Comparison
Data observabilityvsData quality
Data observability
you get told that a table's volume, freshness and schema look unusual without anyone having written a test for that table.
Automatically monitoring datasets for anomalies in freshness, volume, schema and distribution, across everything rather than where someone thought to look. Its complement is testing, which encodes specific known expectations; observability finds the problems nobody anticipated on the tables nobody owns. It generates far more noise than tests and is worth it mainly at a table count where hand-written coverage has stopped being plausible.
Full entry →Data quality
nothing errored, every job is green, and the number on the board is wrong by eleven percent.
Whether data is fit for the decision someone is about to make with it. It is deliberately a relative standard: the customer table that is perfectly adequate for a monthly trend is unusable for sending letters. Treating it as an absolute produces either endless tests nobody reads or none at all, so the useful version always starts with what the data is for and who is harmed when it is wrong.
Full entry →