You're pinpointing the core tension between vendor abstraction and operational reality. Your point about raw counts lacking context echoes the distinction between key performance indicators and key risk indicators in security frameworks. A simple alarm volume tells you nothing about signal quality or resource burden.
The maintenance overhead you describe is essentially unplanned integration work, which a 2019 paper by Tamburri et al. on "Infrastructure Debt" quantifies as consuming up to 35% of platform teams' capacity. The script isn't just a patch, it's a liability on your balance sheet.
This also creates a measurement trap. By defining your own metric via script, you decouple from the vendor's benchmark data, making it impossible to compare your alarm fatigue levels with industry baselines they might publish. You're forced into a proprietary data model.
Nullius in verba
That idea to ask vendors for "glue code" examples during evaluation is a fantastic filter. I'm definitely adding that to my procurement checklist.
Your point about moving the conversation to contract language is key. I've seen this backfire though - if you push too hard on service credits for missing reports, some vendors just slap together a broken dashboard to check the box. Then you're stuck with an official but useless feature and lose the moral high ground to ask for a real fix.
It sometimes feels like you have to choose between a custom script you control and a poorly implemented feature you don't.
Yeah, you hit the nail on the head. The raw count is the most basic starting point, not a finished metric.
I've built a few of these for Splunk dashboards that were missing key filters. The real cost isn't the initial script, it's the downstream expectation. Once you hand that "alarm count" CSV to leadership, it becomes the official number. Then you're on the hook to explain every monthly fluctuation, but your script doesn't capture *why* - it just counts.
It pushes the analytical work back onto you, using a tool (PowerShell) that's not built for it. You end up building a janky data pipeline instead of analyzing the data.
Data is the new oil - but it's usually crude.
That's a good way to put it. The "script health" metric often goes unmonitored because we treat these solutions as temporary, when they inevitably become permanent fixtures.
I'd add that the risk isn't just the script breaking, but also the data source changing silently. The API call succeeds, but the schema or the meaning of a field shifts. You're still getting data, it's just wrong in a new way. That's harder to catch than a total failure.
This is why, for any critical metric like lead source costs, we now log a fingerprint of the raw data - like a count of unique campaign IDs returned - alongside the processed result. If that fingerprint changes dramatically, it's a signal to investigate even if the script itself is "healthy."
—Anita