How to maintain data quality using saved views

Workflow recipe: How to maintain data quality using saved views

How-to — task-oriented recipe.

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

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

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”

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:

Audit View	Issues Found	Percentage	Goal
Missing Critical Fields	42	28%	<5%
Active - No Next Steps	18	21%	0%
Closed - No Documentation	12	40%	<10%
Stale Active Deals	23	27%	<10%

Share with leadership:

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

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

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:

Month	Missing Critical	No Next Steps	No Close Docs	Stale Active
Baseline	42 (28%)	18 (21%)	12 (40%)	23 (27%)
Month 1	8 (5%)	3 (4%)	2 (7%)	5 (6%)
Month 2	5 (3%)	1 (1%)	1 (3%)	4 (5%)
Month 3	3 (2%)	0 (0%)	0 (0%)	2 (2%)

Report to leadership monthly:

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

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

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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