Скачать или смотреть Data Quality vs Data Observability: Key Differences Explained

Confused by "data quality" and "data observability"? They're not interchangeable—quality validates the data (accuracy, completeness), while observability monitors the system (freshness, anomalies) for proactive trust. Discover the real differences, why tools blur lines, and how they partner like a smoke detector (early alerts) and firefighter (damage control).

Using a sensor misconfiguration example, see how observability catches upstream issues before bad data hits. Build resilient pipelines: reliable by design, not exception!

Based on our latest Datagaps blog: https://www.datagaps.com/blog/data-ob...
Watch the recent blog series:    • Datagaps Insights: Video Blog  

Timestamps:
00:00 - Intro: Why the Confusion?
00:24 - The Shared Goal: Building Data Trust
00:59 - Data Quality: Final Inspection & Validation
01:46 - Data Observability: System Health & Journey
02:04 - In a Nutshell: Correctness vs. Reliability
02:24 - Pattern: Catch-Up vs. Future-Proofing
02:36 - Why the Blur? Tool Overlaps
03:22 - Real Example: Fahrenheit Sensor Glitch
03:38 - Quality Flags the Error (Too Late)
04:00 - Observability Catches It Upstream
04:21 - The Partnership: Early Warnings + Final Checks
04:44 - Shift: From Reactive Fixes to Proactive Stewardship
05:04 - Roadmap: Observability First, Quality Last
05:27 - Final Question: Reliable by Design?
05:42 - CTA: Contact & Free Demo

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