The Real Cost of Bad Data: What You're Losing Without Realizing It
Disclaimer
At Zirkel, we work every day to help businesses fix data problems. So yes, we have a conflict of interest when we say, "Bad data is costing you more than you think."
But we’re not just speaking from opinion. We're standing on facts. In writing this article, we’re leaning on two peer-reviewed academic papers and several whitepapers published by the world's top audit firms. Sure, those firms, like us, have a commercial interest. But they also have decades of field experience, and so do we. What follows isn't sales talk. It's reality, backed by research.
Bad Data Isn't Just Annoying. It's Expensive.
When we hear "bad data," we think typos or a missing field here and there. Annoying, but harmless, right?
Not even close.
Bad data eats away at your business quietly but steadily. It wastes your team's time. It bloats your infrastructure costs. It sabotages your decision-making. And sometimes, it even puts your compliance at risk.
Academic research backs this up. The paper Cost and Value Management for Data Quality by Zhang, Yang, and Wang explains how bad data causes both direct losses (like mistakes and rework) and indirect ones (like damaged reputation and missed opportunities). Their advice? Don't chase perfect data. But do recognize that tolerating poor data comes with a very real price tag.
Copy of the graph that can be found in another peer-reviewed paper here: https://www.jiem.org/index.php/jiem/article/view/232/130
And this isn’t just theory. According to findings cited by Deloitte, 80% of companies suffer income loss due to poor data quality, with losses ranging from $10 to $14 million annually. PwC's research highlights massive revenue impacts from incomplete sales data. Real businesses. Real money.
Where the Costs Creep In
Time Wasted
According to Harvard Business Review, knowledge workers spend up to 50% of their time finding and cleaning data. That's not analysis. That's cleaning up messes. Imagine hiring a chef only to have them spend half their day doing dishes.
Higher Infrastructure Costs
Storing, processing, and backing up bad data isn't free. Duplicate records, incomplete entries, stale files—clog your databases and jack up your cloud bills.
Broken Automation
Automation is supposed to save time. But bad data triggers false alerts, broken workflows, and embarrassing customer emails. And when that happens, people stop trusting the system.
Bad Decisions
If the dashboard says sales are up—but they're not—you make the wrong move. You double down on a failing product. You skip a critical pivot.
Lost Trust
As the paper Data quality: The management dimension explains, data quality issues aren't just technical flaws. They're organizational risks. When users doubt the numbers, they stop using the tools. They rebuild everything in spreadsheets. They waste even more time.
Compliance Risks
KPMG highlights that bad data can mean failed audits and regulatory fines. In sectors like finance and healthcare, that's not just expensive. It's existential.
Why Does This Happen?
Most bad data problems aren't caused by lazy people. They're caused by systems that rely too much on manual work and have no ownership structure.
The less humans have to manually touch data, the fewer mistakes slip in. That's why automation matters. Not because people are bad at their jobs—but because no one can copy-paste perfectly a thousand times in a row.
And without clear data ownership, problems fester. Everyone assumes someone else is checking. Nobody actually is.
The research is clear: data quality is a management issue, not just an IT one.
How Much Is It Costing You?
MIT Sloan Management Review estimates that companies lose between 15% to 25% of their revenue due to bad data.
According to the UK Government, the range is 10% to 30% of revenue spent addressing data quality problems.
Whether you believe the higher or lower estimate, the math isn’t friendly. Even 10% of your revenue leaking out the side door is nothing to shrug off.
You Can't Afford to Ignore This
Fixing bad data doesn’t mean spending millions or building giant systems. It means:
Setting ownership clearly.
Automating repetitive tasks.
Auditing critical data flows.
Focusing on "good enough" quality where it matters most.
At Zirkel, we help companies do exactly that. We don't promise perfection. We promise progress. And often, that's all it takes to stop the bleeding.
If you want to know where your data might be quietly costing you, let's talk.
Bad data is expensive. Good data pays off.
Start fixing it today.
This blog was inspired by a recent case scenario in which we’re helping a service that was bloated by redundant and/or obsolete reports/datasources.
