Episode 59 — 5.4 Monitor Data Health: Profiling, Quality Metrics, Data Drift, Automated Checks, ISO
Échec de l'ajout au panier.
Échec de l'ajout à la liste d'envies.
Échec de la suppression de la liste d’envies.
Échec du suivi du balado
Ne plus suivre le balado a échoué
Episode 59 — 5.4 Monitor Data Health: Profiling, Quality Metrics, Data Drift, Automated Checks, ISO
-
Narrateur(s):
-
Auteur(s):
À propos de cet audio
This episode explains data health monitoring as the early warning system that keeps pipelines and reports reliable, which DA0-002 tests through scenarios involving silent failures, unexpected pattern shifts, or deteriorating quality. You will define profiling as learning what “normal” looks like in ranges, distributions, and categorical frequencies, and you will connect that baseline to quality metrics like completeness, accuracy, and timeliness. Data drift is framed as pattern change over time, which can be expected in some contexts but alarming in others, especially when it breaks model assumptions or changes KPI meaning. Automated checks are treated as scalable controls that surface issues without manual inspection, while ISO is referenced as a mindset of consistent process, evidence, and continual improvement rather than a requirement to memorize a standard. The objective is to understand what to monitor, why it matters, and how monitoring supports governance and trust.