What is the difference between Data Quality and Observability in DataOps?
Data Quality is static. It is the measure of data sets at any point in time.
Data Observability is dynamic — it is the testing of data, integrated data, and tools acting upon data — as it is processed — that checks for flow rates and data errors.
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A financial analogy: Data Quality is your Balance Sheet, Data Observability is your Cash Flow Statement
Crafting your data observations into a singular Data Journey that integrates all tools, tech, data, and results in a single view .. that is DataOps Observability.
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Another financial analogy: DataOps Observability is like a Profit and Loss Statement for your data business.
How is DataOps Observability different from Data Observability?
Data Observability tools test data in the database. While this is a fine thing, DataKitchen has been promoting the idea of tests for many years (and in our DataOps Automation Product!). You need to correlate that information with other critical elements of the data journey – where a fundamental understanding is required.
