Informatica enhances IDMC with AI-powered MDM, governance, and compliance tools

Informatica is adding new AI-based capabilities to its Intelligent Data Management Cloud (IDMC) platform to help enterprises manage their data more efficiently for generative AI and agentic applications and use cases.

IDMC sits on top of enterprise databases, ingesting and cataloguing from various sources and applying data governance rules as it goes. It includes tools for extract, transform, load (ETL), data observability for data quality, and master data management.

The latest updates, which include new capabilities for master data management, governance, and data integration, will help enterprises maintain data of high quality and understand its lineage, Krish Vitaldevara, chief product officer at Informatica, told InfoWorld.

The ability to clean, trace, and unify data across silos has become a critical necessity for enterprises looking to operationalize generative AI and agentic applications, as the accuracy of their responses is dependent on the quality of underlying data, Vitaldevara said.

These new features, which have been made generally available, are designed to automate much of the groundwork required to prepare data for AI workloads, expanding upon the company’s broader strategy to position itself as a foundational layer for AI readiness, Vitaldevara said.

Automating records matching for data audit, compliance

To improve master data management, Informatica has introduced Claire Match Analysis and Explainability, an Enrichment and Validation Orchestrator, and Data Catalog Scanner for MDM.

Claire Match Analysis and Explainability provides field-level contribution scores showing exactly why master data records were matched or kept separate, logging evidence for audit trails, said Devin Pratt, research director at IDC.

“For generative AI applications, this transparency allows teams to verify that LLMs reference legitimately merged ‘golden’ records rather than duplicates, reducing hallucination risks and compliance issues,” Pratt added.

The feature also comes with self-service tuning capabilities that will allow business users to adjust match thresholds and retrain supervised models without IT involvement, Informatica said in a statement.

The self-tuning capabilities will accelerate feedback loops for AI-driven applications, Pratt said.

The analyst also pointed out that though rival offerings, such as  SAP MDG and Stibo STEP, provide ML-assisted matching with scorecards, they lack Claire’s granular field-level explainability and self-service tuning capabilities.

“Talend and IBM InfoSphere offer fuzzy-match engines but rely more on technical stewardship with limited UI-based explanations, positioning Informatica ahead in AI-driven transparency,” Pratt said.

Informatica has added another feature — an Enrichment and Validation Orchestrator — to automate validation and enrichment of records to improve data management.

Constellation Research principal analyst Michael Ni feels that this feature stands out from similar offerings as it can orchestrate how records are enriched and validated not only across Informatica services but also third-party data sources, including large language models in real time.

“Competitors like Stibo’s enrichment plugins or Ataccama’s rule sequencing require more scripting,” Pratt explained.

Additionally, the enterprise data management vendor has added a Data Catalog Scanner for MDM to automate data compliance tracking.

The Scanner automatically harvests MDM metadata, maps record-level lineage, and integrates with Informatica’s Cloud Data Governance and Catalog (CDGC) inside IDMC for compliance tracking, Pratt said.

Enterprises can use this feature for regulatory audits and AI feature engineering discovery, Pratt added.

New governance features to provide more visibility, control

In order to provide more visibility and control over data being used in AI workloads, Informatica has added a new AI governance catalog, a new API, and an AI-based data lineage discovery feature.

The AI governance catalog tracks proprietary Claire models, customer machine learning pipelines (TensorFlow, PyTorch, Spark ML), third-party LLMs, and fine-tuned derivatives in order to ensure responsible and compliant deployment across the enterprise with centralized visibility, Pratt said.

Other benefits include automated risk scoring against EU AI Act and NIST guidance, model-card generation, and policy-driven approval gates for lifecycle management, Pratt added.

The API, on the other hand, accelerates data cleaning as it combines IDMC’s real-time data quality checks that can be used at data entry points rather than post-storage cleaning, according to analysts.

|This approach ensures only compliant data is used in operations or AI systems, reducing downstream remediation and enhancing trust in analytics. The API makes these checks responsive and automated, ideal for modern cloud architectures,” said Robert Kramer, principal analyst at Moor Insights and Strategy.

MCP support for IDMC

Informatica is also adding Model Context Protocol (MCP) support to the IDMC platform.

“Customers can now build and manage their MCP servers to connect to any asset on Informatica’s cloud platform (such as MDM) managed on Informatica’s Cloud Application Integration,” Informatica said in a statement.

Support for MCP, according to Constellation Research’s Ni, is becoming a table-stakes requirement for data management vendors, as enterprises need a reliable way to expose trusted data assets to AI agents.

“Wrapping governed data, especially master data, in MCP enables AI systems to discover and interact with authoritative business context in real-time, improving their accuracy and reliability,” Ni said.

Traditional MDM vendors, such as Stibo and Talend, haven’t yet publicly committed to providing MCP support. Other updates to the IDMC platform include support for new generative AI connectors for application integration and the general availability of Claire Copilot for data integration.