The business impacts of data quality issues—what they look like and how to think about fixes

For leaders in product analytics or product management, data is the backbone of every decision, strategy, and innovation. But what happens when that data is unreliable? Expensive data quality issues aren’t just inconvenient—they’re silently eroding trust, delaying progress, and undermining your ability to deliver results.

If you’ve ever questioned the accuracy of your metrics or seen your team struggle with inconsistent tracking, you know the stakes are high. The path to better planning and confident decisions begins with addressing these challenges head-on. Consider these familiar scenarios:

These pain points aren’t just operational nuisances—they’re barriers to growth and innovation. But here’s the good news: You can fix them.

High-quality data isn’t just about accuracy; it’s about enabling your teams to move faster, collaborate smarter, and make decisions with confidence. With tools like Mixpanel and Avo, you can standardize tracking, uncover insights in real time, and create a culture of accountability around data quality.

In this blog, we’ll highlight six root causes for data quality issues, and three essential dos and don’ts so you can:

By confronting your data quality challenges, you’ll unlock the full potential of your analytics, enabling your team to plan smarter, execute better, and lead with confidence. It's time to take charge of your data and drive the impact your organization needs.

If you can relate to some of the above symptoms then here’s the diagnosis: You’re suffering from poor data quality, and you need treatment. We’re hoping this blog and our upcoming workshops with Mixpanel can help you in your work to improve your data quality. 

We’ve learned it’s not as easy as just jumping in to implement solutions. There’s a hidden first step, which is allowing ourselves to face the severity of the problem. That will be a large focus of this post. We will guide you through finally facing your data quality issues, shed light on the implications, and help you build your case for taking action.

But for now, let’s start at the beginning: defining what great data looks like, and how we can start the journey towards it. 

“Data quality” is quite abstract. What does amazing data quality even look like? We need a tangible, relatable understanding of what defines “good data quality”  to move forward.

We all strive for data that is accurate, consistent, reliable, and fit for its purpose. We need data we can effectively use for analysis, decision-making, and operational tasks. And in today’s world, that includes data we can reliably build AI products on to drive value and revenue.

But let’s make one thing clear: We should never be striving for perfect. Perfect is the enemy of good. You will always have some data quality issues. However, too many choose to ignore all of them. We get stuck trying to live with them, with the additional cost of dealing with it downstream and risking bad decision-making. But the consequences of data quality issues are too significant to ignore. The most important thing is to realize what the business impact is in your case.

You might be thinking, “Our data isn’t perfect, but what’s the real impact?” Or perhaps you recognize the challenges, but the scale of the problem feels overwhelming, making it easier to accept the status quo.

The reality is your data issues are likely more pervasive—and costly—than you realize. These challenges don’t just impact accuracy; they disrupt decision-making, slow innovation, and ultimately drain resources across the business. From missed opportunities to wasted spend, the cost of subpar data adds up in ways that directly affect your bottom line and long-term strategy.

While the challenges may seem daunting, there’s a clear path forward. Recognizing the critical importance of data quality is the first step—and once that shift happens, you can begin addressing the root causes with a focused, systematic approach. Let’s dive into some of the most common data quality issues that could be hindering your organization’s performance and explore actionable strategies to resolve them.

Let’s shed light on themes of issues to help us identify which ones we have. Below are (overlapping) themes of data quality issues with examples, so you can start looking for signals:

Over tracking: This is basically tracking for product “coverage” rather than data use cases. It results in excessive or irrelevant events are collected, which overwhelms your data consumers and your data infrastructure, and magnifies all other issues:

Usability issues: The data is difficult to use or access, causing a lack of data literacy which blocks you from democratizing data via self-serve analytics (a great read on this by our friend Glenn Vanderlinden):

Incomplete data: Something is missing in the data you’re collecting, such that it gives an incomplete picture: 

Inconsistent data: Your events and properties have inconsistencies in naming, typing, or even the actions they represent depending on the product, platform, event, or codepath:

Duplicate data: Data duplication or even multiplication is happening on some level:

Inaccurate data: The data doesn’t match your expectations - and might even be totally impossible given the constraints of reality:

Sequencing issues: When tracking actions are made in an unexpected order compared to user and/or system actions:

Misconfiguration issues: Faulty integrations or unexpected configurations can cause issues at any point in an event’s journey. The further upstream the data issue is, the more magnified the implication is:

Anyone who has created a chart before will for sure have experienced at least one of these issues. It’s a gut-wrenching feeling—when you’re sharing an amazing win or a fantastic opportunity with your CEO, but they immediately doubt the data. You go back to your desk with your tail between your legs, do a little digging and find out that they’re right. The data wasn’t reliable. 

If you relate to any of the sentences at the top of this post, or identify with some of the examples above, you know you have data quality issues. And if you don’t relate to any of this, please reach out and tell us your secret (or talk to your most senior data analyst and they will prove you wrong 😏). The next step is to understand the root causes of these issues. 

Failure to acknowledge the importance of data and data quality in your business strategy leads to lack of ownership, processes, documentation and/or tools, which leads to data quality issues.

Measuring product success is essential and should be a part of the strategy from the beginning. But collecting the data needed to measure success is too often treated as a side project. It’s considered as a chore that needs to be checked off the list in order to ship the feature. A chore done for some analyst, rather than as an impactful tool to make good decisions. We end up rushing and, as a result, track irrelevant events, design suboptimal data structures and make mistakes during implementation.

Ask yourself: Where do we stand with the following:

These are all crucial for fixing the problem at the root, and anything less than fixing at the root will leave us in reactive damage control.

The trick is to break down the challenge into manageable chunks.

Data quality needs to be addressed at the root. But first, you have to face your issues and build your case to the team on why. Then you build systems to prevent new issues from adding to the mess. Finally, prioritize historical issues to fix, one at a time. Here's what the beginning of that process looks like (full practical steps deserve a blog of their own):

Don’t: Get stuck in the “data quality for the sake of it” trap: Pick your battles. Focus on issues that have business implications.

Do: Make the problem visible and help everyone understand how much time, money and sanity improving data quality will save. Use the resources from this blog post to identify the business impact and themes of data quality issues of your own data. You’ll start seeing patterns of problems you can tie to business impact.

This will help you get buy-in from management and other teams to pursue the path to better data.

Don’t: Delay preventative measures: not being ready to fix existing issues does not mean that you can’t start preventing new ones.

Do: Start preventing future issues today, even while you still have messy data: Tackle the root causes of your data quality issues to prevent more from surfacing. You do that by addressing the root causes:

As we discuss in our recent series on data mesh for event data quality at scale, the goal is to establish federated governance with domain-oriented ownership. This means the goal is not to have a central governor of data quality. Instead, we strive for a federated team that defines what constitutes quality, while the domain teams own their own data quality.

Don’t: Try to fix “all the issues.” Prioritizing all means you’re prioritizing none.

Do: Focus on what’s most impactful for your business, for example important events that revenue operations depend on.

Start with an audit. Establish an overview over your current state of event data, highlighting the data quality issues. By adding some high level information about each issue, you can measure the impact. Useful information is for example

You’ll learn a lot from this, and more importantly, you can prioritize and triage your issues, deciding which to fix and which you will ignore for now and find a way to live with.

Once data reliability is established, Mixpanel becomes a game-changer for your team. It’s an intuitive, time-saving solution that empowers your entire organization to make informed decisions faster. Without Mixpanel, key players often depend on data experts to find answers, creating bottlenecks. Mixpanel eliminates that dependency by enabling everyone to access meaningful insights directly. As a leader, you’ll equip your team to move faster, collaborate better, and drive successful product outcomes with confidence.

Want to learn more?

Join our upcoming webinar: Fix Your Data, Empower Your Decisions: Data Quality Series for Product Leaders, where you can connect with like-minded individuals and get actionable advice on how to tackle broken data.

Found this helpful? Terrible advice? Tell us. We love to knowledge-share and we can talk about data cultures and data quality all day, every day. Find me on Linkedin, join our community, or subscribe here to find out when we at Avo publish more content like this for you.

Special thank you to Thora Gudfinnsdottir, Michelle Han, Kalina Bryant, Timo Dechau and Glenn Vanderlinden for their contributions to the ideas in this post.