Data governance you can trust

Data governance is the framework of policies, roles, processes, and standards that define how data should be managed and used across an organization.

Data governance includes:

• Access control and permissions.
• Policy modeling and approval workflows.
• Classification and sensitivity labeling.
• Retention and lifecycle management.
• Privacy and regulatory compliance.
• Stewardship and ownership assignments.
• Risk and impact evaluation.

Governance defines what must happen with data—but it does not always provide the visibility required to execute those rules.

Data governance empowers an organization to scale data trust. It ensures that all teams, from analytics to the C-suite, are using the same reliable data.

In essence, data governance is important for four key reasons:

• Better Decision-Making: It establishes a “single source of truth,” providing leaders with reliable, high-quality data they can trust to make informed strategic choices.
• Improved Efficiency: It breaks down data silos and streamlines processes, reducing wasted resources and improving collaboration across the organization.
• Regulatory Compliance & Risk Mitigation: It provides the necessary controls to comply with regulations like GDPR and HIPAA, protecting the organization from costly fines and data breaches.
• Increased Data Value: By enforcing standards for quality and security, it turns raw data into a trustworthy asset that can be confidently used for analytics, AI, and building customer trust.

Ultimately, effective data governance unlocks the true potential of data, transforming it from a simple byproduct of business into a critical driver of innovation and competitive advantage.

By providing lineage, metadata, trust indicators, and observability that make governance enforceable at scale.

Only partially. Policies can be defined, but without intelligence, they cannot be consistently applied or audited.

Data governance provides the foundation that transforms AI into production-ready, trustworthy systems. Actian enables it through:

AI-Ready Data Foundation

• Comprehensive metadata management creates clean, accessible, and governed data.
• Automated context enrichment captures quality indicators needed for model training.
• Trust indicators enable AI teams to assess data reliability before deployment.

Knowledge Graph Intelligence

• A federated knowledge graph provides rich relationships and context.
• Business glossary integration ensures AI systems understand the semantic meaning.
• Automated relationship mapping supports advanced AI applications.

Production Readiness

• Consistent governance policies scale AI initiatives beyond isolated experiments.
• Quality metrics and lineage tracking enable confident model deployment.
• Automated compliance monitoring ensures AI systems meet regulatory requirements.

Without data governance, AI initiatives struggle to move from proof-of-concept to production deployment. Actian creates the metadata-rich environment that AI systems require for trustworthy, scalable implementation.

Yes. It connects classification, access rules, and policy logic directly to data operations.

Actian deliver comprehensive governance through:

Simplified Data Stewardship

• Template-driven documentation with a drag-and-drop interface.
• Custom properties and fields that align with organizational requirements.
• Collaborative documentation that allows teams to discuss data assets.
• Smart recommendations for reducing manual tagging overhead.

Regulatory Compliance

• Automated identification, classification, and management of data assets at scale.
• Centralized policy enforcement through a federated knowledge graph architecture.
• Comprehensive audit trails for compliance reporting.

Business Glossary Automation

• Consistent business language across all data consumers within the organization.
• Automated mapping between business concepts and technical assets.
• Interactive relationship visualization through a 360-degree view.

Learn more about data stewardship and our business glossary capabilities.

Data intelligence unifies metadata, lineage, cataloging, governance signals, quality metrics, observability, and data products and contracts into a searchable, explainable, trust-driven data experience.

Data intelligence includes:

• Metadata management.
• Business glossary and definitions.
• Data cataloging and discovery.
• Data products and contracts
• Lineage and impact analysis.
• Data quality and drift monitoring.
• Governance enforcement and workflows.
• Trust indicators for analytics and AI.
• MCP servers.

Data intelligence operationalizes governance and makes it usable at scale.

Actian Data Intelligence Platform provides a solution that enables your data teams to find, understand, and create a common understanding around data through:

Automated Intelligence

• Knowledge graph architecture that automatically understands data relationships.
• Smart classification recommendations for policy enforcement.
• Continuous scanning and metadata synchronization across the entire data ecosystem.

User-Centric Design

• Dual applications: Studio for data stewards, Explorer for business users.
• Marketplace-style discovery experiences that encourage data adoption.
• Context-rich documentation that eliminates governance friction.

Integration-First Architecture

• Seamless integration with existing data quality and infrastructure tools.
• API-first approach that supports custom workflows and automation.
• Synchronization with your current technology stack.

Learn more about the Actian Data Intelligence Platform.