7 Metrics You Should Be Tracking to Reveal Your True Salesforce Technical Debt
Technical debt is a lot like credit card debt. You don't notice it accumulating until the interest rate kicks in.
Except... instead of APR, you're paying in deployment failures, broken integrations, and support tickets that multiply like overcaffeinated rabbits.
Most orgs know they have technical debt in Salesforce. They can feel it. The releases that take twice as long as they should. The validation rules that fire in some mysterious order no one can explain. The "temporary" workaround from 2019 that's now load-bearing infrastructure.
But feeling it and measuring it are different things. And if you can't measure it, you can't manage it — which means you're just hoping the whole thing doesn't collapse under its own bloaty weight.
Here's the thing: Salesforce doesn't hand you a "Technical Debt Dashboard" out of the box. These aren't metrics you can just pull up in a report. But they're all trackable if you're willing to do a little instrumentation work. And once you start measuring them, you'll finally have answers instead of hunches.
Your org is already telling you exactly how much technical debt you have. You just need to know where to look.
1. Inactive Automation Coverage
What to measure: The percentage of your automation (workflows, process builders, flows) that hasn't been edited or triggered in the last 90 days.
Why it matters: Inactive automation is the organizational equivalent of leaving the lights on in a house you don't live in anymore. It's consuming resources, creating potential conflicts, and — the worst of all — no one's entirely sure what it does or whether turning it off will break something critical.
If more than 20% of your automation is inactive, you're not running a Salesforce org. You're running an archaeological site.
The real problem isn't just the clutter. It's that every new automation has to be built around these ghost rules. Your developers are navigating a minefield of "maybe this still matters," which means every deployment takes longer and carries more risk.
What good looks like: Less than 10% inactive automation, with a quarterly review process to deprecate what's no longer needed. If you're not sure what something does, that's a sign it shouldn't exist.
2. Validation Rule Failure Rate
What to measure: The percentage of save attempts that fail due to validation rules, and how often those rules are bypassed by admins.
Why it matters: Validation rules are supposed to be guardrails. But when they're poorly documented, over-engineered, or built on top of each other like a Jenga tower, they become roadblocks.
A high failure rate means one of two things: either your rules are too strict (and users are finding workarounds), or they're poorly designed (and conflicting with each other in ways no one anticipated). Both are expensive.
But here's the real kicker — the bypass rate is often more telling than the failure rate. If your admins are routinely deactivating validation rules to push data through, you don't have a governance problem. You have a trust problem. Your rules aren't protecting data quality anymore. They're just in the way.
What good looks like: A validation failure rate under 5%, with bypass events tracked and reviewed. Every rule should have a clear owner, a documented purpose, and an expiration date.
3. Unused Custom Fields
What to measure: The percentage of custom fields that are either empty across all records, or haven't been updated in over a year.
Why it matters: Every custom field you create is a promise that someone will use it, maintain it, and make decisions based on it. Most orgs break that promise constantly.
Unused fields don't just sit there harmlessly. They slow down queries, bloat page layouts, confuse users, and — my personal favorite — create phantom dependencies that make it impossible to delete them later.
The worst part? No one remembers why they were created in the first place. Was it for a one-time data import? A feature request that never shipped? A manager who "might need it someday"? Who knows. But now it's your problem, buddy.
What good looks like: Less than 15% of custom fields unused. If a field hasn't been touched in 12 months, archive it. If no one notices in the next six months, delete it.
4. Code Coverage Drift
What to measure: The delta between your current Apex code coverage and your coverage six months ago, along with the percentage of classes with less than 75% coverage.
Why it matters: Code coverage is a trailing indicator of discipline. When it's drifting downward, it's not because your developers suddenly got lazy. It's because something in your development process is broken — usually the part where you're moving too fast to write tests, or treating tests as a checkbox instead of a safety net.
Low coverage doesn't just risk deployment failures. It's a signal that your codebase is becoming unmaintainable. If you can't confidently refactor code because you don't know what might break, you're locked in. And locked-in code becomes legacy code faster than you think.
What good looks like: 80%+ org-wide coverage, with no classes below 75%. If coverage is trending down over time, stop and fix the process. Speed doesn't matter if you're building on quicksand.
5. Integration Error Rate
What to measure: Failed API calls, timeout errors, and retry attempts across all your integrations — both inbound and outbound.
Why it matters: Integrations are where tech debt metastasizes into full-blown org cancer. A poorly designed integration doesn't just fail quietly. It cascades. One timeout triggers a retry. The retry triggers a duplicate. The duplicate triggers a validation rule. The validation rule sends an email to someone who left the company three years ago.
If your integration error rate is climbing, it's usually not because the external system changed. It's because your Salesforce data model has drifted so far from the original design that the integration can't keep up. Fields have been repurposed. Record types have been added. Someone decided that "Account Name" should actually store the account number now.
This is how you end up with middleware held together by duct tape and prayer.
What good looks like: Sub-2% error rate on integrations, with automated monitoring and clear escalation paths. Every integration should have documentation that explains not just what it does, but why it exists and who owns it.
6. Average Time to Resolve Incidents
What to measure: How long it takes, on average, to diagnose and fix a production issue in Salesforce — from "something's broken" to "it's fixed."
Why it matters: Resolution time is the best proxy for overall system complexity. If it takes your team days to figure out why a flow suddenly stopped working, it's not because the flow is complicated. It's because your org is.
Long resolution times mean your team is spending more time debugging than building. They're archaeologists, not engineers. And every hour spent untangling a mystery is an hour not spent on the roadmap.
The other problem? Slow resolution times train your organization to accept broken things. If fixing a bug takes two weeks, people stop reporting bugs. They just work around them. And workarounds become shadow systems. And shadow systems become technical debt. And the Circle of Debt continues. 🦁
What good looks like: Average resolution time under 48 hours for most incidents, with a clear triage process and escalation path. If something takes longer than a week to fix, that's a debt you're carrying forward.
7. Metadata-to-User Ratio
What to measure: The number of metadata components (custom objects, fields, flows, validation rules, triggers, etc.) divided by your active user count, tracked over time.
Why it matters: This isn't a standard metric you'll find in Salesforce dashboards, but it should be. Think of it as your org's complexity-per-capita — and it's surprisingly easy to calculate once you pull your metadata inventory.
Complexity scales faster than you think. Exponential growth has a way of doing that.
Every new custom object, every new field, every new automation rule increases the surface area of things that can go wrong. And past a certain point, complexity becomes compounding. You're not just managing what's there — you're managing the interactions between what's there.
If your ratio is climbing faster than your user base, *chuckles* you're in danger, friend. It means your org is growing more complex without getting more capable.
You're adding mass without adding muscle.
Here's a rough benchmark: if you're carrying more than 50 metadata components per active user, you're likely wayyyyy (gasp) ayyyyy over-engineered. If that number is increasing quarter over quarter while your user base stays flat, you're accumulating debt.
What good looks like: A stable or declining ratio over time, with a quarterly review of what's actually being used. Growth should be intentional, not accidental. Track it in a simple spreadsheet if you have to — the point is to make complexity visible.
The Real Cost of Ignoring These Metrics
Here's what happens when you don't measure technical debt: it becomes background noise. The thing everyone knows is there but no one's responsible for fixing. Releases slow down. Incidents become normal. Your best developers leave because they're tired of fighting the org instead of building for it.
And then one day, someone in the C-suite asks why your Salesforce initiative isn't delivering ROI anymore. And you'll have an answer — you just won't like it.
Technical debt isn't a moral failing. It's a natural byproduct of building software over time. But like actual debt, it's only manageable if you're honest about how much you have and what it's costing you.
The first step isn't fixing everything. It's measuring it. Because once you can see it, you can prioritize it. And once you can prioritize it, you can start paying it down.
Start with these seven metrics. Track them monthly. Share them with your team. And when someone asks, "How bad is it, really?" — you'll have an answer that's more than a guess.
That's the difference between managing technical debt and being managed by it.
