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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Data Stewards

Ensure Data Quality and Governance at Scale

Maintain data integrity, enforce governance policies, and ensure compliance with automated monitoring and end-to-end lineage.

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Why Data Stewards Choose DQLabs

Maintain Data
Integrity

Ensure consistent, accurate, and reliable data across systems with continuous automated quality monitoring.

Enforce Governance Policies

Automate policy enforcement through configurable data quality rules and real-time alerts to uphold organizational standards.

Ensure Compliance with End-to-End Lineage

Track data flow and transformations across the data lifecycle to support auditing, accountability, and regulatory requirements.

How DQLabs Empowers Data Stewards

Automate governance enforcement, gain full visibility into data lineage, and monitor data quality continuously to ensure trusted, compliant data across the enterprise.

Implementing Domain-Based Agile Governance

The Problem: Lack of domain-specific governance hampers data quality enforcement and obscures visibility across functions, systems, and teams.
The Solution: DQLabs enables categorization of data by business domains—such as banking or sales—enhancing visibility and governance across functions, systems, and teams.

Enforcing Role-Based Access Controls

The Problem: Without granular access controls, organizations risk unauthorized data access, compromising security and compliance.
The Solution: DQLabs ensures secure and compliant data access with granular role-based controls, allowing assignment of predefined roles—Admin, Owner, or User—to manage privileges and safeguard sensitive data.

Integrating Disparate Data Catalogs

The Problem: Managing data assets across multiple platforms without seamless integration leads to fragmented data governance and compliance challenges.
The Solution: DQLabs integrates effortlessly with leading data catalog and governance platforms like Collibra, Alation, and Atlan—streamlining data asset management, quality control, and regulatory compliance.

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