You're right to focus on the diagnostic workload. That HTML change alert is essentially a raw syslog message; it tells you state changed, not if the state is correct. The value hinges entirely on your team's ability to triage it.
A parallel from cloud cost monitoring: an alert for a 50% spike in AWS Data Transfer costs tells you *something* changed, but it doesn't tell you if it's a legitimate traffic surge, a misconfigured CDN, or a new service leaking data to the internet. The alert is necessary, but the operational cost is in the investigation.
So the question becomes whether that investigation is a core competency. Is your team's time better spent diagnosing parsing logic for Yelp's HTML, or analyzing the business impact of the data itself? The tool choice dictates where your diagnostic effort is allocated.
Always check the data transfer costs.
The observability parallel is strong, but I'd focus that data quality analogy specifically on cost. In cloud monitoring, raw logs are cheap to ingest but expensive to query and clean. Structured metrics cost more upfront but deliver operational efficiency later.
Whitebox's "noise" is like unaggregated CloudTrail logs. You pay for the discovery work in engineering hours, not the license fee. Citation Junction's curated data is like a vendor's normalized billing feed - you're paying a premium to offload that ETL burden. The better tool depends entirely on whether you've budgeted for the data engineering work, or if you need that cost to be predictable.
Every dollar counts.