The Cost of Poor Data Quality

The short answer. Poor data quality costs money in ways that rarely appear as a single line item: rework and reconciliation time, decisions made on wrong numbers, projects that overrun because data was not fit to move, compliance exposure, and automation that acts confidently on flawed inputs. Because the cost is distributed across many small failures, it is systematically underestimated. Making it visible, by measuring quality and attaching it to business impact, is the first step to reducing it.

Every finance leader has seen a business case for data quality and wondered what the return actually is. It is a fair question, and the honest answer starts by acknowledging why the cost is hard to see in the first place.

Why the cost hides

Bad data almost never produces one visible failure with a number attached. It produces an hour of rework here, a slightly wrong decision there, a project that runs a few weeks long, a report a leader quietly stops trusting. None of it lands as a line item anyone owns. That distribution is exactly why it persists: a cost that nobody is accountable for is a cost nobody reduces. The organisations that take data quality seriously are usually the ones that have already paid a large, visible version of it once.

Where the cost actually lands

Area	How the cost shows up
Operational rework	Staff time spent reconciling, correcting and working around bad records
Decision quality	Choices made on inaccurate figures, and shadow spreadsheets built because the system is not trusted
Project overrun	Migrations and system changes that stall because data was not fit to move
Compliance exposure	Gaps and errors surfacing in audit, and sensitive data in places it should not be
Automation and AI	Models and automated processes acting on flawed inputs at speed and scale
Duplicate-driven waste	Inflated stock, split spend, duplicated outreach to the same customer

Migration makes the cost visible

A migration is where years of tolerated data problems finally present a bill.

If you want a concrete example of the cost, an ERP migration provides one. Research into S/4HANA programmes repeatedly identifies inadequate data planning as a cause of overrun, with historical and master data named as a hidden cost driver. The mechanism is simple: a new system validates data more strictly than the old one, so problems tolerated for years suddenly block loads, and the remediation happens under deadline pressure at consultant day rates. The data was always wrong. The migration is just when the invoice arrives.

How to make it countable

1. Baseline the condition. Profile your key data to establish completeness, duplication and consistency, so you know the size of the problem rather than guessing.
2. Attach impact to the worst findings. For the highest-impact problems, estimate what they cost in rework hours, delayed decisions or project risk.
3. Count the workarounds. Shadow spreadsheets and manual reconciliation are measurable proxies for distrust in the data.
4. Track the trend. Improvement is the proof, and it only exists if you measured the starting point.
5. Express it in the language of the audience. Avoided cost and reduced risk land better with finance than quality scores do.

Automation raises the stakes

One shift makes this more urgent than it was. A person reading a report can sense when a figure looks wrong. An automated process or an AI model cannot: it treats the data as true and acts at speed and scale. That means data quality increasingly sets a ceiling on how far an organisation can safely automate. For businesses investing in AI, data quality stops being a hygiene topic and becomes the foundation the larger investment depends on, which is usually the most persuasive framing available.

A note on the numbers you will see quoted

Search this topic and you will find large, confident figures for what poor data quality costs organisations annually. Treat them carefully. Many trace back to older studies, are repeated without the original methodology, or describe averages across industries that may have little to do with your situation. Citing an unverifiable number in a business case is a risk, because the first person to check it undermines everything around it. A smaller figure you measured in your own organisation is more persuasive than a large one you cannot source, and it has the advantage of being defensible in the room.

Where to start counting

If you want a number without launching a study, start narrow. Pick the data domain people complain about most, profile it, and pick the two findings with the clearest operational consequence. Estimate the time currently spent working around each one, using the team's own figures rather than benchmarks. That gives you a small, specific, defensible cost attached to a named problem. It will understate the total, which is fine: the purpose is not a complete picture but a credible first number that funds the next step. Comprehensive costing can follow once the direction is agreed.

Frame it for the person you are asking

The same underlying facts land differently depending on who is hearing them. To finance, the story is avoided cost and reduced risk against a measured baseline. To operations, it is fewer fire drills and less rework. To compliance, it is a smaller exposure surface and fewer audit findings. To leaders funding AI and automation, it is the foundation that makes those investments safe rather than speculative. Teams that struggle to get data quality funded often have the evidence and are presenting it in the wrong language for the room. Translating the same numbers into the listener's concerns is usually what unlocks the decision.

How deKorvai helps

deKorvai makes the hidden cost visible, which is the precondition for reducing it. It profiles data automatically to establish its real condition, applies configurable business and technical rules so quality is measured against a defined standard, and reports results on real-time scorecards with trend insight. That gives you the baseline a business case needs and the ongoing measurement that proves improvement. It also addresses the costliest category directly, resolving duplicates into golden records using fuzzy matching that catches near-duplicates rather than only exact copies.

Key takeaways

• The cost is distributed, which is why it is underestimated and tolerated.
• It lands in rework, decisions, projects, compliance and automation.
• Migration is where the bill arrives, under deadline pressure.
• Baseline first: you cannot show a return on a cost nobody counted.

Frequently asked questions

What does poor data quality cost a business?

It costs in operational rework, decisions made on wrong figures, projects that overrun because data was not fit to move, compliance exposure, duplicate-driven waste such as inflated stock and split spend, and automation acting on flawed inputs.

Why is the cost of poor data quality so hard to see?

Because it is distributed. Bad data rarely produces one visible failure with a number attached; it produces an hour of rework here and a slightly wrong decision there. A cost nobody owns is a cost nobody reduces.

How do you quantify the cost of bad data?

Baseline the condition by profiling your key data, attach business impact to the worst findings in rework hours or project risk, count the workarounds such as shadow spreadsheets, track the trend over time, and express the result as avoided cost rather than quality scores.

Why does AI make data quality more urgent?

Because a person reading a report can sense when a figure looks wrong, while an automated process or model treats the data as true and acts at speed and scale. Data quality therefore sets a ceiling on how far an organisation can safely automate.