If you’ve been working with Salesforce or Account Engagement (Pardot) for a while, you already know one uncomfortable truth: your database is slowly getting messier every single day. New leads enter, fields go unvalidated, people change jobs, forms get copied, integrations misfire – and suddenly your CRM isn’t a source of truth anymore… it’s a liability.
At Exelement, we work with Salesforce and Pardot customers every day, and we see the same pattern repeat itself:
Bad data creeps in silently – but the cost is anything but silent.
Let’s break down exactly how “dirty data” hurts your business, why it happens, and what you can do about it.
Dirty data destroys email deliverability
If you’re using Account Engagement for email marketing, your sending reputation is everything. But bad data works against you:
Common deliverability killers:
- Invalid or mistyped emails (e.g. “gnail.com”, “email@email”, “test@test”)
- Spam traps coming from old lists
- Role-based emails (“info@”, “sales@”, “admin@”)
- Bounced emails that were never cleaned
- Duplicates where one version is mailable and the other is “dead”
- Messy list uploads missing necessary fields and containing wrong information
How it costs you money:
Poor deliverability → lower inbox placement → fewer leads engaging with your campaigns → lower pipeline contribution.
You pay for mailable prospects – so storing unusable emails is literally a wasted budget.
Bad data breaks segmentation
Strong segmentation is the backbone of every automated program – nurtures, ABM flows, product onboarding, event journeys.
But segmentation only works if your data can be trusted.
Symptoms of dirty segmentation:
- Prospects missing industry, persona, lifecycle stage
- Forms populating fields with inconsistent values (“FinTech”, “fintech”, “Financial Tech”, “Finance Tech”)
- Leads with outdated job titles
- Integrations overwriting fields unintentionally
- Contacts living in the wrong country or region
The impact:
- You send the wrong content to the wrong audience
- You exclude the people who should get the email
- You end up building more workarounds, more lists, more manual fixes
Ultimately, segmentation loses its sharpness – and your campaigns lose their power.
Dirty data makes lead scoring useless
Lead scoring is only as strong as the data behind it. Bad data means your scoring framework gives misleading signals.
Examples we see all the time:
- Prospects get qualified based on spam form submissions
- Duplicates show different scoring values, which becomes confusing for sales
- Missing fields prevent grade recalculation
- Incorrect first-touch attribution inflates the wrong leads
Business impact:
When lead scoring becomes unreliable, sales stop trusting the system.
That’s where marketing and sales alignment breaks down—and revenue slows.
Inaccurate reporting = bad decisions
Executives rely on Salesforce dashboards and B2BMA to understand pipeline, ROI, and campaign performance. Dirty data destroys reliability.
Reporting pitfalls:
- Inconsistent campaign member statuses
- Leads with no source or wrong source
- Opportunities associated with incorrect contacts
- Automation rules creating biased performance metrics
- Default campaigns assigned incorrectly (common with API-created prospects)
What this leads to:
- Misallocation of budget
- Wrong channels getting credit
- Inaccurate ROI calculations
- Decisions based on fiction instead of fact
A CRM filled with inaccurate data is more dangerous than no CRM at all.
“But why does dirty data happen?”
Some of the most common causes in Salesforce/Pardot environments:
- Old forms with outdated field mappings
- Integrations creating partial or empty records
- CSV uploads containing blank rows, invisible characters or malformed emails
- No required fields applied at the point of entry
- Multiple teams entering data inconsistently
- API imports assigning the wrong (or default) campaign
- No automated data quality rules
- Years of never cleaning mailable prospects
Dirty data is never caused by one big mistake—it's caused by many small ones repeated over time.
So how do you fix it?
The good news: most organizations can dramatically improve data quality in 2–4 weeks with the right plan.
Start with a structured health check
This includes reviewing:
- Mailability status
- Bounces & unengaged prospects
- Duplicate ratio (Salesforce + Pardot)
- Field inconsistencies
- List hygiene & opt-in quality
- Form and integration behaviour
- Default campaign assignments
- Automation rules you’ve forgotten about
Create rules for prevention
- Standardize field values
- Set required fields on forms
- Configure country/state normalization
- Implement rules to archive unmailable contacts
- Control who can import what
- Add validation logic to fields in Salesforce
- Fix the default prospect campaign (the source of many hidden issues)
Clean in waves
Instead of trying to perfect everything at once, clean by category:
- Email issues
- Field normalization
- Duplicate merging
- Source accuracy
- Mailability structure
- Automation cleanup
Exelement can help you get there
At Exelement, we specialize in Salesforce and Account Engagement.
We’ve seen virtually every data-quality problem imaginable – from ghost campaigns to thousands of mailable and unmailable prospects clogging up a database – and we know exactly how to fix them.
If your Pardot database is starting to overflow, or if your CRM data simply feels “off”, you don’t need a full rebuild.
You need a structured, expert-led clean-up and prevention framework.
Reach out if you want us to run a health check, clean your database, or help you prevent this from happening again. We’ll make sure your systems start working for you – not against you.
