Losing $24K Annually? Configure Odoo 18 Field Cleaning Rules

Import customer list from old CRM. 2,847 contacts. Phone numbers: Some have spaces, some have dashes, some have parentheses, some have country codes, some don't. Example: "(555) 123-4567" vs "555.123.4567" vs "+1 5551234567" vs "5551234567". Marketing team can't run SMS campaign because phone format inconsistent.

Your database is a mess: Company names with extra spaces ("ABC Corp "), emails in mixed case ("John.DOE@Company.COM"), phone numbers in 14 different formats, HTML code pasted into text fields, product descriptions typed in ALL CAPS. Sales rep copies data, pastes incorrectly formatted info, it spreads.

Cost: Marketing campaign fails because 847 phone numbers rejected by SMS provider (wrong format) = $4,200 wasted on campaign. Support team can't find customer records (searching "ABC Corp" doesn't match "ABC Corp " with extra spaces) = 3.7 hours weekly wasted = $8,424/year. Data export to accounting software fails because of inconsistent formatting = manual cleanup 11 hours monthly = $11,220/year.

Odoo 18 Field Cleaning Rules fix this automatically: Standardize phone formats, trim extra spaces, fix text casing (all lowercase → Title Case), remove HTML tags. Set rules once, apply to thousands of records. Here's how to configure field cleaning so you stop losing $23,844/year to bad data formatting.

What Field Cleaning Rules Do

Automatically standardize data formats across your database. Set rules for specific fields, apply to all records. Four main actions:

Step 1: Access Field Cleaning Rules

1. Open Data Cleaning app
2. Go to Configuration → Field Cleaning
3. See list of existing rules (if any)

Step 2: Create Field Cleaning Rule

Basic Setup

1. Click New
2. Enter rule name: "Contact Data Standardization"
3. Select Model: Choose which data you're cleaning
   • Contacts (res.partner)
   • Companies (res.company)
   • Products (product.product)
   • Sales Orders (sale.order)
   • CRM Leads (crm.lead)
4. Choose Cleaning Mode:
   • Automatic: Rules apply immediately to all records
   • Manual: Review suggestions before applying

Configure Notifications (Manual Mode Only)

If using Manual mode, specify who gets notified about cleaning tasks:

1. Field appears: Notify Users
2. Select users to notify
3. Choose frequency: Daily, Weekly, Monthly

Step 3: Add Cleaning Rules

Now define specific rules for each field you want to clean.

Rule 1: Trim Extra Spaces from Company Names

1. Under Rules section, click Add a line
2. Field to Clean: Company Name
3. Action: Trim Spaces
4. Trim: Select option
   • All Spaces: Removes ALL spaces (use for single-word fields)
   • Superfluous Spaces: Keeps single spaces, removes extras (recommended)
5. Click Save & New

Before: "ABC  Corp   Inc  "
After:  "ABC Corp Inc"

Before: "Tech   Solutions    LLC"
After:  "Tech Solutions LLC"

Rule 2: Standardize Phone Numbers

1. Click Add a line
2. Field to Clean: Mobile (or Phone)
3. Action: Format Phone
4. System auto-formats based on:
   • Country code from contact's address
   • International phone format standard
5. Click Save & New

Before: "(555) 123-4567"
Before: "555.123.4567"
Before: "5551234567"
Before: "+1 555 123 4567"

After: "+1 555-123-4567" (all standardized)

Rule 3: Fix Text Casing

1. Click Add a line
2. Field to Clean: Name (or any text field)
3. Action: Set Type Case
4. Case: Choose option
   • All Lowercase: "john doe"
   • First Letters Uppercase: "John Doe" (Title Case)
   • All Uppercase: "JOHN DOE"
5. Click Save & New

Before: "JOHN SMITH"
Before: "jane doe"
Before: "MiXeD CaSe"

After: "John Smith" (all consistent)

Rule 4: Remove HTML Tags

1. Click Add a line
2. Field to Clean: Description (or any text field)
3. Action: Scrap HTML
4. Removes all HTML/XML tags, keeps plain text
5. Click Save & Close

Before: "<p>Product description</p><br><strong>Features</strong>"

After: "Product description Features"

Step 4: Apply Rules to Records

Manual Mode: Review Suggestions

1. From Field Cleaning Rule form, click Clean icon
2. Or go to Data Cleaning → Field Cleaning tab
3. See list of records needing cleaning
4. Each row shows:
   • Field name
   • Record ID
   • Current Value
   • Suggested Value (after cleaning)
   • Record Name
5. Review suggestions
6. Select records to clean
7. Click Validate

Automatic Mode: No Action Needed

Rules apply automatically when records created or updated. No manual validation required.

Real-World Examples

Example 1: E-commerce Company Contact Cleanup

Example 2: B2B SaaS Product Catalog Cleanup

Example 3: Manufacturing Company Address Standardization

Advanced Tips

1. Multiple Rules for Same Field

You can apply multiple cleaning actions to the same field:

Example: Company Name field
Rule 1: Trim Spaces (superfluous)
Rule 2: Set Type Case (Title Case)

Result:
"  ABC  corp   INC  " 
→ "ABC corp INC" (spaces trimmed)
→ "Abc Corp Inc" (title case applied)

2. Test Before Going Automatic

3. Schedule Regular Cleaning

For Manual mode: Set notifications to Weekly or Monthly. Ensures team regularly reviews and validates cleaned data.

Common Mistakes

Real-World Impact Example

Quick Implementation Checklist