> ## Documentation Index
> Fetch the complete documentation index at: https://support.affinity.co/llms.txt
> Use this file to discover all available pages before exploring further.

# How to maintain data quality using saved views

> Workflow recipe: How to maintain data quality using saved views

<Note>**How-to** — task-oriented recipe.</Note>

**Last Updated:** November 27, 2025

**Object Tags:** Data Quality, Saved Views, CRM Hygiene, Audit, Data Management

***

## 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.

***

## 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

***

## 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:**

1.

**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

1.

**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%

1.

**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:**

1.

**"Missing Critical Fields"**:

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

1.

**"Active - No Next Steps"**:

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

1.

**"Recent Closes - Missing Documentation"**:

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

1.

**"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

***

## 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

***

## 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

***

## 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:**

1.

**"Missing Owner or Status"**:

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

1.

**"Active - No Next Steps"**:

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

1.

**"Closed - No Documentation"**:

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

1.

**"Stale Active (90+ days)"**:

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

1.

**"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"

***