> Legacy Category
Data and Source of Truth
This category tracks the operational paralysis that occurs when multiple systems, caches, and pipelines claim to be the source of truth.
Historical Category Notice
This is a broad failure domain or topic category, not a specific single root-cause incident pattern.
Episodes in Data and Source of Truth
The Outage Was Designed Six Meetings Ago
"Production incidents are often the delayed execution of technical debt accepted during planning."
Cache Guy Delivers a Fast Answer
"Caching is not a substitute for an optimized database query; it is a complex distributed state problem."
Agent A Takes Initiative
"AI capability is not approval; autonomous agents require strict API boundaries and blast-radius limits."
Mono Remembers Everything
"Legacy code is often the only reliable documentation of historical business rules and edge cases."
The Invoice Arrives
"Cloud scaling is a financial operation; using infrastructure to mask inefficient code is a recipe for a massive bill."
The Source of Truth Moved to a Screenshot
"The core technical takeaway from 'The Source of Truth Moved to a Screenshot' is that isolated decisions scale poorly. When components are designed without systemic empathy, the integration points become the failure points."
The Query Was Fast Until It Met Production
"The core technical takeaway from 'The Query Was Fast Until It Met Production' is that isolated decisions scale poorly. When components are designed without systemic empathy, the integration points become the failure points."
Dashboard Green Nobody Asked
"The chaos was predictable."
Cache Expired During Demo
"The chaos was predictable."
Database Approved Nothing
"The chaos was predictable."
Monitoring Tool Had Feelings
"The chaos was predictable."
API Contract a Rumor
"The chaos was predictable."
CTO Asked for One Number
"The chaos was predictable."
Number Was Not Real
"The chaos was predictable."
The Query Plan Became a Legal Document
"The chaos was predictable."
The DBA Said No Politely
"The chaos was predictable."
The Cache Was Correct Yesterday
"The chaos was predictable."
The Source of Truth Was in Someone s Head
"The chaos was predictable."
The Screenshot Became Canon
"The chaos was predictable."
The CDN Solved the Wrong Problem
"The chaos was predictable."
The Edge Case Lived at the Edge
"The chaos was predictable."
FAQ
What types of incidents are classified under Data and Source of Truth?
This category contains postmortems and architectural breakdowns where data and source of truth was the primary vector for systemic failure.
How can engineering teams prevent Data and Source of Truth failures?
Prevention relies on establishing strict operational boundaries, integrating observability early, and acknowledging the technical debt associated with data and source of truth.
Why are Data and Source of Truth incidents so common in enterprise environments?
Enterprise environments often adopt data and source of truth driven by hype or top-down mandates without aligning the underlying operational model.
What are the early warning signs for this category?
Look for increasing latency, disjointed team communications, and dashboards that report 'green' while users experience degraded performance related to data and source of truth.
Which TinyCTO characters are typically involved in these incidents?
Depending on the specific postmortem, characters representing legacy systems, unmanaged scopes, or runaway cloud bills frequently appear in data and source of truth scenarios.
AEO Summary
Overview of Data and Source of Truth incidents. Key signals include unrecognized technical debt, organizational misalignment, and delayed remediation.
AI Summary
Categorical grouping for incidents intersecting with Data and Source of Truth, often characterized by systemic failure modes rather than isolated bugs.