How to maintain data quality using saved views

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Last Updated: November 27, 2025 Object Tags: Data Quality, Saved Views, CRM Hygiene, Audit, Data Management

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Overview

Create saved views that systematically identify data quality issues - empty critical fields, stale deals, missing documentation, and inconsistent data. This workflow transforms ad-hoc data cleanup into a systematic monthly routine that maintains high CRM data quality across your team. What you’ll accomplish: Build 4-6 audit views that surface data gaps, establish monthly data hygiene routine, and improve overall CRM data completeness from ~70% to 95%+. Who it’s for: Operations managers, CRM admins, team leads, and anyone responsible for data quality and CRM hygiene. When to use this: Implementing data quality standards, preparing for audits, onboarding new team members to data expectations, or improving reporting accuracy.

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Prerequisites

• List Owner or Admin permissions (helpful but not required)
• Understanding of which fields are critical for your pipeline
• Familiarity with filters (especially “is empty” operator)
• Buy-in from team on data quality standards

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Workflow Steps

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Step 1: Identify Critical Fields That Should Never Be Empty

Define data quality standards: Minimum required fields (for all deals):

• Owner (who’s responsible?)
• Status (where in pipeline?)
• Sector/Industry (what space?)
• Lead Source (how did we find them?) Stage-specific required fields:
• Active deals: Next Steps, Last Contact, Investment Amount
• Partner Review: Investment Memo Link, Deal Champion
• Due Diligence: DD Lead, Target Close Date
• Closed/Passed: Close Date, Close Reason (or Pass Reason) Document your standards:
• Create list of critical fields by stage
• Share with team
• Get agreement on expectations

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Step 2: Create Core Data Quality Audit Views

View 1: Missing Critical Fields (Overall) Build the view:

1. Filters:

• (Lead Source = is empty)

1. Sorts:

• Date Added (newest first) - recently added should have data
  • Last Contact (newest first) - recently touched should be complete

1. Columns:

• Name, Owner, Status, Sector, Lead Source, Date Added, Last Contact Save the view:
• Name: “Data Audit - Missing Critical Fields”
• Permissions: Shared with ops team or private if running solo
• This creates New Lists variant (Boolean OR not in Classic) View 2: Active Deals - Missing Next Steps

Build the view:

1. Filters:

• Status = Active (or your active stages)
  • Next Steps = is empty

1. Sorts:

• Last Contact (newest first)
  • Owner (groups by person)

1. Columns:

• Name, Owner, Status, Last Contact, Next Meeting, Next Steps Save: Name = “Active Deals - No Next Steps”

View 3: Closed Deals - Missing Documentation Build the view:

1. Filters:

• Status = Closed Lost OR Closed Won
  • Date = Last 3 months (recent closes)
  • Notes = is empty OR Close Reason = is empty

1. Sorts:

• Date Added to Status (newest first)

1. Columns:

• Name, Status, Close Date, Close Reason, Notes, Owner Save: Name = “Recent Closes - Missing Documentation”

View 4: Stale Active Deals Build the view:

1. Filters:

• Status = Active

1. Sorts:

• Last Contact (oldest first) - stalest deals at top

1. Columns:

• Name, Status, Owner, Last Contact, Last Meeting, Next Steps Save: Name = “Active Deals - No Contact 90+ Days”

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Step 3: Run Initial Baseline Audit

Week 1: Data Quality Assessment For each audit view:

Open “Missing Critical Fields” view:

• Count: How many deals are missing critical fields?
  • Example: 42 out of 150 deals (28%)
  • Export to spreadsheet for tracking

Open “Active Deals - No Next Steps”:

• Count: 18 active deals without next steps
  • Percentage: 18/85 active deals = 21%

1. Open “Recent Closes - Missing Documentation”:

• Count: 12 closed deals without close reasons or notes
  • Percentage: 12/30 recent closes = 40%

Open “Stale Active Deals”:

• Count: 23 deals active but no contact in 90+ days
  • Percentage: 23/85 active = 27% Document baseline:

Share with leadership:

• Current state of data quality
• Goals for improvement
• Plan for monthly audits

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Step 4: Clean Up Initial Issues

Week 2-3: Data Cleanup Sprint Assign ownership:

1. Share audit views with team
2. Each person responsible for their deals
3. Set deadline: 2 weeks to clean up Systematic cleanup:

For “Missing Critical Fields” view:

• Contact deal owners: “Your deal [Name] is missing [Field]. Please update by Friday.”
• Work through list top to bottom
• Use field editing to fill gaps
• Re-check view daily to track progress For “Active - No Next Steps” view:
• Each owner reviews their deals
• Adds Next Steps based on last interaction
• If truly no next steps, consider changing status to On Hold For “Recent Closes - Missing Documentation”:
• Each owner documents why deals closed
• Adds close reasons and learnings
• Critical for pattern analysis For “Stale Active Deals”:
• Each owner decides: Re-engage or move to Passed?
• Updates status appropriately
• If re-engaging, adds Next Steps and schedules outreach Track progress:
• Check audit views daily
• Count remaining issues
• Celebrate as numbers drop

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Step 5: Establish Monthly Audit Routine

First Monday of Every Month (30 minutes): Run all audit views:

“Missing Critical Fields”:

• Count current issues
  • Compare to last month
  • If >10 issues: Email owners with deadline

“Active - No Next Steps”:

• Should be near zero if team has good habits
  • Any deals here = flag to owner immediately

“Recent Closes - Missing Documentation”:

• Review past 3 months of closes
  • Contact owners of undocumented closes
  • Emphasize importance for learning

“Stale Active Deals”:

• Review deals inactive 90+ days
  • Email owners: “These deals haven’t been contacted - should status change?”
  • Set 1-week deadline for status updates Document findings:

Report to leadership monthly:

• Progress toward data quality goals
• Trends (improving or declining)
• Any systematic issues requiring process changes

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Step 6: Integrate Into Team Processes

Make data quality everyone’s responsibility: Weekly team meeting agenda item:

• “Data quality check: Who has deals in the audit views?”
• Quick review of current issue count
• 2-minute discussion
• Reinforces importance New team member onboarding:
• Day 1: Show them audit views
• Explain: “These should always be near empty”
• Week 1: Have them run their first audit
• Monthly: Include in their responsibilities Required Fields + Triggers integration:
• Configure Required Fields to prevent empty critical fields at entry
• Use Status Triggers to require documentation at key stages
• Audit views catch anything that slips through Recognition:
• Celebrate months with zero data quality issues
• Acknowledge team members with best data hygiene
• Share success metrics in team updates

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Step 7: Create Advanced Audit Views (Optional)

As your data quality matures: View 5: Investment Memos Missing

• Filters: Status = Partner Review OR Due Diligence, Investment Memo Link = is empty
• Purpose: Ensure documentation at key stages View 6: Deals Missing Amounts
• Filters: Status = Active stages, Investment Amount = is empty
• Purpose: Forecasting accuracy View 7: Orphaned Opportunities
• Filters: Opportunity List, Organizations = is empty (no company linked)
• Purpose: Relational data integrity

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Expected Outcome

• Data completeness improves from 70% to 95%+ within 3 months
• Critical fields (Owner, Status, Sector, Lead Source) 100% complete
• Active deals have Next Steps 100% of the time
• Closed deals documented with close reasons and learnings
• Stale deals identified and status-updated monthly
• Monthly audit routine takes 30 minutes (vs hours of ad-hoc cleanup)
• Team awareness of data quality standards
• Accurate reporting enabled by complete data
• Reduced “garbage in, garbage out” issues in analytics

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Tips & Best Practices

Audit View Design:

• Use Boolean OR: Find deals missing ANY critical field
• Sort strategically: Prioritize by new or stale Last Contact dates
• Keep focused: One view per data quality dimension
• Update criteria: Adjust as team’s data standards evolve Running Audits:
• Monthly minimum: More frequent for large teams or high-volume pipelines
• Same day each month: First Monday establishes routine
• Time-box: 30 minutes max to avoid audit fatigue
• Track trends: Are issues decreasing month over month? Team Communication:
• Non-punitive approach: Frame as “helping us all succeed” not “you did it wrong”
• Show impact: “Complete data enables better partner decisions”
• Make it easy: Provide templates for common fields (Next Steps examples)
• Celebrate improvement: Acknowledge when numbers drop Prevention vs Cure:
• Required Fields: Prevent empty fields at entry point
• Status Triggers: Require documentation at key milestones
• Audit views: Catch anything that slips through
• Monthly reviews: Systematic cleanup of gaps For Small Teams:
• Audit views still valuable (prevents individual blind spots)
• Can be informal (“Hey, you have 2 deals missing next steps”)
• Monthly audits sufficient For Large Teams:
• Audit views critical (can’t track everyone manually)
• Formal process needed (email campaigns, deadlines)
• Consider weekly spot checks + monthly full audit
• Different views for different sub-teams

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Example Use Case

Growth Partners, a 15-person PE firm, had data quality issues affecting reporting: The Problem (Month 0):

• Partners complained about incomplete context in pipeline reviews
• Reporting to LPs required manual data cleanup (6 hours/quarter)
• 30% of active deals missing Owner
• 45% of closed deals had no close documentation
• Couldn’t analyze which lead sources converted (inconsistent data)
• New analysts didn’t know which fields were important Month 1 - Audit View Creation:

Created 5 audit views:

“Missing Owner or Status”:

• Filters: (Owner = empty) OR (Status = empty)
  • Found: 38 deals

“Active - No Next Steps”:

• Filters: Status = Active stages, Next Steps = empty
  • Found: 32 deals

“Closed - No Documentation”:

• Filters: Status = Closed, Close Reason = empty OR Notes = empty
  • Found: 27 deals

“Stale Active (90+ days)”:

• Filters: Status = Active, Last Contact > 90 days ago
  • Found: 19 deals

“Missing Investment Amounts”:

• Filters: Status = Active OR Partner Review OR DD, Amount = empty
  • Found: 24 deals Total issues: 140 across 5 categories

Month 1 - Initial Cleanup: Week 1: Communication

• Sent team email with audit findings
• Shared audit views
• Explained data quality goals
• Set 2-week cleanup deadline Week 2-3: Team Cleanup Sprint
• Each team member responsible for their deals
• Daily check-ins on progress
• Ops team provided support
• Used field editing to fill gaps efficiently Process Improvements Added:

Month 3:

• Configured Required Fields (Owner, Status, Sector) to prevent future gaps
• Result: New deals always have critical fields Month 4:
• Added Status Trigger on “Closed Lost” requiring Close Reason
• Result: Close documentation now automatic Month 5:
• Created “New This Month” view showing all new adds
• Weekly spot check ensures new entries are complete Business Impact After 6 Months:

Reporting efficiency:

• LP quarterly reporting prep: 30 minutes (vs 6 hours)
• Board materials prep: 1 hour (vs 3 hours cleaning data first)
• Ad-hoc partner requests: Instant (vs “let me clean the data first”) Decision quality:
• Partners: “Context is always complete now”
• Investment committee: “We can trust the data in pipeline reviews”
• Analytics: “Lead source conversion analysis finally accurate” Team efficiency:
• Monthly audit: 30 minutes (vs ad-hoc cleanup consuming hours)
• New team members: Immediately see data expectations via audit views
• Ops time saved: 20 hours/quarter on data cleanup Cultural shift:
• Team: “Data quality is just how we work now”
• New hires: “I was shown the audit views day one - very clear what’s expected”
• Leadership: “Our CRM data is finally an asset, not a liability”

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