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.

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- DQLabs Platform
One unified platform for data leaders, engineers, and analysts—offering observability, quality, discovery, and remediation.
- Agentic AI Data Management
An autonomous, Agentic AI driven capabilities to continuously improve management of data issues.
- DQLabs Capabilities
Automated data quality, anomaly detection, semantics-driven discovery, and issue resolution for trusted and reliable data.
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Monitor data, pipelines, and usage—detect anomalies, ensure reliability, and optimize the health of your data systems.
- Data Quality
Automate data quality with no-code checks, anomaly detection, lineage, and governance for trusted, accurate, and reliable data.
- Data Discovery
Discover data with semantics-driven categorization, advanced search, and catalog integration for faster, actionable insights.
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Bring context and clarity to your data quality with DQLabs’ semantics-driven data discovery.

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Common Data Discovery Challenges

Search Across Complex Environments

Manually searching through a vast and fragmented data landscape makes it difficult for users to find relevant data quickly, leading to inefficiencies and delays in decision-making.

Lack of Data Context

Without semantics-driven discovery, data often lack the necessary context, making it challenging for teams to interpret and categorize data accurately for their specific use cases.

Discoverability for Non-Technical Users

Without a user-friendly interface or proper categorization, business teams struggle to access data quality insights and need to rely on technical teams, causing bottlenecks and hindering quick decision-making.

Centralized View of Data Quality

Get a comprehensive view of data quality across all assets in one place, enabling faster issue detection and better decision-making.

Bi-Directional Catalog Integration

Enable seamless bi-directional integration with your data catalogs to enhance data quality management. DQLabs pulls metadata, runs quality checks, and pushes insights back, ensuring complete visibility of data health within your catalog.

Semantics-Driven Discovery

Automatically categorize data by domain, application, and tags for an organized, easy-to-navigate data landscape.

Self-Service Data Discovery

Empower business users with intuitive access to contextualized data quality insights without technical expertise.

Frequently Asked Questions

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.