Your marketing metrics are lying – why data hygiene is key

Have you ever worried that the strategy you signed off on might be faltering — not because it’s flawed but because your marketing metrics are lying to you? 

As CMO, the weight of every campaign, budget request and marketing decision rests squarely on your shoulders. However, you need clean data to make sound decisions. That’s why, if there’s one thing keeping CMOs up at night, it’s bad data. Over 60% of marketing leaders are, at most, moderately confident in their data.

If you can’t trust your data, how can you feel confident in your strategy, your budget or your vision?

The reality is that without clean data, you can’t put that much trust in your analytics. That means all the issues on your shoulders, like justifying your marketing budget to your C-suite, navigating cross-channel campaigns successfully and having to respond to increasing demands for ROI, are going to continue to feel like burdens. It’s only through good data hygiene that you can turn them into opportunities to shine.

Why you can't trust your marketing metrics 

You have data. Lots of it. But distortion, duplication and inconsistency make it unreliable. That means figuring out what actually drives sales isn’t as simple as pulling a report. If you can’t trust your numbers, you can’t defend your budget or make strategic decisions that fuel growth.

Why dirty data is dangerous

Beyond muddying decision-making, dirty data also comes with other problems like: 

• Flawed tracking: Broken codes and bugs create incomplete metrics.
• Duplicate records: Inflated numbers make weak campaigns look strong.
• Inconsistent methods: Comparing unstandardized data leads to chaos.
• Missing insights: Siloed data hides the full conversion path.
• Faulty attribution: Miscrediting touchpoints skews performance analysis.
• Superficial metrics: Clicks don’t equal revenue growth.
• Unverified sources: Bad data leads to bad decisions.

If you sweep dirty data under the rug, you might also subject your business and your professional reputation to serious issues, such as the following: 

• Compliance risks: Inaccurate tracking can lead to hefty fines.
• Wasted budgets: Misleading insights drain ad spend.
• Missed opportunities: Poor segmentation blocks high-impact strategies.
• Brand damage: Duplicate or irrelevant messaging erodes trust.
• Leadership credibility: Stakeholders lose confidence in bad data-driven decisions.

The bottom line is that dirty data is an operational issue and a leadership risk. In today’s market, guesswork isn’t an option. Unclean data can erode up to 20% of an organization’s revenue while opportunities slip away. 

The fix: automated data management. You need a system that continuously cleans data, eliminating discrepancies before they create confusion. Reliable insights mean smarter decisions, optimized spend and the confidence to drive real growth.

How data hygiene keeps your metrics honest

Good data hygiene in marketing is all about keeping your data clean, accurate and reliable. Think of it like brushing your teeth. You want to keep things fresh, clean and free of anything that could cause bigger problems later.

Why is data hygiene important? 

Ensuring your data is clean is the foundation of smart decisions and effective strategies. Accurate, consistent and up-to-date, it ensures your insights are trustworthy and actionable.

Exactly what is clean data?

Clean data is data that’s free from duplicates, errors and missing values. Properly formatted and organized, clean data eliminates outliers that skew analysis and keeps your dashboards reliable. Without it, you’re flying blind.

Cleaning data has four steps:

• Data cleansing: Like spring cleaning for your data, data cleansing spots and fixes errors such as typos, missing info or outdated values. For example, this process might identify outdated email addresses in your CRM, helping to improve your nurture campaigns.

• Deduplication: This process removes repeated customer records that skew campaign results. If one customer record shows up twice, you might see an inflated ROI for a campaign. Worse, the same customer might receive duplicate messages, causing them to feel frustrated with your brand.

• Standardization: This process makes sure data is in the same format across platforms. Standardization is important because inconsistent date formats or phone numbers can disrupt SMS campaigns or make reporting chaotic. Imagine trying to measure SMS ROI when half your audience didn’t receive the message due to formatting errors.

• Data validation: This step acts as a checkpoint to confirm data meets necessary rules, such as ensuring emails are deliverable or customer ages fall within a target demographic. It helps prevent issues such as invalid email addresses resulting in wasted ad spend on retargeting efforts that will never reach the intended recipients.

By incorporating these steps into your data processes, you not only improve your data quality but also unlock better insights and campaign results. All of this ties into data governance, a set of rules that ensures data hygiene is part of your daily operations.

When your data is clean, it’s also easier to analyze and drives faster, smarter decisions. And it’s essential for growth-focused CMOs. Clean data offers:

• Time savings: Sales and marketing teams lose up to 32% of their time fixing data issues. Data hygiene gives you this time back. 
• Accurate analytics: You can count on more reliable insights to optimize campaigns and measure performance effectively.
• Reduced budget waste: Bad data squanders 21% of media budgets, leading to poor targeting and wasted spend.
• Revenue protection: Poor data quality costs businesses $12.9M annually, on average — per company! Data cleansing in marketing helps to reduce these potential losses.
• Team alignment: Clean data ensures everyone works from the same source of truth, improving collaboration and execution.

Clean data keeps your marketing strategy on track, prevents wasted resources and protects your bottom line. Without it, your campaigns are only as reliable as the bad data driving them.

A dirty-data tale from a leading performance marketer

Lee Riley, a seasoned performance marketer, shares a cautionary tale from a major retail brand that should make any marketer sit up and take notice. This company leaned heavily on Google Analytics (GA) to gauge the impact of its digital channels. They knew GA’s data wasn’t perfect — it was skewed and incomplete — but they stuck with it anyway. Why? It was simple, clean and easy to present to executives. No need for messy, complicated conversations about attribution.

But here’s the kicker: they also completely missed a glaring issue. The lookback window in GA was set to 90 days. For those unfamiliar, this setting decides how far back GA will look to attribute credit for a conversion. Ninety days? That’s outrageously long. For comparison, 28 days would’ve been more realistic. Meta’s standard? Just 7 days. This overgenerous setup ended up “gifting” paid search a whopping 40% more conversions and revenue than it actually deserved.

Why was this problematic? GA is biased to its own tech, so paid search campaigns got the lion’s share of the budget — even when their performance likely didn’t justify it. Meanwhile, other full funnel channels like social media were under-credited and risked having budgets slashed. Promising channels, with untapped incremental customers and revenue, were starved of resources and potential strategic changes, while paid search soaked up investment, especially in tougher times, based on skewed data. The ripple effect was simple: strategic inefficiencies, more limited growth opportunities and more stunted conversations between correlative teams, both marketing or otherwise.

By the time the team caught on, the 'damage' was done, aka, opinions on each channel's value were set and change would be more difficult. Resources had been wasted, opportunities missed and channels that could’ve delivered real results were unfairly blamed. Data would have to be reassessed and those not-so-easy attribution conversations with executives would have to happen anyway

Key insight: Never blindly trust your attribution models. If you’re working with flawed data, you’re setting yourself up for bad decisions and the cost can be staggering. Clean, accurate data and strong attribution strategies aren’t optional; they’re the backbone of successful, scalable marketing.

Automated data hygiene is a critical strategic advantage for CMOs

Automation isn’t just a tool to make cleaning data easier; it’s your competitive advantage. For strategic CMOs, it’s the secret to staying ahead of the competition where every second and every dollar counts.

Here’s why automation is essential for data hygiene:

• Faster decision-making: Manual data cleaning is too slow for today’s market. Automation processes tasks like deduplication and error correction instantly, giving you the agility to act on opportunities before competitors do.

• Flawless consistency: Humans miss things. Automation ensures your data is accurate and validated every time, eliminating mistakes that could derail your strategy.

• Effortless scaling: As your data grows, manual processes can’t keep up. Automation scales with your business, allowing you to manage high data volumes without sacrificing quality or efficiency.

• Reduced errors: Dirty data leads to flawed campaigns and wasted spend. Automation drastically reduces human error, ensuring cleaner, more reliable data.

• Focused resources: By cutting down on repetitive tasks, automation lets your team focus on creative strategy and growth-driving initiatives instead of fixing messy data.

A centralized marketing intelligence solution can help you integrate data from hundreds of sources, streamlining collection and ensuring information is always high-quality and up-to-date. This automation eliminates common marketing analytics issues such as inconsistent metrics and manual data entry errors, giving you more accurate measurement and insights at scale.

For CMOs working with both online and offline data, automation is essential for integrating these sources into advanced models like multi-touch attribution (MTA) and marketing mix modeling (MMM). 

These require rigorous preprocessing and cleaning to deliver accurate causal impact analysis (quantifying how specific marketing actions or changes directly impact outcomes like sales, customer acquisition or engagement) and actionable optimizations. Without automation, extracting reliable insights from these models is nearly impossible.

Automation is like having a tireless team member who works 24/7 to ensure your data is clean, actionable and ready to power impactful decisions. The result? Faster, smarter campaigns that consistently outperform competitors who are still stuck in manual workflows.

Choosing the right tool for your data hygiene

Not all data hygiene tools are created equal. Some solutions, like Power My Analytics, offer basic data connectors, while others, like DataSlayer, provide open-source flexibility. However, these tools often require manual intervention and lack the scalability needed for growing marketing teams.

Funnel stands apart as the leading choice for automated marketing data hygiene. With hundreds of pre-built connectors, out-of-the-box data transformation and a centralized data hub, Funnel eliminates the manual work of cleaning, standardizing and integrating your marketing data.

Unlike other tools that focus solely on extraction, Funnel automates the entire data workflow, ensuring real-time accuracy, consistency and reliability — giving marketing teams instant access to clean, actionable insights without extra effort.

If you’re looking for a scalable, automated solution to eliminate data discrepancies, optimize reporting and drive better marketing decisions, Funnel is the clear choice.

What about data cleaning for marketing data?

When it comes to keeping marketing data clean, the process should be quick and easy. There’s no need for complicated coding.

We love tools with point-and-click features — simple and efficient, just the way it should be.

When it comes to data cleaning in marketing, there are a few jobs to be done, like: 

1. Removing duplicate conversions to prevent inflated ROAS
2. Standardizing UTM parameters for better campaign tracking
3. Fixing inconsistent data formats for reporting accuracy
4. Validating ad spend data to prevent budget miscalculations

With a data hub that automates these jobs, you can collect, clean, transform and share all your data in one seamless flow.

How Funnel ensures clean, reliable marketing data

Manually compiling marketing reports in Google Sheets or other tools can be a frustrating process. You spend time downloading data from multiple platforms, dealing with inconsistent field names, handling different currencies and troubleshooting inaccuracies. These issues can lead to misleading insights and wasted time fixing data errors.

Funnel automates these time-consuming tasks by ensuring data hygiene at every stage — from collection and standardization to validation and reporting. Let’s see how.

How Funnel maintains data hygiene

When you connect one or more data sources to Funnel, it automatically cleans and organizes your data into:

• Metrics – Measurable data points like costs, clicks and impressions.
• Dimensions – Attributes that describe those metrics, such as campaign, ad group or country, allowing for better segmentation and analysis.
• These metrics and dimensions are stored as fields, which Funnel categorizes to keep your data structured and error-free.
• Custom fields – Created by users or provided by Funnel for enhanced analysis. These fields help standardize naming conventions, unify tracking parameters or calculate custom metrics — all without altering raw data.
• Imported fields – Pulled directly from connected data sources, ensuring accurate, real-time data ingestion.
• Built-in fields – Standardized fields provided by Funnel, such as date, week number or data source name, ensuring consistency across platforms.

By automatically sorting and validating fields, Funnel eliminates common data discrepancies and ensures all reporting is based on accurate, structured information.

What this looks like in practice: Ensuring clean, unified cost data in Funnel

Managing ad spend across multiple platforms can be challenging due to inconsistent cost metrics and currency variations. Different ad platforms — such as Google Ads, Facebook Ads and LinkedIn Ads — may use unique naming conventions for cost, making it difficult to analyze total ad spend accurately.

Let’s look at an example of how Funnel can help solve these issues.

A marketing team running multi-channel campaigns faced the following problems:

• Different cost labels across platforms (e.g., “Ad Spend” on Facebook, “Cost” on Google Ads, “Amount Spent” on LinkedIn)
• Currency inconsistencies, with some reports showing USD, others in EUR or GBP
• Difficulties aggregating total spend, leading to misaligned budgets and inaccurate ROAS calculations

How Funnel ensures cost data hygiene

• Automatically unifies cost data from all platforms into a single metric, labeled “Cost”
• Standardizes naming conventions across ad networks, ensuring consistency
• Automatically converts currencies based on exchange rates, so all costs are reported in a single, unified currency
• Prevents duplicate cost entries, ensuring total ad spend is accurate and trustworthy

The result?

With clean, standardized cost data, the marketing team can:

• See total ad spend across all platforms in one place
• Accurately measure ROAS and optimize budgets without manual data correction
• Ensure financial reports are precise, avoiding discrepancies between marketing and finance teams

See this in action here: 

Turn your dirty data into a strategic advantage

Get intelligence that gives you a competitive edge with actionable insights, time savings and measurable improvements in ROI. Outperform the competition with marketing intelligence you can rely on and keep your C-suite happy with metrics that tie your marketing efforts directly to growth.