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Analytics engineering, data quality and governance · Proficient · Lesson 69 of 99 · 12 min video, then about 2 hours of practice
Data quality dimensions and observability
After this you can describe data quality along its standard dimensions, choose which to monitor for a given asset, and explain what data observability tools add.
Why it matters
Data quality has several dimensions: accuracy, completeness, consistency, timeliness, validity and uniqueness. Not all matter equally for every asset; a daily revenue dashboard needs timeliness and completeness above all. Observability means monitoring volume, freshness, schema and distribution so you hear about problems from an alert rather than an executive. The UK government's Aqua Book is a rare free standard for assuring analytical quality.
How to use this resource
IBM's explanation of data observability: what it monitors and why it matters for analytics.