How RevOps Teams Keep CRM Data Clean

TL;DR: How do you keep CRM data clean automatically?

To keep CRM data clean automatically, you need to capture data at the source—during customer conversations—rather than relying on reps to update records manually. The key steps are: audit your current data quality, define standards, automate capture from calls, set up validation rules, and schedule regular reviews.

Most RevOps teams can implement a solid data hygiene system in 1-2 weeks. The payoff is accurate pipeline data, better forecasting, and reps who spend time selling instead of doing data entry.

Here's exactly how to do it.

What do you need before getting started?

Before you begin, make sure you have admin access to your CRM and clarity on which fields matter most for your business. This ensures you can make configuration changes and prioritize the right data.

Requirements:

Admin access to HubSpot or Salesforce
A list of required fields for deals, contacts, and companies
Access to call recordings (Zoom, Teams, or Gong)

Optional but helpful:

Current data quality baseline (% of records with missing fields)
Buy-in from sales leadership on data entry expectations

Step 1: How do you audit your current CRM data quality?

Start by running reports that surface your biggest data quality issues. This gives you a baseline and helps prioritize which problems to fix first.

In HubSpot or Salesforce, create reports that show:

Deals missing required fields — How many open deals lack next steps, close dates, or qualification data?
Stale contacts — How many contacts haven't been updated in 90+ days?
Duplicate records — How many contacts or companies have multiple entries?
Incomplete company data — How many companies lack industry, size, or other segmentation fields?

Pro tip: Export the results and calculate a "data quality score" (% of records that meet all requirements). Track this monthly to measure improvement.

This audit typically reveals that 30-50% of CRM records have missing or outdated data. That's normal—and fixable.

Step 2: How do you define your data quality standards?

Next, document exactly which fields are required and what formats they should follow. This creates clarity for your team and enables automated validation.

Create a simple data dictionary that covers:

Object	Required Fields	Format/Picklist Values
Deal	Close date, Amount, Stage, Next steps	Dates in YYYY-MM-DD, Stages from picklist
Contact	Email, Job title, Phone	Email validated, Title standardized
Company	Industry, Employee count, Website	Industry from picklist

Focus on fields that impact:

Forecasting — Close date, amount, stage
Routing — Industry, company size, region
Outreach — Contact info, job title

Pro tip: Start with 5-7 required fields per object. You can add more later. Too many required fields upfront leads to reps abandoning entries entirely.

Step 3: How do you automate data capture at the source?

This is the highest-impact step: capture data directly from customer conversations instead of asking reps to type it in. Most CRM data originates in calls anyway—budget discussions, timeline questions, stakeholder mentions.

Traditional approach (manual):

Rep has a discovery call
Rep takes notes (maybe)
Rep updates CRM fields (often skipped or delayed)
Data decays as details are forgotten

Automated approach with CRM automation tools:

Rep has a discovery call
AI extracts key data from the conversation
CRM fields update automatically within minutes
Data is accurate and complete

AskElephant, for example, writes directly to HubSpot and Salesforce fields based on call content. When a prospect mentions "Q2 timeline" or "decision maker is the VP of Sales," those details populate the appropriate fields without rep involvement.

Teams like Kixie and Rebuy use this approach to eliminate manual data entry entirely.

Step 4: How do you set up validation rules and alerts?

Create automated checks that flag incomplete records before they become problems. This catches the data issues that slip through even with automation.

In HubSpot, set up workflows that:

Alert reps when a deal moves to a later stage but required fields are empty
Notify managers when deals have no activity in 14+ days
Block stage changes until required fields are populated

In Salesforce, use validation rules to:

Require close date and amount before deals can be marked "Commit"
Ensure contact roles are added before deals reach proposal stage
Flag duplicates for review

Pro tip: Don't block too aggressively at first. Start with alerts, measure compliance, then add blocks for chronic issues. Reps who feel blocked will find workarounds that make data quality worse.

For real-time visibility, set up proactive alerts that surface data quality issues in Slack before they impact forecasting.

Step 5: How do you schedule regular data hygiene reviews?

Establish a cadence for reviewing and maintaining data quality. Even with automation, some manual review is necessary.

Weekly (RevOps):

Review flagged records from validation rules
Merge identified duplicates
Check data quality score trend

Monthly (RevOps + Sales Leadership):

Audit sample of closed-won and closed-lost deals for data completeness
Review fields that consistently have low fill rates
Adjust required fields or validation rules based on findings

Quarterly:

Purge truly stale records (no activity in 12+ months)
Review and update picklist values
Check integration data flows for issues

Pro tip: Create a recurring calendar event for weekly reviews. 30 minutes on Monday morning prevents hours of cleanup later.

What mistakes should you avoid when cleaning CRM data?

The most common mistake is trying to fix data quality with training alone. Telling reps to "be better at data entry" doesn't work—they're focused on closing deals.

Relying on manual entry when automation is possible: Every field you can populate automatically is one less field a rep can forget.
Making too many fields required at once: Reps will enter junk data ("TBD", ".", "123") just to get past required field blocks.
Cleaning data without fixing the source: One-time cleanup projects provide temporary relief. If you don't fix why data gets dirty, it will get dirty again.
Not measuring improvement: Without a data quality score, you can't tell if your efforts are working.

How does AskElephant help with CRM data hygiene?

AskElephant automates the most error-prone part of CRM data management: capturing deal information from customer conversations. Instead of relying on reps to remember and type details, AskElephant writes directly to CRM fields within minutes of each call.

Here's what it automates:

Deal qualification fields — Budget, authority, need, timeline
Next steps and follow-ups — Automatically created as tasks
Contact roles — Stakeholders mentioned in calls are logged
Risk signals — Churn indicators trigger real-time alerts

For RevOps teams, this means cleaner data without policing rep behavior. The CRM reflects what actually happened on calls, not what reps remembered to enter.

Teams using AskElephant report saving 2-3 hours per rep per week on data entry—time that goes back to selling. And because data is captured at the source, forecast accuracy improves.

AskElephant pricing: Starting at $99/month. No seat minimums. Enterprise solutions available. View pricing.

See how AskElephant automates this

Frequently asked questions

How often should you clean CRM data?

Most RevOps teams should audit CRM data quality monthly and run automated hygiene checks weekly. However, if you automate data capture from calls, you significantly reduce the need for manual cleanup. The goal is prevention, not remediation.

What causes CRM data to become dirty?

The primary cause is manual data entry. Reps skip fields when they're busy, use inconsistent formats, or forget to update records entirely. Other causes include duplicate records from integrations, data decay as contacts change roles, and merging data from acquisitions.

Can you automate CRM data cleaning?

Yes—you can automate both data capture and data validation. Tools like AskElephant write structured data to CRM fields automatically from calls. Validation rules in your CRM flag incomplete records. Deduplication tools merge duplicates automatically. The combination dramatically reduces manual cleanup.

What is the ROI of clean CRM data?

Clean CRM data improves forecast accuracy, reduces wasted outreach to stale contacts, and enables better segmentation for marketing. According to various industry studies, bad data costs companies 15-25% of revenue. Teams with automated data hygiene report saving 2-3 hours per rep per week on manual entry—time that goes back to selling.

If automating CRM data entry sounds useful for your team, you can request a demo here.