July Product Updates: Enterprise Data Governance, Sovereignty, and Scale

Enterprise data teams are stuck balancing three demands: 

1. Governance that earns trust.
2. Sovereignty over infrastructure and compliance.
3. Speed without manual work.

Over the past few months, we shipped updates across databases, data management, and business intelligence to close those gaps. Here’s what we rolled out. 

Data Management: Trust

Data Contract Builder gives data stewards and owners a shared workspace to define, version, and govern data contracts using ODCS v3.1.0. Business users and technical stakeholders collaborate without writing YAML. Contracts are machine-readable, version-controlled, and portable across any ODCS-compatible pipeline.

Impact: Data reaches downstream systems clean. Quality issues surface early. Rules deploy in days, not weeks. Downstream teams trust the data because the contract is auditable and agreed-upon upfront.

Data Product Builder wraps existing datasets into governed data products without waiting on data engineering. Data Product Owners define output ports directly in the Data Intelligence Platform. The platform automatically generates an ODCS-compliant data contract as a concrete starting point for governance, with no YAML required. That contract plugs directly into Data Contract Builder for collaborative governance, or into CI/CD when data engineering is ready to enforce it.

Impact: Teams blocked by engineering backlogs move forward on governance without dependencies. Start data-first. Evolve to contract-first as your maturity increases, using the same generated contract as your foundation.

Data Steward Agent automates the routine work of keeping your catalog current. Stewards copy plans and change lists directly from the Agent chat to share with their teams before applying large-scale updates. Complex queries run with full reasoning intact across metadata, ownership, and lineage, so stewards execute more ambitious governance tasks without degradation. Documentation and definitions stay consistent across workflows and external agents via MCP and A2A integrations. Built into the Data Intelligence Platform’s federated knowledge graph, the Agent acts with full catalog context, not generic AI output.

Impact: Your catalog becomes a trusted semantic layer stewards can actually manage at enterprise scale. Faster, more reliable stewardship means better metadata quality, more consistent definitions, and a catalog that data teams and AI systems can trust.

DataConnect 14.0 embeds enterprise-grade data quality into every integration pipeline by addressing three persistent bottlenecks: rule authoring complexity, blind rule configuration, and visibility gaps. Automate Design analyzes patterns and suggests rules in minutes. The AI Prompt workflow lets you describe a problem in plain English, generates the rule, and validates it against live data before deployment. Full-record browser and assessment reports show exactly what you’re fixing and where quality problems live. The platform processes at Spark scale (10-100x faster), detects 1,200+ semantic types, and integrates seamlessly with existing DataConnect jobs.

Impact: Data quality scales to lakehouse volumes without manual onboarding. Teams build trust in enterprise data used for analytics, AI, and operations.

AI Analyst now connects to Actian Ingres and HCL Informix* databases, unlocking governed agentic analytics on operational and historical data stored in legacy systems. Business analysts and data teams ask natural-language questions of transactional data without writing SQL. The semantic layer handles dialect translation automatically. It works alongside any other connected data source in AI Analyst, enabling cross-source analysis without building new pipelines.

Impact: Modernize access to trusted operational data without replacing core systems. Teams explore historical and transactional records through conversational analytics.

Databases: Sovereignty

Zen 17 AI brings native vector search to the embedded database layer. Developers run unified SQL queries that join relational data, metadata filters, and vector similarity search in a single lightweight engine, with no separate vector database required. MCP server support means AI agents connect natively to Zen. HNSW delivers enterprise-grade performance. It works where connectivity is inconsistent or nonexistent. Existing Zen code runs unchanged. Zero migration. Zero downtime.

Impact: AI-powered features land in embedded and edge environments faster. Architecture complexity drops. Data stays where it’s created.

Analytics Engine 8.0 brings sovereign, AI-ready analytics to on-prem and air-gapped environments. The patented vectorized engine offers a streamlined data lake experience, reading Iceberg, Delta Lake, Parquet, and other open formats directly, in place, with zero custom ETL. External table mechanism simplifies dataset registration at scale. A new set of in-database AI capabilities unlocks modern use cases for building Retrieval-Augmented Generation (RAG) applications, supports predefined embedding models, includes built-in model metrics, and allows direct access to LLMs natively via SQL statements.

Impact: Analytics teams run AI workloads without ripping out their existing databases or moving data to the cloud.

Business Intelligence: Efficiency

Jaspersoft 10.1.0 automates the routine work of report design while giving IT administrators and compliance teams new control. The Calculation Configuration Wizard removes manual expression scripting for totals and counts. Composite Elements simplify page layout. The Scheduler Dashboard gives administrators real-time visibility into job execution and enables targeted restarts. No need to re-run entire batches when a single job fails. Accessibility improvements handle Section 508, PDF/UA, and WCAG standards natively. Reports built years ago run faster on the modernized engine.

Impact: Designers ship faster. Administrators troubleshoot in minutes. Regulated industries meet compliance without compromise.

Take Action

Data Stewards & Governance Leads: See the Data Contract Builder, Data Product Builder, and the Data Steward Agent in action. [Schedule a demo]

Developers Building AI: See how the new Zen 17 AI runs RAG in the embedded layer. [Book a technical deep-dive]

Data Engineers & Architects: Experience data quality at Spark scale. [Take the product tour]

Analytics Teams: Explore [Analytics Engine 8.0] for sovereign, AI-ready analytics. See [Jaspersoft 10.1.0] ship reports without rewrites.

Ingres and HCL Informix Users: Ask natural-language questions of your data through AI Analyst. Keep an eye out for details from your account manager.

Informix® is a trademark of IBM Corporation in at least one jurisdiction and is used under license.

About Author

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.