Bad data costs money. That much is obvious. But how much, exactly? And more importantly, how do you make the case for investing in better data quality when leadership wants hard numbers?
For pharmaceutical and medical device companies, these questions carry extra weight. Regulatory submissions depend on accurate documentation. Clinical trial data must be airtight. A single data error in an eCTD submission can trigger months of delays, rejected applications, and millions in lost revenue.
This article breaks down how to calculate the real cost of bad data and measure the return on investment when you fix it.
What Counts as “Bad Data” in Pharma and MedTech?
Bad data isn’t always obvious. Sometimes it’s a typo in a patient record. Other times it’s outdated product specifications that never got updated after a formulation change. The common thread? The data doesn’t serve its intended purpose.
In regulated industries, bad data typically falls into a few categories. Inaccurate data contains errors, whether from manual entry mistakes or system glitches. Incomplete data is missing fields or records that downstream processes need. Inconsistent data varies in format or terminology across systems, making it hard to reconcile. Outdated data reflects old information that hasn’t been refreshed. Duplicate data creates confusion when the same record exists in multiple places with slight variations.
Any of these can cause problems in a regulatory context. Health Canada and the FDA require precise documentation. When your data doesn’t meet their standards, you’re not just dealing with internal inefficiency. You’re risking rejection letters, warning letters, or worse.
The Financial Impact Most Companies Underestimate
Research from Gartner puts the average annual cost of poor data quality at $12.9 million per organization. That figure spans industries, but pharma companies often face even steeper costs given the regulatory stakes involved.
A 2025 IBM study found that over a quarter of organizations estimate losing more than $5 million annually due to poor data quality. Seven percent reported losses exceeding $25 million.
These numbers don’t capture everything. Bad data rarely announces itself at the point of failure. Instead, it surfaces downstream as delayed workflows, compliance risks, failed submissions, and decisions made on faulty information.
Consider a mid-sized pharmaceutical company preparing a new drug application. If their clinical trial data contains inconsistencies, the regulatory team might spend weeks reconciling records before they can even start drafting the submission. That’s weeks of salary costs, plus the opportunity cost of delayed market entry. If the product generates $100,000 in daily revenue once approved, every day of delay has a direct dollar value.
Where Bad Data Hits Hardest in Regulatory Affairs
The regulatory submission process is particularly vulnerable to data quality issues. Here’s where problems typically emerge.
Document preparation suffers when source data is scattered across systems or contains errors. Regulatory specialists spend enormous time tracking down correct information, verifying accuracy, and manually reformatting content for eCTD requirements. Studies suggest data preparation accounts for roughly 80% of the work that goes into regulatory projects.
Submission rejections happen when documents contain errors or don’t meet formatting standards. Each rejection triggers rework cycles that can stretch for weeks. The cost isn’t just the time spent fixing problems. It’s the compounding delay in getting your product to patients.
Audit failures create significant exposure. Regulatory bodies like Health Canada and the FDA conduct inspections where data integrity is front and center. In 2023, the FDA issued over 1,150 warning letters for pharmaceutical non-compliance. The average cost of a compliance violation in 2025 sits around $14.8 million per incident.
Cross-functional miscommunication happens when different teams work from different versions of the truth. Clinical research generates data that regulatory affairs interprets, which manufacturing then implements. When these handoffs involve inconsistent data, errors multiply.
How to Calculate the Cost of Bad Data at Your Organization
Calculating data quality costs requires looking at both direct expenses and hidden impacts. Here’s a practical framework.
Start by measuring time spent on data issues. Track how many hours your team spends each week cleaning data, reconciling discrepancies, and fixing errors. Multiply by fully loaded labor costs. If your regulatory affairs specialists spend 20% of their time on data cleanup, that’s 20% of their salary going toward fixing problems rather than creating value.
Next, count your data incidents. Review the past 12 months. How many times did a data quality issue cause a significant problem? This might include submission delays, audit findings, rework requests, or compliance issues. Estimate the cost of each incident, including direct remediation costs, opportunity costs from delays, and any penalties incurred.
Then look at your time to detection. When data problems occur, how long before someone notices? In many organizations, the answer is days or weeks. Some issues only surface when a downstream consumer, like a regulatory reviewer, flags something that looks wrong. The longer detection takes, the more expensive the fix becomes. Industry analysis suggests that catching errors early in data collection costs a fraction of what it costs to fix them once they’ve propagated through your systems.
Finally, assess decision impact. Consider decisions made using potentially flawed data. Were forecasts wrong? Did product launches miss their windows? Were resources misallocated based on faulty analytics? These costs are harder to pin down but often dwarf the more visible expenses.
A Practical Formula for Data Quality ROI
Once you’ve quantified the cost of bad data, calculating ROI on quality improvements becomes straightforward.
The basic formula is: ROI = (Benefits Gained – Investment Made) / Investment Made
For data quality specifically, a useful variation is: Data Quality ROI = (Value Created – Data Downtime Costs) / Total Data Investment
Let’s break this down with concrete categories.
Benefits gained might include reduced time spent on data cleanup and error correction, fewer submission rejections and associated rework, faster regulatory approval timelines, reduced audit findings and compliance penalties, and better decision-making from reliable analytics.
Investment made typically covers technology solutions like data quality tools and platforms, process improvements and workflow changes, training programs for staff, ongoing maintenance and governance costs.
Here’s a worked example. Say your regulatory team currently spends $400,000 annually on activities directly tied to fixing data problems, including manual reconciliation, error correction, and submission rework. You invest $150,000 in a data quality and automation platform plus $50,000 in process improvements.
After implementation, data-related issues drop by 70%. Your annual cost falls to $120,000, a savings of $280,000.
ROI calculation: ($280,000 – $200,000) / $200,000 = 40%
That’s a 40% return in the first year. And because many benefits compound, subsequent years often show even better returns as the organization gets better at maintaining data quality.
Key Metrics to Track
Beyond the headline ROI number, a few specific metrics help demonstrate ongoing value.
Time to detection measures how quickly your organization identifies data quality issues. Best-in-class teams catch problems in minutes or hours. Many organizations take days, weeks, or even months. Reducing this metric directly correlates with lower remediation costs.
Time to resolution tracks how long it takes to fix issues once detected. Faster resolution means less disruption to downstream processes.
Incident frequency counts how often data quality issues occur. A declining trend indicates your preventive measures are working.
Submission cycle time measures the elapsed time from starting a regulatory submission to completing it. Improving data quality should compress this timeline.
First-pass acceptance rate tracks what percentage of submissions are accepted without requests for additional information or corrections. Higher rates indicate cleaner data feeding into your submission process.
Why Automation Changes the ROI Equation
Manual data quality efforts hit a ceiling. You can only throw so many people at the problem before costs become prohibitive. Automation fundamentally changes this dynamic.
AI-powered platforms can analyze documents at speeds impossible for humans to match. They catch inconsistencies, flag potential errors, and ensure formatting compliance without requiring constant human oversight.
For regulatory submissions specifically, automation delivers several advantages. Robotic process automation handles repetitive data entry tasks that are prone to human error. Natural language processing extracts key information from source documents, reducing manual transcription. Validation rules check data against regulatory requirements before submission, catching problems early. Integration across systems ensures everyone works from consistent data rather than maintaining separate copies.
The ROI on automation often exceeds manual approaches because the marginal cost of processing additional data is nearly zero once systems are in place.
Making the Business Case to Leadership
When presenting data quality ROI to executives, a few principles help your case land effectively.
Lead with the cost of inaction. Frame the conversation around what bad data is already costing the organization. This is money already being spent, often invisibly. Improving data quality isn’t creating new expenses. It’s redirecting existing waste toward productive outcomes.
Use concrete examples. Abstract percentages are less compelling than specific incidents. If a data error caused a three-month delay on your last submission, quantify what that cost. Specific stories resonate more than aggregate statistics.
Connect to strategic priorities. Data quality improvements aren’t an end in themselves. They enable faster regulatory approvals, reduced compliance risk, better resource allocation, and ultimately, getting life-saving products to patients sooner. Frame your case in terms of what leadership already cares about.
Start small and prove value. Rather than proposing a massive overhaul, identify a high-impact area where you can demonstrate results quickly. Early wins build credibility for larger initiatives.
Account for soft benefits alongside hard numbers. While your quantitative ROI calculation forms the backbone of your case, acknowledge the less tangible benefits too. These include improved team morale (nobody likes spending their days fixing errors), better stakeholder confidence in your data, and reduced risk exposure that may never materialize as an incident but represents real value.
What Good Looks Like
Organizations that have tackled data quality systematically report meaningful improvements. Reduction in quality-related costs of 20% or more is achievable. Submission cycle times can compress significantly. Audit readiness improves when data integrity is built into daily operations rather than scrambled together before inspections.
The pharmaceutical companies that navigate regulatory complexity most efficiently treat data quality as an ongoing discipline, not a one-time project. They invest in tools that maintain quality proactively rather than relying on heroic cleanup efforts before each deadline.
For companies working with Health Canada, the FDA, or other regulatory authorities, the stakes are too high to ignore data quality. The question isn’t whether bad data is costing you money. It’s how much you’re losing and what you’re going to do about it.
Ready to see how automation can improve your regulatory data quality? Request a demo to learn how RoboReg’s AI-powered platform helps pharmaceutical and medical device companies reduce errors, accelerate submissions, and maintain compliance.
