How to monitor and improve marketing data quality with Supermetrics

You're staring at your dashboard on Monday morning, and something feels off. The conversion numbers from Google Ads don't match what you're seeing in your CRM, or Meta is reporting great performance, but your actual revenue doesn't match that. Your team is about to make budget decisions based on these numbers, and you're not sure which ones to trust.

Poor marketing data quality is a quiet force undermining your marketing efforts, but luckily, there are proven ways to turn your data into your biggest competitive advantage using Supermetrics.

Key takeaways

• Poor data quality paralyzes teams and can waste you millions: When you can’t trust their numbers, teams either freeze up or abandon data entirely, resorting to gut-feeling decisions that alone can lead to serious budget misallocations.
• Design systems to catch errors fast, not eliminate them: You can’t automate away all mistakes, but you can design systems to surface them quickly. With Supermetrics’ Marketing Intelligence Platform, you can set up automated checks for naming conventions and UTM tags so issues are caught before they affect reporting or MMM analysis.
• Someone must own data quality, or it becomes no one’s responsibility: Assign a specific person (Marketing Ops/Analyst) to establish processes, documentation, and regular audits. It can't be “everyone’s job”.
• Centralize and standardize your data across platforms: Use platforms like Supermetrics to pull scattered data into one place with consistent naming conventions and metric definitions, eliminating the confusion of platform-specific terminology.

When data quality goes wrong, everything goes wrong

It's not uncommon to see companies make million-dollar mistakes because of poor data quality. Imagine reallocating your entire Q4 budget based on faulty attribution data, or launching a campaign targeting the wrong audience because your customer segments were corrupted.

Here's a real example: A company doing marketing mix modeling for a client completely excluded an entire media channel from their analysis. The data existed, but the team assumed it wasn't available. That "missing" channel turned out to represent a surprisingly large portion of the media mix, which meant all their recommendations were based on fundamentally incomplete information.

Poor data quality creates a vicious cycle. When people can't trust the data, they either become paralyzed by inconsistency or abandon data entirely and revert to gut feelings. Teams start doing manual spot checks, creating Excel spreadsheets to verify campaign names, and constantly asking, "Can you check this?". Instead of optimizing campaigns, teams are forced to constantly firefight data issues.

The sweet spot is data-informed decision-making—not blind faith in numbers, but not complete rejection, either. Data should be used a bit like GPS navigation: you follow the directions, but you don't drive into a lake just because the map says to.

Red flags that signal data quality problems

Here’s a checklist you can use to spot some of the warning signs that the data might have quality issues:

• No naming conventions: Usually indicates there's no data strategy at all
• One-size-fits-all dashboards: While there should be a single source of truth, hundreds of KPIs crammed into one view serve no one well
• Data that's suspiciously consistent: For example, CPMs should fluctuate with seasonality; if they're always the same, something's off
• Always confirming results: If your performance metrics always say "Yes, boss" the algorithms might be gaming the metrics
• Oddly rounded numbers: Spending exactly $10,000 is more likely a data error than actual performance
• Unused dashboards: If things are surprisingly quiet around your reports—ask why, maybe there is a surprisingly simple answer to why people are not using them, and an equally simple solution

What good marketing data quality actually looks like

Good marketing data quality shouldn't be about perfection, but rather about reliability and usefulness. When the data quality is solid, you know the information is accurate and you understand where it comes from. Are you pulling conversions from Google Analytics 4, your CRM, or individual ad platforms? Everyone on your team should know the answer.

Good data should also be accessible. In day-to-day terms, this means that your team understands why naming conventions matter (without them, your website traffic shows up as "direct" visits instead of being attributed to your banner campaigns). They should also know where to go when they need additional information.

You're probably not going to have a 100% complete picture anywhere, but you should aim for 99% and document what's missing. The key is being aware of the gaps rather than discovering them when it's already too late.

Really good data challenges your assumptions. It should push you to question short-term thinking and test new hypotheses. If your data always confirms exactly what you want to hear, something's probably wrong, and this is the moment you should pause and investigate.

5 most common marketing data quality issues and how to solve them

Marketing data quality issues usually don’t start with the tools—they start with unclear processes, human shortcuts, and lack of oversight. Here are five of the most common problems we see—and how to solve them so your team can move faster, with more confidence.

1. Human error

Problem: Manual steps can still break your data

There's this myth that you can automate everything, set it up once, and it's correct forever. Sounds great, but it's not how it works. There will always be some manual elements in the game, especially around naming conventions. For example, someone needs to put the UTM parameters in, and if they don't follow the naming conventions, that's an easy way for human errors to slip in.

Solution: Automated quality checks and alerts

Instead of trying to eliminate human error, design your system with Supermetrics to catch it fast.

1. Automate naming convention checks

• Use Custom Fields to apply logic that flags campaigns not following your naming conventions.
• Break campaign names into parts using the ‘split text and pick part’ function, making inconsistencies easy to detect.

2. Automate reporting and alerts

• Pull data into a dashboard (e.g., Looker Studio) for ongoing monitoring.
• Build the following tables:
  • Campaign names table: All campaign names across platforms and flags improperly named campaigns.
  • Misnamed campaigns: A filtered list of only the campaigns that violate naming conventions.
  • UTM conventions tracking: Checks UTM tracking accuracy for campaign URLs.
  • Misnamed destination URLs: Isolates URLs with broken or improperly structured UTM parameters.

This setup helps your team spot and fix issues before they disrupt reporting, ensuring cleaner data and faster, more reliable analysis.

2. Missing or incomplete data

Problem: Teams give up on hard-to-get data, then forget it's missing

Teams often take the easy road when data is difficult to obtain. "We don't have that data because it's too hard to get" might become the default response. Then someone forgets that the data is missing, and the team ends up making decisions with incomplete information.

Most of the time, you won't get 100% of everything. But you should document what you don't have and why. Instead of cutting corners, make a conscious decision about whether getting certain data is worth the time investment.

Solution: Make missing data visible and actionable

Let’s say you want to do marketing mix modeling (MMM), but preparing MMM data is a time-consuming process, which could take up to five people and 1000 hours just to clean the data. One of the main issues is identifying campaigns that didn't follow your naming conventions. Here’s where you can use Supermetrics to monitor your data:

1. Automate naming convention check:

• Create a custom field in Supermetrics to automatically flag campaigns with improper naming conventions.
• Then, use the ‘split text and pick part’ function to break down campaign names into parts, making it easier to detect inconsistencies.

2. Automate reporting and alerts:

• Once you’ve set up all the logic, pull data into a dashboard, for example, in this case, Looker Studio.
• Build two tables:
  • Campaign names table: pulled campaign names across platforms, including the custom field that flagged improper naming.
  • UTM parameters table: listed destination URLs and UTM parameters, with alerts for incorrectly structured UTM tags.

This will help you automatically monitor your marketing data quality, catch errors faster, and run more frequent MMM cycle.

3. Too much or too little detail skewing decisions

Problem: Too much or too little detail = unusable

Marketers often "hoard" data they don't use, making it impossible to find meaningful insights because everything might look equally important. In this scenario, you either have too little detail to make good decisions, or so much granularity that you can't see the forest for the trees. It clutters dashboards, skews analysis, and creates confusion—because teams end up working with different levels of detail, often without realizing it.

Solution: Match granularity to the use case

With Supermetrics, you can extract the exact KPIs you need from each platform and group campaigns using naming conventions or other logic. Then, in your preferred reporting tool—like Looker Studio, Google Sheets, or Power BI—you build tailored dashboards for each role.

This way, the CFO gets a high-level ROI snapshot, the campaign team gets creative-level performance. Everyone sees the data that matters to them—nothing more, nothing less.

4. Broken naming conventions

Problem: Naming conventions look good on paper—but break in reality

By now, we’ve fully grasped that naming conventions: are essential, but someone always has to manually input the names. You might have a perfect template with dropdown menus—country, campaign type, theme—but at the end of the day, a human being needs to remember to use it correctly, and even with a documented system, an error can creep in.

I've seen companies spend 1,000 man-hours on a single marketing mix modeling project just going through campaign names to make sure they're correct. That's not sustainable.

Solution: Turn templates into actionable guardrails

Even the best naming systems fail without adequate adoption. That’s why we strongly recommend building on a proven campaign naming convention template—like this campaign name generator template for Google Sheets. It outlines naming structures that work across platforms and helps teams align on what “good” actually looks like.

Once you have the template in place, you can bring it to life in your reporting setup. Use it to:

• Standardize naming logic across Meta, Google Ads, and LinkedIn
• Train new team members on campaign setup conventions
• Audit historical campaigns and catch inconsistencies early

And if you want to go a step further, Supermetrics helps you monitor naming compliance automatically—so broken naming doesn’t derail MMM analysis or reporting deadlines.

5. Lack of processes and ownership

Problem: When no one owns data quality, things fall apart

“It’s everyone’s job” usually means no one actually does it. Without clear ownership, issues get patched reactively, and it becomes harder to really trust your data. Unfortunately, in the no-ownership scenario, problems only surface when it’s already too late.

Solution: Make ownership visible and build repeatable habits

Supermetrics helps you embed accountability into daily workflows—without adding layers of manual work.

Start by assigning a dedicated owner: ideally, this would be someone like a marketing analyst or marketing effectiveness manager who’s responsible for QA and process hygiene. Then:

• Use field-level notes in dashboards to document changes and flag any changes that are hard to understand without additional context
• Set up automated dashboards to catch errors
• Trigger notifications for missing or malformed naming conventions
• Run quarterly "myth-busting" audits to challenge assumptions hiding in your reports

When everyone knows what's being tracked, why it matters, and who’s on the hook, data quality becomes proactive instead of reactive.

Here is how VodafoneZiggo uses Supermetrics for better data management and marketing efficiency

3 habits to make your data quality sustainable

Start with simple routines

You don’t need a complex framework to start improving data quality. Begin with the highest-impact areas—naming conventions, UTM tagging, and budget pacing—and build simple weekly checks. Supermetrics helps automate the heavy lifting, so maintenance becomes part of your workflow.

Build buy-in through training

People care more when they understand the “why.” Show how bad UTMs break attribution, or how inconsistent naming makes spend disappear from dashboards. Use your own data to make training sessions relevant—and, very importantly, bake data quality into onboarding.49% of marketers say upskilling would improve their data quality—proof that this is often a people problem, not a tooling one.

Balance speed with reliability

Perfect data isn’t the goal—trustworthy data is. It’s okay to go fast, but make sure your core decision-making metrics are validated. Teach your team to question anomalies and trace issues down to the root, not just fix the obvious symptom.

Finally, remember: tools are great, but habits take you over the finish line. Automate what you can, yes—but also agree on who owns what and when checks happen. We’re not chasing 100% accuracy, but rather creating enough clarity and consistency to move forward with full confidence.

Do you want to see how real teams are making this work? Check out our on-demand webinar where we walk through examples of building scalable data quality practices—without overengineering.

Your move

Data you can’t rely on doesn’t have to be your default.

With the right setup, you can spot broken campaigns early, automate naming QA, and actually trust your dashboards—without turning your team into full-time data janitors.

Supermetrics helps you make sense of messy data. From Custom Fields to pacing alerts to MMM dashboards, all the tools you need are there—ready when you are.