Everyone knows bad data is a problem. Few know how expensive it actually is.
The costs hide. They're embedded in slow decisions, missed opportunities, wasted effort, and customer friction. Nobody tracks "hours lost to data problems" as a line item.
But the costs are real. And they compound.
Here's how to see what bad data is actually costing you—and make the case for fixing it.
The Visible Costs
Some data problems have obvious price tags.
Manual data cleaning
How many hours per week do your people spend fixing data?
- Correcting duplicate records
- Reconciling mismatched information
- Filling in missing fields
- Standardising formats
- Cross-checking between systems
Add it up. A team of five spending two hours each per week on data cleaning is 520 hours per year. At $50/hour loaded cost, that's $26,000 annually—just on cleanup.
And that's a conservative estimate. Many organisations spend far more.
Failed integrations
When data is inconsistent, integrations break.
- Orders fail to sync between systems
- Customer records don't match across platforms
- Inventory levels disagree between warehouse and website
- Reports pull different numbers from different sources
Each failure requires investigation and manual intervention. Even "minor" integration issues can consume hours of staff time weekly.
If your team regularly complains that systems don't talk to each other, bad data is often the root cause.
Incorrect reporting
Bad data produces bad reports. Bad reports produce bad decisions.
When leadership debates numbers instead of direction, meetings stall. Decisions get delayed. Everyone wastes time reconciling instead of strategizing.
The cost isn't just the meeting time—it's the opportunity cost of decisions not made.
The Hidden Costs
The bigger costs are the ones you don't see.
Slow decisions
When you can't trust your data, decisions take longer.
Every question requires verification. Every report needs a caveat. Every analysis prompts "but are these numbers right?"
Organisations with trusted data make decisions in hours. Organisations with bad data make the same decisions in weeks—if they make them at all.
What's the cost of being slow in a competitive market?
Missed opportunities
Bad data means missed signals.
- The customer segment that's growing fastest—invisible because the data is fragmented
- The product that's underperforming—masked by incorrect cost allocation
- The market trend you could have caught early—lost in noise and errors
You can't optimise what you can't see. Bad data blinds you to opportunities competitors might catch.
Customer friction
Bad data creates bad customer experiences.
- Shipping to outdated addresses
- Applying wrong pricing
- Sending irrelevant communications
- Asking customers to provide information you already have
- Getting their name wrong
Each incident is small. Collectively, they erode trust. Customer lifetime value drops. Acquisition costs rise to replace churned customers.
Employee frustration
Your best people hate fighting systems that don't work.
They joined to do meaningful work, not to reconcile spreadsheets. When data problems make their jobs harder, engagement drops. Eventually, they leave.
Replacing skilled employees costs 50-200% of annual salary. How much of your turnover traces back to frustration with broken systems and bad data?
Opportunity cost of analytics investment
Maybe you've invested in analytics tools. A data warehouse. Dashboards. BI platforms.
If the underlying data is bad, those investments underperform. You're running expensive tools on garbage inputs. The outputs might look pretty, but they can't be trusted.
The investment doesn't pay off until the data quality improves.
Quantifying the Damage
To make the case for fixing data problems, you need numbers. Here's a framework.
Direct labour costs
Identify everyone who touches data cleanup:
- Operations staff reconciling records
- Finance team adjusting for errors
- IT team fixing integration failures
- Analysts cleaning data before reports
- Customer service resolving data-related complaints
Estimate hours per week. Multiply by loaded hourly cost. Annualise.
Formula: Hours/week × People × Weeks/year × Hourly cost = Annual direct cost
Error correction costs
Track data-related errors and their resolution costs:
- Shipping errors (reshipping, refunds, labour)
- Billing errors (credit notes, reconciliation, customer recovery)
- Inventory errors (emergency orders, stockouts, write-offs)
- Compliance errors (penalties, audit remediation, legal)
Estimate frequency and average cost per incident. Multiply.
Formula: Incidents/year × Average cost per incident = Annual error cost
Decision delay costs
Estimate how much faster decisions could be with trusted data.
Pick a specific decision type. How long does it take now? How long should it take? What's the value at stake?
Example: A pricing decision takes two weeks because of data uncertainty. With trusted data, it would take two days. The 12-day delay on a decision affecting $100,000/month in revenue costs ~$40,000 in foregone optimisation per incident.
Customer impact costs
Estimate customer-facing data problems:
- How many customers experience data-related issues annually?
- What percentage of those issues contribute to churn?
- What's the lifetime value of a churned customer?
Even small percentages add up. If bad data contributes to 5% of your churn, and churn costs you $500,000 annually, that's $25,000 attributable to data quality.
Total cost of poor data quality
Add up:
- Direct labour costs
- Error correction costs
- Decision delay costs (conservative estimate)
- Customer impact costs
This total is your "data tax"—what you're paying every year because of poor data quality.
Most businesses that do this exercise are shocked by the number. It's typically 5-25% of operating expenses, depending on how data-intensive the business is.
Root Causes
Bad data doesn't happen randomly. It has causes.
No single source of truth
Multiple systems storing the same information means multiple versions of truth.
Customer records in the CRM, the ERP, the email platform, the support system. Each slightly different. Each confident it's correct.
Without a master source, discrepancies are inevitable.
Poor data entry
Data quality starts at the point of entry.
- Fields that aren't required so they get skipped
- No validation so garbage gets accepted
- Poorly designed forms that confuse users
- Manual entry of information that could be automated
Every error at entry propagates through the system.
System silos
When systems don't integrate properly, data degrades at the boundaries.
Information entered once has to be re-entered elsewhere. Formats change. Fields don't map. Timing creates conflicts.
Silos aren't just inefficient—they're data quality killers.
Process gaps
Who's responsible for data quality? Usually, nobody specifically.
Without clear ownership:
- Nobody monitors for problems
- Nobody enforces standards
- Nobody fixes root causes
- Problems accumulate until crisis forces action
Legacy systems
Old systems have old limitations.
- Inadequate field lengths (truncated data)
- Missing validation rules
- Poor integration capabilities
- Outdated data models
Legacy systems often can't support modern data quality requirements.
Fixing the Problem
Improving data quality isn't a project—it's a capability. Here's where to start.
Establish ownership
Someone needs to be accountable for data quality. Not "everyone" (which means no one). A specific person or team.
This could be:
- A data governance role
- An operations leader
- A technology leader with expanded scope
- A fractional CTO who can establish practices
Ownership creates accountability. Accountability creates improvement.
Define standards
What does "good data" mean for your business?
Define:
- Required fields and their formats
- Validation rules
- Acceptable ranges and values
- Naming conventions
- Update frequencies
Standards give everyone clarity on expectations.
Fix at the source
Don't just clean bad data—prevent it.
- Add validation at data entry points
- Remove unnecessary manual entry through integration
- Design forms that guide correct input
- Automate data enrichment where possible
Every error prevented saves multiple cleanup efforts downstream.
Create a single source of truth
Choose authoritative systems for each data domain:
- Customer master: CRM or ERP?
- Product data: Where does it originate?
- Financial data: What's the system of record?
Then enforce those choices through integration. When systems conflict, the authoritative source wins.
Monitor continuously
Data quality degrades over time. Monitoring catches problems early.
Build dashboards that track:
- Duplicate records
- Missing required fields
- Values outside expected ranges
- Integration failures
- Data freshness
Review regularly. Fix systematically.
Invest appropriately
Data quality improvement has costs: tools, time, potentially system changes.
Compare those costs to your calculated "data tax." The ROI is usually compelling.
A $100,000 investment that reduces $300,000 annual data costs by 50% pays back in eight months.
Making the Case
When proposing data quality investment:
- Quantify current costs using the framework above
- Identify root causes so you can propose targeted solutions
- Project realistic improvements (not everything, but meaningful progress)
- Calculate ROI with conservative assumptions
- Start small if needed with a pilot that demonstrates value
The numbers usually speak for themselves. The challenge is doing the analysis to surface them.
The Compound Effect
Here's what makes data quality investment compelling: the benefits compound.
Good data enables better analytics. Better analytics enable better decisions. Better decisions create better outcomes.
Meanwhile, bad data does the opposite. Problems create more problems. Workarounds create complexity. Trust erodes.
You're either in a virtuous cycle or a vicious one. Data quality investment shifts you from one to the other.
The organisations that will win in the next decade are the ones making decisions from clean, trusted, timely data. The ones that don't will be flying blind while competitors see clearly.
Concerned about your data quality? Book a call to discuss your situation. We can help you assess the true cost and build a plan to fix it.
