The Cost of Bad Data Quality: Key Stats Every Business Should Know

Overview

The term ‘bad data’ might seem exaggerated to some, almost anthropomorphized, but the reality is that customer data is about real people. When that data is incorrect, it can severely impact your business's success.

We’ve all heard the phrase, “data is the new oil,” and just like oil, data must be refined before it’s useful. In other words, inaccurate data can cost companies big time.

A new study from EDQ reveals that,

Inaccurate data affects the bottom line for 88% of businesses, with an average revenue loss of 12%.

This loss stems from three main sources:

• Wasted marketing budgets.

• Inefficient resource allocation.

• Wasted staff time.

Here’s a summary of the data we’ve collected related to these three factors:

• 38% of businesses manually verify data using Excel.

• 23% of businesses rely exclusively on manual data checks.

• 22% of businesses report inaccuracies in their customer data.

• 80% of businesses report that at least 30% all customer records contain errors or missing information and about 20% of their entire database is dirty.

In this post, we’ll explore some surprising statistics from the study, along with insights gathered from our extensive work with clients over the years, showing the tangible impact of poor data quality.

The Hidden Costs of Bad Data

That 12% lost revenue only scratches the surface. According to the study,

Poor data quality accounts for 20% of all business process costs.

Approximately 28% of companies encounter email delivery problems, and 21% have experienced reputational damage. The risk isn't just about improving conversions—it’s about protecting your brand from the fallout of sending communications to incorrect or outdated contacts. This risk is especially high in the U.S., where ISPs are increasingly stringent on inaccurate email lists, and in markets like India, where consumers are highly sensitive to brand reputation and trust.

A Growing Trend in Marketing

Improving the quality of customer data is now a top priority for many companies, with CRM systems, marketing automation, customer experience management, and analytics packages all becoming critical areas of focus.

The Experian survey gathered responses from over 1,200 organizations across the UK, US, and Europe, spanning various industries and company sizes. It covered how businesses collect, manage, and deal with the consequences of poor-quality data.

Why Does Data Quality Matter?

Where do companies collect data?

Here’s a quick snapshot of where companies collect their customer data:

• 73% gather data from their website.

• 60% rely on face-to-face sales teams.

• 54% collect data through call centers.

• 47% capture data via mobile websites or apps.

Despite these channels, many businesses struggle to maintain high data quality. Surprisingly, 38% still manually check data in Excel spreadsheets, and 23% rely solely on manual checks to ensure the accuracy of their contact records.

The Scale of the Problem

On average, businesses estimate that 22% of their contact data is inaccurate, a sharp increase from 17% the previous year. Marketing and sales professionals believe the issue is even more severe, with over 30% of their records containing errors.

This is particularly problematic for multichannel marketing, with 42% citing inaccurate contact data as the biggest obstacle.

The study also highlights the impact of bad data on email marketing. 67% of companies report problems with email bounce rates, while more than 70% of loyalty programs face challenges due to inaccurate customer information, with 34% citing it as the primary cause.

A Company Problem – Not Just an IT Problem

Even just a few years ago, in 2017, the looming spectre of bad data was apparent. Gartner surveyed a wide range of companies in its study and learned that data quality costs them over $14 million dollars annually, with the average cost of maintaining bad data around $100 per record.

Now imagine how much more connected we are today and you can see how the problem could compound exponentially. This article from SAP highlights how bad data costs the U.S. a staggering $3 trillion annually.

Many companies, in an attempt to wrangle departments to make sense of it all, place the task of organizing and managing all this information squarely on IT’s shoulders. But bad data affects more than just servers and databases – it affects everyone. In this day and age, it is very much a business problem.

And that’s not even factoring in the cost beyond customer data. A few inaccuracies in customer names or details is one thing. But oftentimes, depending on the company culture in relation to data upkeep, it can affect other areas of business as well – productivity, security and making cost effective decisions.

And just as the cost of bad data can continue to multiply year over year, the savings from having accurate data are just as big (if not more-so), as noted in this chart from RingLead:

In short, this is not a problem we can continue to throw money at and hope it goes away or works itself out.

What a Difference Clean Data Can Make!

Of course, cost savings are one thing, but oftentimes management (and other executives) don’t just want savings – they want to see a direct correlation in terms of revenue as well. The real question is, how much can clean data make for us? Here’s a hypothetical (albeit very realistic) example from the same Ringlead chart:

And in addition to revenues and savings, the benefits of clean data go much farther. With greater data reliability comes greater credibility and a stronger decision-making foundation backed by data. Reports become more accurate. Customers respond to more accurate personalization. All departments enjoy greater productivity and efficiency. It’s a cycle of wins.

So as you can see, a few inaccurate records or non-standardized entries don’t seem like a big problem, but as your business scales, more and more information becomes fragmented and fraught with issues. Costs escalate. Efficiency plummets. But by the same token, by spending a little now, you reap far greater benefits over time. And any campaign started or improved based on solid, reliable information is one you can look to time and time again for greater insights and metrics that count.

Good Data Rocks Your World

It turns out that good data lets your company:

• Prospect and target new customers

• Identify cross-sell and upsell opportunities

• Gain account insights

• Increase efficiency

• Retrieve the right info fast

• Build trust with customers

• Increase adoption by reps

• Plan and align territories better

• Score and route leads faster

  
  

The list goes on and on. You check out some case studies on the benefits of good data.

You start to picture what good data could do for your company. A slow-motion movie montage plays in your head. You envision reps joyously searching accurate and up-to-date records for the contact info needed to convert a lead. Managers spontaneously forming a conga line after realizing how easy it is to align territories and identify new markets now that all records have complete industry and competitive information. Executives lining up to shake your hand after viewing one of your dashboards. All these possibilities don’t create world peace, but they come pretty close.

And What About AI Data?

Data quality plays a major role in shaping the outcomes and reliability of AI systems. When it comes to using AI, poor data quality can compromise the system’s performance, leading to biased outcomes, vulnerabilities, and even ethical and societal repercussions. Messy or unrepresentative data can lead to misguided business decisions and increased operational costs.

High-quality data ensures that AI models are accurate, unbiased, and robust in their predictions. It allows these models to better predict real-world scenarios and fosters trustworthiness in their applications. From an economic perspective, using high-quality data can maximize the return on investment in AI initiatives.

Adherence to data quality standards is vital for compliance with evolving global data regulations. In essence, the foundation of any effective AI system lies in the integrity and quality of its data.

Conclusion

Bad data can severely impact businesses, leading to wasted marketing budgets, inefficiencies, and reputational damage. To avoid these losses, companies need to invest in automated data management, regular data cleaning, and improved collection practices. In today's data-driven world, maintaining accurate customer information is key to boosting marketing effectiveness and business growth.

If your company is struggling with bad data, it’s time to take action. We can help you implement the right tools and strategies to clean up your data, streamline your operations, and drive measurable results. Don’t let bad data hold you back—let’s turn it into an opportunity for success. Contact us today!