Every Salesforce organization accumulates baggage. Fields are created for specific business needs that evolve over time. A field added for a pilot program five years ago. Another created for a report that nobody runs anymore. A third added "just in case" and never actually used.
You probably have hundreds of them. And they're silently costing you—in performance, storage, team frustration, and missed opportunities to modernize.
This is the first post in a series about removing technical debt from your Salesforce org. We're starting here because unused fields are the most tangible form of organizational clutter, and cleaning them up delivers immediate, measurable benefits. More importantly, it's the foundation for everything that follows.
Most Salesforce admins think of unused fields as an aesthetic problem. They clutter the UI. They make data models harder to understand. But the costs go far deeper.
Every field in your Salesforce org consumes resources. When a record is queried, loaded, or processed, all of those fields are included. A Contact object with 300 fields instead of 100 means more data moving through your system with every operation.
This compounds in scale. A flow that processes 10,000 records? That's potentially millions of field values being read and written. Batch Apex jobs? Same story. Reports pulling thousands of records? The query engine has to work harder.
In mature Salesforce organizations, field bloat is one of the primary reasons for slow page loads and query performance degradation. You won't see dramatic differences in isolation, but across thousands of daily operations, those small delays accumulate into real user frustration.
Salesforce charges per gigabyte for data storage. Every unused field occupies space in your database. More importantly, every backup of your org includes those unused fields. If you're using Salesforce's data recovery services or maintaining sandbox copies, you're paying to store data you don't use.
For organizations managing data privacy and compliance requirements, this becomes even more critical—you're storing and protecting data that adds no business value.
Unused fields create cognitive overhead. When an admin looks at an object schema with 300+ fields, finding what they need becomes harder. When a developer needs to understand the data model, they waste time deciphering which fields are actually used versus which are historical artifacts.
Field creep directly impacts onboarding time for new team members. It contributes to inconsistent data quality because nobody understands the full picture anymore. It makes validation rule logic harder to maintain. It complicates field dependency mappings.
This might sound intangible, but it's real: teams working in bloated orgs feel the burden. There's a psychological weight to maintaining systems that feel cluttered and out of control. It signals that the organization doesn't invest in cleanliness, which influences how people approach new customizations.
Clean systems inspire clean thinking. When your data model is intentional and well-maintained, your team respects it more and treats it accordingly.
Most Salesforce organizations are surprised when they audit their fields. Here's what we typically see:
If your Account object has 150 fields, you might be carrying 45-75 unused ones.
The key to field cleanup is systematic identification. You can't safely remove fields without knowing what's actually being used. Here's how to build a complete picture:
Salesforce doesn't provide a built-in "field usage" report, but you have several options:
Salesforce Native Tools:
Third-Party Tools:
Manual Inspection:
Create a checklist. For each field you suspect is unused, verify it's not referenced in:
Talk to your team. Which fields do people actually use? Which ones have people complained about seeing in record layouts? This qualitative data often reveals things that automated scanning misses.
Document your findings:
Risk Levels:
Not all fields can be deleted immediately. Consider:
Safe removal process:
Most fields can be deleted immediately if they have no references, but use a phased approach for fields with dependencies.
After your first field cleanup pass, track these metrics:
Document these wins. They justify the effort and provide motivation for the next cleanup phase.
The dangerous part of field cleanup is the backsliding. Six months after your cleanup pass, you'll start accumulating new unused fields again unless you establish governance:
Create a field lifecycle policy:
Tools to support this:
Removing unused fields is the first step. But field bloat is usually symptomatic of a larger issue: technical debt accumulation.
In Part 2, we'll expand beyond fields to tackle other sources of technical debt: custom objects you don't need, legacy automation (Workflow Rules and Process Builder) that should be replaced with Flows, and dead code cluttering your Apex namespaces.
The cleanup you do now will make that next phase easier and faster. A clean foundation enables modernization.
The hardest part is getting started. Once you remove your first batch of unused fields and see the impact, momentum builds naturally.
In two weeks, we'll publish Part 2: "Beyond Fields: Clearing Out Custom Objects, Workflows, and Dead Code." We'll tackle the broader technical debt landscape and help you identify which custom objects, automations, and code should be retired.
Subscribe to be notified when it publishes.
What's your biggest frustration with field management in your Salesforce org? Share in the comments below, or reach out—we'd love to hear your cleanup stories and challenges.