Modern organizations are challenged not just with managing large volumes of data, but with discovering, understanding, trusting, and activating that data for decision-making, use cases, and AI/ML initiatives. Actian Data Intelligence Platform is built to help enterprises meet those challenges. It brings together metadata, governance, data quality, semantic context, and self-service discovery in a unified environment.
For advanced users such as data engineers, data stewards, analytics architects, and ML-ops engineers, this guide provides a deep dive into how to leverage the platform effectively, including best practices, advanced features, and architecture considerations.
Why the Platform Matters for Advanced Users
As data ecosystems become increasingly complex, often spanning multi-cloud, hybrid, data lakes and warehouses, and streaming environments, traditional solutions such as data catalog only or data governance only fall short.
Actian Data Intelligence Platform solves this problem in a variety of ways:
- Offering a unified data catalog, Model Context Protocol (MCP) Server, data marketplace, governance, lineage tracking, and data observability in a single solution.
- Supporting a federated knowledge graph and identifying semantic relationships between data assets across domains.
- Being designed for hybrid/multi-cloud and on-premises deployments, acknowledging real enterprise environments.
- Meeting the needs of advanced users by offering APIs, scanning and connectors, and automation of metadata and governance workflows.
What Advanced Users Should Care About
To meet the needs of advanced users, key capabilities include:
- Ensuring data lineage, discoverability, and governance across datasets.
- Embedding data quality and observability to understand whether data is trustworthy before it’s used for analytics, AI, ML, or other use cases.
- Enabling self-service while maintaining governance and access controls, allowing non-technical users to access and consume data without violating policies.
- Supporting AI readiness by ensuring data is annotated, catalogued, contextually understood, and accessible for AI/ML workflows.
- Operating in a distributed environment, where domains may own their own data but the enterprise still requires oversight, coherence, and a semantic “mesh.”
The platform offers all of these capabilities, making it a powerful solution for advanced users to build and operate serious enterprise-scale data intelligence.
Key Platform Differentiators
Advanced and differentiating features of the Actian platform include:
- Contract-first data products. Embedding policies and data contracts into published data products, ensuring governance is baked in.
- Real-time data quality and observability integration. Enabling data monitoring from ingestion through consumption.
- Support for AI assistants and agents. Providing support via trusted, AI-ready data and an MCP Server.
For advanced users, exploiting these features is what turns a traditional data catalog into a true enterprise data intelligence engine.
Architecture Overview and Deployment Considerations
Before diving into ways to use the platform, it’s essential to understand how it fits into the organization’s broader architecture and how to plan deployment and onboarding for advanced usage.
Architectural Layers
At a high level, the Actian Data Intelligence Platform can be viewed as having the following layers:
- Metadata ingestion and scanning. Connectors, scanners, and APIs pull metadata from data sources such as databases, data lakes, BI tools, and apps. The platform supports over 100 connectors.
- Knowledge graph / semantic layer. Metadata is ingested, and relationships are captured in a graph model. This enables rich semantics and search capabilities.
- Data catalog/data marketplace. This user-facing layer is where data assets are discovered, data products are published, access requests are processed, and governance rules are applied.
- Governance, contracts, quality, and observability. The underlying framework ensures data is properly governed, quality is monitored, lineage is tracked, and policies are enforced.
- Consumption and activation layer. This is where business users, analysts, ML workflows, and AI agents access the curated data products.
Model Deployment and Planning Considerations
Advanced users need to consider deployment logistics:
- On-premises vs. cloud vs. hybrid. Actian Data Intelligence Platform supports hybrid environments, which is important when data sources span both on-premises and cloud-based environments.
- SaaS vs. self-hosted. Many organizations opt for the SaaS offering. However, for stringent compliance or highly custom metadata workflows, organizations must understand whether self-hosted or managed hosting is required.
- Connector strategy. Map out all data sources, BI tools, and applications, and ensure connectors and scanners are available, or custom connectors are built to support deployment.
- Domain organization (data mesh style). Because the platform supports a federated knowledge graph and domain/sub-catalogs, plan how the organization’s domains will align. Determine who owns what responsibilities and how cross-domain sharing occurs.
- Metadata governance processes. Advanced users should set up metadata ingestion schedules, data change procedures, lineage refresh strategies, and naming/ontology standards.
- Performance and scalability. If organizations have massive data assets, they will want to monitor metadata ingestion volumes, graph complexity, search performance, and API throughput.
- Security and compliance. Because governance is built in, users must configure role-based access, field-level lineage, PII classification, and audit trails. Actian Data Intelligence Platform has built-in compliance for GDPR/CCPA and other regulations.
Implementation Roadmap for Advanced Users
Here’s a suggested 10-step roadmap tailored for advanced users:
- Discovery and inventory. Catalog the data estate. List sources, owners, current metadata gaps, and other relevant information.
- Design metadata model and ontology. Define the business glossary, ontology including terms and domains, and knowledge graph relationships.
- Install connectors and scanners. Connect key data sources, such as databases, data warehouses, lakehouses, and BI tools.
- Initial ingestion and graph build. Perform initial data ingestion, then create a canonical view of metadata, relationships, and lineage.
- Publish initial data products. Define data products, and embed governance contracts and policy metadata.
- Enable discovery and self-service. Configure the data marketplace, search user interface, and user roles for business and technical users.
- Embed observability. Connect data quality tools and monitoring dashboards, and set data quality alerts.
- Roll-out to domains. Because advanced usage often involves scale, bring in the domain team and federated data catalog design liaisons.
- Integrate with ML/AI workflows. Connect the data catalog to ML pipelines and AI assistants. Build automated agents that leverage metadata.
- Continuous maintenance and improvement. Monitor usage, search analytics, update ontology, and refine contracts. Retire data products when they’re no longer needed.
Best Practices for Advanced Users
To get the most benefit from the Actian Data Intelligence Platform, advanced users should follow these five best practices:
1. Governance by Design
Embed governance and policy at the time of dataset creation or data product publication, not when problems arise. Use data contracts and SLAs to set expectations and automate enforcement. Leverage field-level lineage, automated tagging such as PII classification, and audit trails to support compliance.
2. Metadata and Domain Ownership
Enforce discipline around metadata, including standardized naming conventions, glossary definitions, and versioning. Allow domain teams to own their sub-catalogs, while centralizing governance retains oversight. The Actian Data Intelligence Platform supports peer-to-peer data sharing between domains.
Avoid outdated or stale metadata by scheduling refreshes, advertising metadata updates, and retiring dormant assets.
3. Enable Self‐Service but Safeguard Access
Expose the data marketplace to business users, but implement access request workflows, role-based access, and security policies behind the scenes. Educate users on how to evaluate data quality, lineage, and metadata before using a data asset. Provide smart recommendations or default quality thresholds.
Monitor usage, flag seldom-used assets, and improve or retire them to avoid clutter.
4. Monitor Data Product Lifecycle and Quality
Define metrics for each data product, such as usage, freshness, completeness, number of accesses, and number of downstream dependencies.
Employ dashboards that track the health of data products and the data catalog itself. Use alerts for metadata growth, asset churn, and high lineage change frequency, which may indicate risk. Conduct periodic reviews of key data products to see if they are still relevant, the owners are listed correctly, and governance policies are followed appropriately.
5. Integrate With ML/AI and DevOps
In the ML/AI pipeline, treat data products and catalog metadata as first-class citizens. Use the MCP Server to connect AI agents with contextual data. This helps avoid agent “hallucinations” or incorrect data usage.
Automate metadata ingestion and catalog refreshes as part of CI/CD pipelines. For advanced users building ML workflows, link models back to data products in the data catalog so that when data changes, users can proactively understand the model impact.
Measuring Success and ROI
For advanced users, it’s important to quantify the impact of the platform. Here are ways to measure the platform’s success within the organization:
Key Metrics to Track
- Reduction in time to find relevant datasets.
- Increase in data product reuse or the number of business users accessing data products.
- Increase in the number of data products published and maintained with governance.
- Increase in the number of quality incidents caught pre-consumption versus reported downstream.
- Increase in the number of ML models deployed with data-product lineage and reduction in model failure rate.
- Compliance and audit readiness, resulting in fewer audit findings and faster reporting time.
- Business value such as cost savings, faster time-to-insight, and revenue uplift from AI initiatives.
Continuous Improvement
- Use data catalog analytics to monitor usage trends like which data products are underused, which domains are most active, and where bottlenecks occur.
- Solicit feedback from business users about discoverability, usability, and metadata richness.
- Revisit governance policies annually or semi-annually to ensure they remain relevant.
Get a Personalized Demo of the Actian Data Intelligence Platform
For advanced users, the value of the Actian Data Intelligence Platform lies not just in the feature list but also in how it’s integrated into the organization’s architecture and processes. It helps with complex tasks such as capturing metadata, enforcing contracts, linking business context to data assets, supporting AI readiness, and enabling self‐service.
The platform is powerful. The key to its success lies in thoughtful design, disciplined metadata governance, and the ability to enable users to discover, trust, and activate data at scale. Teams spend less time searching for data and more time extracting value from it.
Schedule a personalized demo of the platform today to see how it can simplify and accelerate an organization’s data and AI goals.
FAQ
An enterprise data marketplace is a governed platform that connects data producers and consumers, enabling publication, discovery, licensing, and secure delivery of curated data products, with APIs and connectors to integrate data into analytics and AI workflows.
Review the provider’s quality score, request sample datasets, and verify schema documentation. Many platforms offer trials to assess quality.
Prioritize governance and automated policy enforcement, data quality and lineage verification, intuitive search, flexible licensing and billing, and seamless analytics/AI integrations, plus cloud-native scalability and regulatory compliance automation.
Common challenges include balancing privacy and access, integrating legacy systems, scaling infrastructure, achieving adoption, and managing licensing complexity; these are addressed via change management, training, and phased pilots.
Public marketplaces have open listings for any qualified buyer, while private exchanges restrict access to invited participants and often offer custom agreements.
Data contracts embed schema definitions and quality thresholds that CI/CD pipelines validate on every release, ensuring compliance.
Connect the marketplace’s API to your pipeline, pull schema definitions into version control, and configure automated tests for data quality.
Verify the provider’s data contract for consent documentation and audit logging. Review compliance certifications and data processing agreements.
They combine provider vetting, automated profiling and anomaly detection, lineage tracking, SLAs, and continuous compliance monitoring with detailed audit trails and integration to security and governance systems.
