Salesforce Data Governance: A Practical Guide for Growing Orgs

Data governance sounds like something that belongs in a 200-page enterprise document. It doesn't have to be. At its core, it's about answering three questions: who owns the data, what are the rules, and how do you enforce them?

Here's a practical framework that works for growing companies without requiring a dedicated data team.

Start With Data Ownership

Every field in Salesforce should have an owner. Not in the metadata sense — in the "who's responsible for this being accurate" sense.

For most organisations, this breaks down into:

• Sales owns: Lead, Contact, Opportunity, and Activity data related to the sales process
• Marketing owns: Campaign data, lead scoring rules, and marketing-related fields
• Operations owns: Account hierarchies, territory assignments, and process-related fields
• Finance owns: Revenue fields, billing information, and financial custom objects

When nobody owns a field, nobody cares if it's wrong. Assign ownership explicitly.

Define Your Data Standards

You don't need a 50-page document. You need clear, enforced rules for the basics:

Naming conventions: Is it "United Kingdom" or "UK"? Is it "Google LLC" or "Google"? Pick a standard and enforce it with picklists, validation rules, or duplicate rules.

Required fields: Only make fields required if they're actually necessary. Every unnecessary required field is an invitation for users to enter junk data. "N/A" in a phone number field helps nobody.

Picklist values: Audit them quarterly. Merge values that mean the same thing. Retire values nobody uses. A picklist with 47 options is a text field with extra steps.

Automate Enforcement

Relying on user discipline for data quality is a losing strategy. Use Salesforce's built-in tools:

Validation rules for format enforcement (phone numbers, postcodes, email domains). Keep them focused — a validation rule that blocks a sale because a secondary address is missing will be hated and eventually circumvented.

Duplicate rules with matching rules tuned to your data. The default matching rules are a starting point, not a solution. Tune them based on your actual duplicate patterns.

Record types to ensure different teams see different picklist values and page layouts. A Sales rep shouldn't see Support-specific fields, and vice versa.

Flows for data enrichment — auto-populating fields based on other fields, standardising formats on save, or flagging records that don't meet quality thresholds.

Measure Data Quality

You can't improve what you don't measure. Build a simple dashboard that tracks:

• Records missing key fields (by object and field)
• Duplicate record counts over time
• Records not updated in 90+ days (stale data)
• Picklist value distribution (spot outliers)

Review this monthly. It takes 15 minutes and tells you exactly where your data quality is degrading.

Handle the Backlog

Every org has a data quality backlog. Don't try to fix everything at once. Prioritise by business impact:

1. Fix data that directly affects revenue reporting

2. Clean data that impacts customer-facing processes

3. Address data that affects internal workflows

4. Eventually get to nice-to-have cleanup

A weekly "data quality hour" where one person spends 60 minutes cleaning the highest-priority records is more effective than a quarterly "data cleanup project" that never quite finishes.

Make It Stick

The biggest risk to data governance isn't technical — it's cultural. People need to understand why clean data matters to them, not just to "the company."

Show the sales rep how clean data means their pipeline reports are accurate. Show the marketing team how good data means their campaigns reach the right people. Make it about their outcomes, not about compliance.

Need Help Setting This Up?

If your Salesforce org's data quality is holding you back, let's have a conversation about putting practical governance in place. No 200-page documents — just workable solutions.