How Data Observability Helps You Discover Hidden Data Value

Treasure hunters and enterprise data teams share similarities in their quest for discovery. Both rely on specialized tools to navigate through vast areas, whether it's a physical terrain or an ocean of data. Data observability is crucial for data teams as it helps improve data discovery by eliminating the need for manual location of files, updating catalogs, and maintaining metadata logs. This leads to better return on investment in data and increased value. Data environments are constantly evolving, making continuous evaluation necessary. Data observability tools help pinpoint where changes should be made and advantages capitalized upon to uncover data value. However, discovering the right data can be challenging due to information silos within enterprises. Different teams often create separate datasets based on their specific needs, leading to limited cross-functional insights. Manual code, catalogs, and metadata work but are not scalable as they cannot keep up with the increasing volume, variety, and velocity of incoming data. Data observability improves data discovery at scale by identifying data quality and reliability issues, checking for schema and data drift, reconciling data in motion, and offering self-service catalogs and metadata profiles. Advancements in data observability eliminate time-consuming manual processes of finding the data needed, automatically updating data catalogs and metadata profiles across all data sources, making them rapidly discoverable.