“The data is wrong.”
Few statements give me the heebie-jeebies quite like this one.
Not because it’s never true. Sometimes data is wrong. But because it’s almost always a conversation ender rather than a conversation starter.
The dismissal pattern
Here’s how the pattern usually goes:
- Analysis reveals something uncomfortable
- Someone says “that data is wrong”
- Everyone nods
- The uncomfortable finding is ignored
- Nothing changes
“The data is wrong” becomes a shield against insights people don’t want to hear. It’s easier to question the data than to question the decisions the data challenges.
What’s usually actually happening
When someone says “the data is wrong,” one of several things is usually true:
The data disagrees with intuition. Which might mean the data is wrong. Or might mean intuition is wrong. Dismissing the data without investigation means you’ll never know which.
The data reveals a problem. Problems are uncomfortable. Data that surfaces problems can feel like an attack on the people responsible. “The data is wrong” is sometimes “I don’t want this to be true.”
The data contradicts a prior decision. If we approved a strategy based on certain assumptions, data that challenges those assumptions threatens the decision. Easier to question the data than to revisit the strategy.
The data is actually wrong. This does happen. But it should be the conclusion of investigation, not the starting assumption.
The 60-80% waste
Research suggests that companies use only 20-40% of the data they collect. The rest sits in systems, untouched and unexamined.
Every “the data is wrong” dismissal adds to that waste. Data that could reveal insights gets labelled as unreliable and ignored.
The irony: the data most likely to be dismissed is often the data most likely to reveal problems. Comfortable data gets accepted. Uncomfortable data gets questioned.
Over time, organisations train themselves to ignore signals that challenge the status quo.
The alternative approach
When someone says “the data is wrong,” don’t accept or reject. Investigate.
Ask: “Wrong how?” Is the issue completeness (missing records)? Accuracy (incorrect values)? Timeliness (outdated information)? Specificity helps.
Ask: “Can we verify?” Is there another data source we can check against? Can we sample and validate?
Ask: “If the data were right, what would that mean?” Sometimes the implications are so uncomfortable that this question is hard to answer honestly. That’s information about the resistance, not the data.
Ask: “What would convince you?” If there’s a standard of proof that would settle the question, pursue it. If no evidence would be sufficient, the objection isn’t about data.
The goldmine in your systems
The data sitting unused in your CRM, your call reporting system, your customer records -that data isn’t perfect. No data is.
But imperfect data analysed thoughtfully beats perfect intuition operating blindly.
The patterns are there. The trends are visible. The problems are signalling.
“The data is wrong” keeps those signals from being heard.
What I say now
When I hear “the data is wrong,” I’ve learned to say: “Let’s find out.”
Not “you’re wrong.” Not “trust the data.” Just “let’s find out.”
Investigation usually reveals one of two things:
-
The data has a specific, fixable problem. Good. Now we can improve the data and try again.
-
The data is sound, and someone doesn’t like what it shows. Also useful information. Now we can have the real conversation about what the data means.
Both outcomes move things forward. “The data is wrong” as a final statement moves nothing.
Your data isn’t perfect. It’s still better than guessing. The goldmine is sitting there. Stop dismissing it and start investigating.
Written by
Dieter Herbst
CEO & Founder at Herbst Group. Working with pharmaceutical commercial leaders across South Africa, Kenya, and Brazil to transform sales force effectiveness through evidence-based approaches.
Connect on LinkedIn
