Practical guides and perspectives on data observability, quality, and context from the Qelvora team.
Data observability is the ability to understand the health of data in your system at any given moment: freshness, volume, schema, lineage, and distribution. Learn how it differs from data monitoring, why it matters for AI-ready data, and how to implement it without agents.
Completeness, accuracy, consistency, timeliness, validity, and uniqueness: these six dimensions are the foundation of any meaningful data quality program. Discover how to measure each one, which dimensions matter most for your use case, and how no-code rules make scoring automatic.
High-quality data is necessary but not sufficient for AI. Without context such as business definitions, lineage, classification, and governance metadata, even perfectly clean data can produce unreliable models. This post explains what data context means, why it matters for LLMs and ML, and how automated discovery makes it achievable at scale.