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Copy file name to clipboardExpand all lines: src/content/articles/service-catalog-metadata-schema-ownership-tracking/index.mdx
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@@ -16,7 +16,7 @@ featured: true
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Service catalogs follow a depressingly predictable arc. At launch, you've got 90% coverage and 95% accuracy — teams are entering their services because the initiative has leadership attention. By year two, coverage has dropped to 45%, accuracy to 30%, and the catalog has become a punchline in onboarding jokes. Engineers ask in Slack instead of checking the catalog because they've learned they can't trust it.
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Here's what makes this worse: a catalog with 80% accurate data is more dangerous than no catalog at all. It gives you false confidence. You page the listed owner at 3 AM, confident you've got the right team, and waste twenty minutes before discovering they handed off the service six months ago. Every minute spent paging the wrong team is a minute your users are affected.
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<Highlighter>Here's what makes this worse: a catalog with 80% accurate data is more dangerous than no catalog at all. It gives you false confidence.</Highlighter> You page the listed owner at 3 AM, confident you've got the right team, and waste twenty minutes before discovering they handed off the service six months ago. Every minute spent paging the wrong team is a minute your users are affected.
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The fix isn't discipline or better training — it's ownership modeling that captures how teams actually work, combined with enforcement automation that keeps data accurate without relying on anyone remembering to update it.
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The ownership model that works distinguishes between different types of responsibility: a __primary owner__ who's responsible for the service's existence and development, plus __role-specific contacts__ for specialized functions.
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<Table
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variant="vertical-column-delineation-table"
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variant="numbered-review-table"
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content={{
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figure: 'Ownership roles and their triggers.',
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thead: {
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th: ['Role', 'Responsibility', 'When contacted'],
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},
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},
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],
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},
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figure: 'Ownership roles and their triggers.',
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}}
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/>
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For tier-1 and tier-2 services, make validation failures blocking. For tier-3 and tier-4, warn but allow the PR to merge — you want to reduce friction for less critical services while maintaining strict standards for critical ones.
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},
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{
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th: 'tier-4',
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td: ['Warn', '—', '—'],
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td: ['Warn', '---', '---'],
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},
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],
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},
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figure: 'Validation enforcement by tier.',
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}}
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/>
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A catalog without health metrics will silently decay. Measuring catalog health requires tracking three dimensions: coverage (what percentage is cataloged), accuracy (does the data reflect reality), and freshness (when was it last updated).
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