Why identify tech debt and redundant metadata?
Over time, Salesforce orgs accumulate layers of unused or low-value components—legacy record types, inactive automations, and unpopulated custom fields. Left unchecked, this clutter:
Increases platform complexity and overhead.
Slows down org performance and complicates testing.
Obscures business logic and security models.
Raises technical risk and administrative burden.
By identifying and managing tech debt proactively, you improve platform agility, simplify governance, and reduce long-term costs.
When to identify tech debt and redundancies?
Run this analysis:
During quarterly org health checks.
Before refactoring automations or major deployments.
During Salesforce org consolidation or optimization projects.
After major shifts in business process or data usage patterns.
Avoid running it:
In non-production environments not representative of live data.
During active development spikes with unstable metadata.
Prerequisites
A synced Salesforce Metadata Dictionary.
Required Elements.cloud permissions:
Org model viewer: View results and share dashboards.
Org model editor: Run and save queries.
Org model manager: Delete saved queries.
Perform Metadata Tech Debt Identification
Step 1: Access the Object Analysis Dashboard
Go to Metadata Dictionary.
Click Analytics 360 (top-right).
In the left pane, under Objects, select Object analysis.
Step 2: Run a Targeted Analysis
Use the Default analysis for an initial sweep.
For deeper insights, click Home > New analysis:
Select All dependencies.
Use filters:
Inactive automations
Exclude managed package components
Focus on Standard and Custom objects.
Step 3: Investigate Key Tech Debt Indicators
Use these thresholds and filters based on Salesforce best practices and the Well-Architected Framework:
Metadata Type | Threshold | Actionable Insight |
Record Types | >5/object | Over-customization; verify business need. |
Apex Triggers | >2/object | May conflict or create test complexity. |
Validation Rules | >10/object | Review for consolidation. |
Flows | >5 versions | Streamline or deactivate unused logic. |
Inactive Automation | Any | Validate safe to delete. |
Fields (Custom) | <10% populated | Likely redundant; validate and deprecate. |
Step 4: Drill Down to Inspect Components
Click on any object name or metadata stack.
Use drill-down views to inspect:
Inactive components
Record type usage
Non-managed Apex triggers
High-count validation rules
Custom fields with low data population
For field analysis:
Sort by Field population and Field impact.
Identify fields with:
Low impact
<10% population
Run Search for References:
If results return zero usage:
Update Assessment Status (e.g., Candidate for Deprecation).
Add to a cleanup story.
Step 5: Take Action and Track
From the list view:
Bulk-select redundant components.
Perform actions:
Tag for cleanup (low-value, legacy, etc.).
Create or link to stories for documentation and tracking.
Update assessment status to inform future audits.
Search for references to ensure safety before removal.
Outcome & Next Steps
You now have:
A data-backed view of your org’s technical debt.
A prioritized list of components for remediation.
Stories to track and execute the cleanup effort.
Next steps:
Embed this analysis into regular release cycles.
Pair with metadata impact reports for dependency-aware cleanup.
Use MetaFields to track tech debt and audit compliance.