April 2026 - Precisely Data Integrity Suite

You can now apply relationship filters to business and technical assets to surface dependencies, mappings, and data flows in seconds. Filter by specific related assets, any relationship of a type, or exclude assets with certain connections to answer complex lineage questions without manual searching.

For more information, see Filter cataloged assets by relationships.

You can create custom tabs in Catalog to organize assets by environment, business function, priority, or any criteria your team defines. Tabs update in real time as asset statuses change, giving each role a personalized navigation bar that surfaces only the most relevant assets without manual refresh.

The Gio™ AI Assistant analyzes your datasets to automatically identify and flag sensitive fields as PII or Critical Data Elements, then validates classifications with AI to reduce manual tagging effort. Your team spends less time on metadata updates, governance gaps close faster, and compliance improves through consistent, AI-validated classifications across thousands of datasets.

You now have full control of your data classification with manual semantic type assignment. You can override automatic detection when needed, or assign types when detection hasn't run. Access this feature from the Details, Profile, or side panel in the Catalog. Choose from built-in semantic types, and track all changes in the Change History tab. An "Overridden" badge clearly indicates manually assigned types. Update or remove assignments anytime. Manually assigned types persist across cataloging operations and are used for rule detection and data quality analysis.

For more information, see Assign a semantic type to a field.

Custom semantic types

You can create and manage custom semantic types to gain complete control over how your data is classified and validated. Rather than relying solely on pre-defined semantic types, you can now define semantic types that align with your organization's specific requirements. Custom semantic types are automatically detected and applied during data profiling, cataloging, and inspection workflows.

For more information, see Semantic types and Semantic type fields.

Semantic type identification is a new feature designed to enhance data catalog systems by automatically classifying data fields according to their semantic type, such as identifying whether a string represents an email address or a security number. This feature helps users quickly grasp what kind of data they are dealing with, making it easier to filter and search semantic information. It helps users quickly understand and manage the nature of data within assets, supports efficient filtering and searching, and enables targeted updates to semantic type information thereby streamlining data profiling and improves overall asset management.

For more information see the following:

Datasource configuration workflow

For more information, see Datasource configuration workflow.

Support for Azure Data Factory (ADF)

Azure Data Factory connector now enables comprehensive metadata cataloging and lineage tracking across your data pipelines. This datasource brings in metadata from Azure Data Factory, including details about tenants, subscriptions, resource groups, data factories, and more allowing you to explore how data moves through your cloud-based ETL workflows.

For more information, see Azure Data Factory.

Support for Microsoft Azure Cosmos DB

Added support for Microsoft Azure Cosmos DB datasource, enabling seamless data cataloging and lineage. Also, enhanced security with key vault integration for credential management, along with advanced logging and debugging capabilities to support efficient troubleshooting and performance optimization.

For more information, see Microsoft Azure Cosmos DB .

You can now assign and manage domain ownership with clearer, more predictable behavior. Ownership flows from parent domains to child domains and directly related assets, stopping at the correct level to avoid over‑assignment. This helps you keep accountability accurate, reduce confusion about inherited ownership, and ensure the right people are responsible for the right data.

Domains are now available as a first-class governed asset type in the Data Governance service. Organize your data governance framework by grouping related business and technical assets into hierarchical domain structures. Manage domain hierarchies with parent-child relationships, and apply governance policies consistently across domains. Domains enable scalable, organized data governance across your enterprise.

For more information, see Domains.

Added support for Databricks Notebook datasource, enabling seamless data cataloging and lineage. Also, enhanced security with key vault integration for credential management.

The new EKS support allows you to deploy and run the DI Suite Agent directly in your own Amazon EKS cluster, enabling enterprise‑grade governance and seamless integration with existing Kubernetes tools. It improves scalability, simplifies operations, and replaces the earlier single VM K3s model.

For more information, see EKS cluster access setup.

You can now configure HTTP and HTTPS ingress for Agent services using simple CLI commands: preview, apply, and TLS apply. This makes it easy to enable secure, browser-friendly access across environments.

For more information, see Supporting HTTP or HTTPS ingress for services deployed to the Agent.

The Precisely Agentic AI framework consolidates AI capabilities across the Data Integrity Suite and provides a selection of AI models. The Gio™ AI Assistant serves as a conversational co-pilot within this framework, enabling you to perform cross-module operations through natural language queries while maintaining transparency in AI recommendations.

For more information, see Gio™ AI Assistant.

The Automated Replication Pipeline Designer provides step-by-step guidance for mapping data between source and target systems with automated field suggestions and real-time validation. The Gio™ AI Assistant streamlines configuration by reducing manual effort and validation errors, allowing you to deploy replication pipelines faster.

For more information, see Automated replication pipeline designer.

The Data Quality Normalization and Standardization Agent guides you through standardizing fields, normalizing values, and validating data against business rules. You can add recommended steps directly to your data quality pipelines, ensuring consistency and accuracy across datasets without manual rule configuration.

For more information, see Data quality normalization and standardization agent.

The Gio™ AI Assistant analyzes your datasets and fields to recommend up to three data quality rules per query, each with a name, description, confidence score, and target asset details. You can edit or reject suggestions before adding them to your centralized rule repository, accessible from the Datasets, Fields, Dataset Rules, Field Rules, and Data Quality Rules pages.

SQL based quality rules allow you to define pass conditions and row filters in quality rules directly using SQL queries. This approach provides greater flexibility and efficiency in creating rules by leveraging existing SQL logic and patterns. By writing custom SQL queries, you can set specific criteria to evaluate rules for selected datasets and fields. You can also use configurable row filters and pass conditions through relevant SQL queries to ensure thorough and controlled data quality assessments.

For more information, see SQL