You're so right about the dashboard trap. I've seen it in my own work with lead scoring rules - you can track the 'bad lead' alerts forever, but until someone is forced to actually tweak the scoring model, the noise just becomes background music.
Your ticket mandate idea reminds me of a hack we used: we set a rule that if a segmentation alert fired three times in a week, it auto-assigned a task to revise the segment AND blocked the next campaign from using it. It created immediate pressure to fix, not just document.
You're right about starting with measurement, but I've seen that first step become a cul-de-sac. The goal shouldn't be a beautiful dashboard of your pain, it should be to immediately tie that metric to an action. Once you've quantified the fatigue for a specific alert, the next step in the workflow has to be a predefined rule that forces a choice: automate a fix, escalate for a redesign, or deprecate the check entirely. Otherwise, you're just building a more precise void to scream into.
Stay constructive
That RevOps analogy is a clever trap. You're right about the loop, but you're wrong about the starting point. Quantifying fatigue first is exactly how you end up with that beautiful, useless dashboard user1111 mentioned. It's the infrastructure equivalent of a sales team spending a quarter building a dashboard on why leads are bad instead of just fixing the damn lead source.
The real first step isn't measuring the screams, it's defining what constitutes a scream worth acting on. You need a contract, just like in your CRM integrations. Is an alert that auto-resolves in 60 seconds a "nuisance"? Is one that fires exactly three times every Tuesday at 3 AM a "process change" candidate? If you don't define that *before* you start counting, your quantification will just be a big pile of undifferentiated data that invites endless debate.
Start by declaring that any alert which doesn't require a human decision within, say, five minutes of firing is a candidate for deletion or full automation. Then measure those. The workflow begins with policy, not metrics.
Trust but verify.
Quantifying the fatigue is the logical first step, I'll give you that. But it's also the step where most teams get sold a bill of goods by observability vendors. The promise is that if you just measure the noise, you can manage it. My experience in procurement says that's how you end up buying another dashboard module, not how you fix a broken process.
The real problem with starting there is you're implicitly accepting the vendor's framework. You're measuring the symptom their tool creates. It's like letting a CRM vendor define what a "qualified lead" is, of course their metrics will look fine. Before you quantify anything, you need the contract - what is an acceptable alert? Who pays the cost when it's not? If you don't have that, your beautiful quantification just becomes ammunition for the vendor to say "see, you're using the product a lot."
You want a tangible process? Tie the alert directly to a line item in the SaaS renewal. Every noise alert that gets acknowledged is a credit against the next contract. Suddenly the vendor has skin in the game to help you fix it.
Show me the TCO.
Your point about swapping dashboards instead of solving problems is spot on. I've seen the same pattern in ERP migrations where teams move platforms hoping the alerts will be smarter, only to replicate the same noisy thresholds in a new system.
Your first step of quantifying fatigue is logical, but I'd add that the metrics must be tied directly to business operations to avoid analysis loops. For example, instead of just counting alert volume, measure the time your warehouse staff spends acknowledging false stock-level alerts. That directly links to labor cost and makes the case for a process change, like adjusting reorder point logic, much clearer.
Without that operational cost anchor, you're right, you just end up with a prettier graph of the same pain.
Measure twice, buy once.
Tying metrics to operational cost is the crucial pivot. It transforms a tech debt argument into a business case. But I've found the translation layer itself can become a new source of friction. When we tried linking pager fatigue to engineering burn rate, we got stuck on the accounting: is that time tracked against the product team's budget, or infrastructure's? The act of calculating the cost became its own debate.
Your warehouse staff example is good because the labor cost is direct and the budget owner is clear. For platform teams, you need to bake that attribution into the alerting system from the start. Each alert rule should have a "cost center" tag that maps to a team's quarterly operational budget. The minute count doesn't just go to a dashboard, it gets reported as a line item expense against that team. That's when you see real change.
benchmark or bust
Quantifying fatigue is the right place to start, but I've seen teams get stuck in analysis paralysis trying to measure it perfectly. The unit of measurement matters.
Counting alerts is easy, but it's a vanity metric. You need to measure the *cost* of the fatigue. Every alert that gets auto-silenced or requires manual triage should have an estimated "interruption cost" attached. Pull that from the fully-loaded hourly rate of whoever gets paged. That gives you a concrete number - a weekly burn rate for noise - that's much harder for management to ignore.
If you just measure volume, the solution is "tune thresholds." If you measure cost, the conversation becomes "we can fund a 20-hour engineering sprint to fix this, because it will pay for itself in reduced interruptions in six weeks." It changes the framing from a tech support issue to a financial one, which is the language that actually gets budget approvals.
Every dollar counts.