What is data discovery, and why is it important?

Data discovery helps organizations locate and categorize assets and assess their data quality efficiently. It provides business context for data and ensures that teams can trust the data they use for decision-making.

How does DQLabs’ semantics-driven discovery improve data understanding?

DQLabs automatically categorizes data assets using semantic layers, tagging them by domain, application, and other attributes, making it easier to navigate and analyze data quality.

How does DQLabs help with fragmented and scattered data?

DQLabs centralizes data quality insights, offering a unified view across all assets, helping teams quickly find and assess their data for increased trust in their data landscape.

Can non-technical users leverage DQLabs for data discovery?

Yes, DQLabs provides self-service data discovery with an intuitive interface, enabling business users to access contextualized data insights without technical expertise.

What types of filters does DQLabs offer for data search?

DQLabs provides advanced filtering capabilities based on attributes like data source, domain, data quality score, and other metadata, making searches more precise and efficient.

The need for Data Quality-Centric Data Observability

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