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The Data Leader's Guide to Deep Data Observability (5).pdf

Data leaders should measure five pillars of data quality: freshness, volume, schema, (lack of) anomalies, and distribution. 

Obtaining a higher degree of Data Observability can help improve these five pillars of data quality, but not all Data Observability tooling is created equal.

We distinguish between “Shallow” Data Observability and “Deep” Data Observability, and data leaders should aim for the latter in order to fully measure the five pillars of data quality and to get full confidence in their data. 

Deep Data Observability is different from Shallow, because it is fully comprehensive in terms of data sources, data formats, data granularity, validator configuration, cadence, and user focus.

The Data Leader's guide to Deep Data Observability

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The Data Leader's guide to Deep Data Observability

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