The Dirty Truth Behind Data Hygiene

Most companies don’t think much about data hygiene—until their brand-new software or system starts acting up. But it’s often too late by then, and the damage is already done: duplicate records, inaccurate reports, missed opportunities, and frustrated teams.

But what about AI? Isn’t AI supposed to compensate for less-than-stellar data? Studies show that 40 percent of all globally operating companies use AI, with many investing in AI with that purpose in mind. 

However, today’s AI-powered tools are only as good as the data they’re built on. Unfortunately, the health of company data isn’t the first place users look when they encounter problems. Teams are more likely to blame new software for underperforming or accuse other teams of misusing it before they suspect data could be the root of the problem. 

Even the most intelligent AI can’t deliver accurate insights or reliable outcomes if your systems are clogged with dirty data. In fact, dirty data can amplify problems, leading to flawed recommendations and wasted resources. Clean and well-maintained data isn’t just helpful—it’s essential to getting real value from AI-driven software.

Read more: Data Quality Isn’t Optional, It’s Your Competitive Edge

Taking proactive steps to maintain clean, consistent, and reliable data from the start helps you get the most out of your tech investment. Let’s discuss the signs and causes of dirty data, why clean data is essential for getting the most value from your tech stack investment, and how minor adjustments in your RevOps can help maintain smooth, data-driven operations. 

Signs of Poor Data Hygiene

Frequent crashes, sluggish reports, confused CRMs, and broken automation are warning signs that your data needs help. Dirty data doesn’t just create inconvenience; it breaks trust in your tools, slows down your teams, and takes a chunk out of your hard-earned revenue. 

If you constantly perform temporary fixes for short-term problems instead of focusing on faster go to market, it’s time to stop treating the symptoms and address the root cause: poor data hygiene.

Top 10 Causes of Dirty Data in RevOps Systems

You didn’t invest in modern software to babysit error messages, wait on frozen reports, or manually untangle customer records every week—but that’s exactly what happens when data hygiene is an afterthought. 

Studies show that over half (60%) of companies don’t track the financial implications of bad data. However, to give you a general idea, early Gartner research found that bad data costs organizations an average of $15 million every year. 

Poor data hygiene quietly erodes the value of your entire tech stack, from sluggish performance to automation failures and untrustworthy analytics.

If your team is stuck cleaning up the same messes over and over, it’s time to stop blaming the tools and start looking at the data that’s feeding them.

1. Siloed Systems and Teams

When sales, marketing, and customer success use disconnected tools, data is fragmented, inconsistent, and hard to trust.

2. Manual Data Entry Errors

Typos, duplicate records, and incomplete fields happen when reps manually enter data, especially under pressure.

3. Outdated or Inactive Records

No one’s purging old leads, stale accounts, or dead opportunities, leading to bloated CRMs and misleading reports.

4. Lack of a Unified Data Governance Strategy

Without clear rules for naming conventions, required fields, and data hygiene, inconsistency runs rampant.

5. Multiple Sources of Truth

Different teams using their spreadsheets or tools create confusion and version control nightmares.

6. Poor Integration Between Systems

When platforms like Salesforce, HubSpot, Marketo, or Zendesk aren’t properly integrated, syncing errors and duplicate records follow.

7. Misaligned Field Definitions

If “qualified lead” means one thing to marketing and something else to sales, the data will never truly align.

8. No Regular Data Audits or Cleansing

Companies that don’t actively monitor and clean their data quickly fall behind, with issues compounding over time.

9. Legacy Data Migration Issues

Bringing in messy data from old systems without scrubbing it first can poison your brand-new RevOps environment.

10. Lack of Accountability for Data Ownership

When no one is responsible for data quality, it becomes everyone’s problem—and no one fixes it.

Now that we’ve discussed the signs and causes of dirty data let’s focus on what good data hygiene looks like and why it matters. 

When companies talk about data hygiene, they’re referring to the ongoing process of ensuring that their data is clean, accurate, consistent, and reliable across systems. Data hygiene is the foundation for everything from smooth system performance to trustworthy analytics and successful automation. 

Data Hygiene: Why it Matters

Without data hygiene, even the most sophisticated systems can’t function properly. Clean, reliable data is the foundation for everything from sales forecasting to automated marketing journeys. 

A shocking study found that 82 percent of companies make decisions based on stale information. While more companies invest in AI-supported ERP systems, 9 out of 10 companies with these systems admit they can’t access real-time insights to drive data-driven decisions. 

When your CRM is overrun with duplicate accounts, broken email addresses, or missing key fields, your go-to-market strategy starts to suffer. The more your team has to second-guess a report’s accuracy or manually fix a contact record, the less time they’re spending on what actually drives revenue.

Good data hygiene practices create trust in your tech stack and across your entire organization. With validated, well-maintained data, RevOps teams can confidently run reports, automate workflows, and tailor customer experiences without fear of missteps. 

Clean data means smoother handoffs between departments, more accurate segmentation, and stronger performance across the board. In short, keeping your data in shape isn’t just good housekeeping. It’s good business.

Data Cleaning: It’s a Dirty Job

Cleaning data includes implementing the right checks and workflows to prevent it from piling up again.

The data cleaning process involves identifying and correcting, or removing, any inaccurate, incomplete, duplicated, improperly formatted, or irrelevant data. This starts with auditing your current data sources to flag inconsistencies and errors. From there, the process may include deduplication, standardizing formats (like dates or addresses), enriching records with missing information, and validating fields against trusted reference data.

But here’s the catch: cleaning data isn’t as simple as hitting “delete” or running a few formulas. It requires a deep understanding of your systems, how your teams use data, and where that data flows across your tech stack. That’s why working with an experienced team is essential. Experts bring the tools, methodologies, and foresight needed to clean your data efficiently without disrupting business operations or breaking critical integrations. They also help build sustainable processes so your data stays clean as your business grows. With the right team by your side, you’re future-proofing your entire operation.

Data Maintenance: Keep The GTM Flowing Smooth

Cleaning your data is a critical first step, but without a plan for ongoing data maintenance, those same errors, duplicates, and inconsistencies will creep back in, sabotaging your go-to-market (GTM) efforts all over again. Every new lead, account update, or integration is a potential entry point for insufficient data. If you’re not actively managing and validating what’s flowing into your systems, you’re setting yourself up for a never-ending game of cleanup and rework.

Read more: Want More From AI? Rethink Data Management

Smart data maintenance turns a one-time fix into a long-term advantage. It’s the difference between constantly putting out fires and running a GTM engine that works. Regular audits, validation rules, automated deduplication, and cross-system syncing keep your pipeline healthy and your ops team focused on strategy—not manual fixes. 

Meet Change Management: The Data Maintenance Solution

Change management isn’t just for rolling out new software—it’s a critical part of keeping data clean long-term.

When organizations overlook the human side of data, they end up with inconsistent inputs, rogue processes, and workarounds that quietly undermine system integrity. 

An experienced change management team introduces structure, training, and accountability, ensuring that everyone across the organization understands how to enter, manage, and use data properly. It’s about building a culture where clean data isn’t just IT’s responsibility—it’s everyone’s.

By embedding clear processes and best practices into daily workflows, change management helps prevent the very issues that lead to dirty data in the first place. Whether it’s defining ownership for key data fields, implementing validation rules, or aligning teams on data governance policies, change management provides the framework to keep those efforts alive well after implementation. It’s not a one-and-done fix—it’s an ongoing strategy that turns data hygiene into a shared habit, not a recurring headache.

If you want consistent lead routing, accurate territory assignments, and high-performing campaigns, you need disciplined, proactive data hygiene. In short, clean once, maintain always—or risk falling back into chaos.