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 Capabilities

Ensure trusted, reliable data with automated data quality, anomaly detection, semantic discovery, and issue resolution.

Issue Management

Gain complete visibility into data quality issues, prioritize effectively, and accelerate resolution with DQLabs’ intelligent issue management.

Improved Governance

Ensure secure access, seamless catalog integration, and domain-based governance for better data quality and compliance at scale.

Smart Native Connectors

Out-of-the-box connectivity to integrate with every part of the data stack: from warehouses and lakehouses to transformation tools and BI platforms.

Business Dashboards and Insights

DQLabs provides out-of-the box dashboards for data leaders, engineers, and scientists for their specific data quality needs.

Data Profiling and Anomaly Detection

Automate profiling, monitor data health, and detect unknown issues with AI-driven anomaly detection.

Drift and Behavior Analysis

Automatically detect anomalies, track key metrics, and adapt to data changes with ML-driven insights.

Data Quality Dashboards

Identify top and bottom-performing assets, leverage supervised learning for accurate scoring, and enhance organization-wide data quality with hierarchical scoring.

Semantic Discovery

Automatically categorize and organize your data for better visibility, governance, and actionable insights.

Usage Observability

Optimize query performance, gain visibility into data consumption trends, and reduce compute costs with comprehensive usage observability.

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On a mission to help organizations improve data trust.

DQLabs is an Agentic AI-powered Data Observability & Data Quality platform that enables data producers, consumers, and leaders to turn data into action faster, easier, and more collaboratively. Observe, measure, discover, and remediate the data that matters.

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