.
“Churn is down 3%!”...but net revenue retention is in freefall.
You're not analyzing — you're campaigning.
2. Correlation Theater
Mistaking coincidence for causation — and building strategy on a story, not a signal.
“Since we launched the new UI, revenue’s up.”
Sure — but maybe your top client just expanded their contract.
3. Vanity Metrics as Validation
Elevating surface-level signals because they’re easy to measure — not because they mean anything.
“Engagement is up!”
Cool. But conversions aren’t.And your CSAT score dipped so now that's a new fire.
4. Bias in the Build
Sometimes the data isn’t misleading.
We are.
Not on purpose — but because we’re looking to prove something, not understand something.
“We pulled the numbers to measure productivity.”
But what you really measured was speed, not value.And now everyone’s chasing shorter calls while quality erodes.
Bias creeps in when:
We define metrics before we define goals
We analyze what’s easy, not what matters
We see what we want to see, and stop looking deeper
📎 Sidebar: Real-World Bias - I once led a call center team where AHT dropped slightly one week. Management excitedly asked, “What did we change? Whatever it is do more of it!”
The answer? Nothing. It was day-to-day variation, statistically insignificant. But we still spent a week trying to explain a blip.
This is how bias shows up: in the urge to explain every bump as a breakthrough.
What It’s Costing You
Strategies built on signals that don’t actually signal
Teams chasing metrics instead of solving real problems
Real issues buried under “dashboard confidence”
Cultural habits that reward presentation over progress
And the painful part, once a metric becomes the goal, it stops being useful. You’re no longer managing performance, you’re managing perception.
What To Do Instead
Validate, Don’t Just MeasureAsk: “What does this metric actually prove, and what might it be hiding?”
Correlate With Context: Look at the data analytically, graphically, and practically. If the trend line says yes, but the real world says no, go with the real world.
Design for Outcomes, Not Optics: If you can’t connect a number to a decision, it’s not a KPI. It’s a decoration.
Build a Culture That Questions the Graph: Reward the person who says, “Is this even the right metric?” Not the one who makes the slide look pretty.
💬 Final Word:
If your data always tells you what you want to hear, it’s not data. It’s PR.
🔎 Up Next:
We’ll dig into Evasion Tactics — the logic dodges that protect bad decisions and kill accountability. Because sometimes the real problem isn’t the plan. It’s the people pretending there wasn’t one.
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