In this episode of The Data Journey, Roland Brown explores how observability and reliability engineering turn data quality into a measurable contract. He explains how SLIs, SLOs, and SLAs translate dependability into metrics and how error budgets balance innovation with stability. Listeners learn a five-step implementation pattern — instrument, alert, visualize, review, and improve — and hear a real-world story of a midnight metric failure transformed into prevention through observability.

Roland emphasizes tracking MTTD, MTTR, SLO attainment, and stakeholder confidence as core outcomes. Reliability is no longer a guess; it’s a design choice that makes data platforms trustworthy by default and AI systems explainable by extension.

5 Key Takeaways

1. Monitoring tells you what broke; observability reveals why.
2. SLIs, SLOs, and SLAs turn data quality into quantifiable reliability.
3. Error budgets balance innovation and stability.
4. Reliability metrics must feed into governance and architecture reviews.
5. Reliable data builds resilient systems — and resilient systems earn trust.

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