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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteSalesforce completed its acquisition of Informatica on November 18, 2025. The strategic role Salesforce describes for Informatica is to give its Agentforce 360 data foundation a broader view of enterprise data: what assets exist across systems, what they mean, and how their values were produced. Metadata and lineage can give AI agents more context for using business data, but Salesforce’s product descriptions do not establish that every system is connected or that the combination has independently proven accuracy gains.
What Informatica adds to Salesforce’s AI data strategy
Salesforce says Informatica brings enterprise data-management capabilities including a data catalog, metadata management, lineage, governance, data quality, privacy, integration, and master data management (MDM). These capabilities are intended to extend Salesforce’s view beyond its own objects and relationships to data spread across enterprise systems.
The roles Salesforce assigns to its products are complementary rather than interchangeable. In its product framing, Data 360 provides a harmonized context layer, MuleSoft helps connect applications and operational signals, and Informatica contributes enterprise metadata intelligence. Agentforce is the agent platform intended to use that foundation; Tableau is also part of Salesforce’s broader data and analytics picture.
| Component | Role Salesforce describes |
|---|---|
| Data 360 | Harmonized context for data used across the platform. |
| MuleSoft | Connections to applications and operational signals. |
| Informatica | Enterprise metadata, catalog, lineage, and related data-management capabilities. |
| Agentforce | Salesforce’s agent platform, positioned to work with that data foundation. |
This is Salesforce’s architecture positioning, not evidence that every Informatica capability is already integrated with every customer’s Salesforce environment. Implementation and coverage depend on which systems and products are connected.
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Why metadata and lineage matter to an AI agent
Metadata supplies meaning and relationships
Metadata describes data assets and their context: what a field or object represents, how it relates to other assets, and where it fits in an organization’s data landscape. Without that context, an agent may encounter similar-looking values in different systems without knowing whether they mean the same thing.
Salesforce’s example of Informatica MDM extends beyond customer records to business objects such as products, suppliers, and assets. That broader scope matters when a task draws on several parts of an organization rather than a single customer profile.
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A catalog helps locate data across systems
A data catalog is an inventory for discovering and understanding data assets. Salesforce describes combining its metadata model with Informatica’s enterprise catalog to show assets across environments such as on-premises systems, data lakes, and legacy infrastructure. The goal is to make relevant data easier to find and interpret, rather than assume it all resides in Salesforce.
Lineage traces a value’s path
Lineage records where data originated and how it was transformed on its way to use. If an agent or employee encounters a value, lineage can help show the source and the steps that produced it. That trace supports investigation and governance; it does not, by itself, prove that the source is correct or that every transformation has been captured.
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Together, metadata and lineage can give an agent context about what data means and a trace of how it came to be. They are parts of a data foundation, not guarantees of trustworthy answers. Salesforce has not published an independently measured figure establishing that Informatica’s capabilities improve Agentforce accuracy, adoption, or business results by a particular amount.
What Salesforce and Informatica have announced, and when
Acquisition completed in November 2025
Salesforce announced that its Informatica acquisition was complete on November 18, 2025. In that announcement, Chair and CEO Marc Benioff summarized Salesforce’s rationale: “You have to get your data right to get your AI right.” This is the company’s explanation for the strategic fit, not an independently tested finding.
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Data 360 integration capabilities described in May 2026
In a May 29, 2026 release, Salesforce described a Data 360 Connector and Scanner for bidirectional data flow between enterprise systems and Data 360, with end-to-end lineage. The same release said Headless Data Management, Headless CLAIRE, and Data Quality Agent were generally available in Spring 2026. It listed Agentic Integration and Metadata Enrichment Agent for Q4 2026. Those are Salesforce’s dated availability statements; the announcement does not establish availability for every customer, configuration, or connected system.
AI-readiness assessment announced in September 2026
In a September 17, 2026 Dreamforce blog, Informatica introduced AI-Ready Data Intelligence. The company said it assesses metadata, data access, lineage, and related dimensions across seven factors: discoverability, quality, context, accessibility, governance, trust, and observability. Informatica said catalog customers could access the capability through Claude at the time of publication. That statement describes the vendor’s announcement as of that date, not a guarantee of its availability now or for all customers.
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Salesforce’s wider architecture positioning
Salesforce’s September 13, 2026 Enterprise AI Harness article placed Informatica alongside Data 360, MuleSoft, Agent Fabric, Tableau, Agentforce, Salesforce Guardian, and Salesforce Platform in a composable architecture. Salesforce framed governance as spanning data, metadata, policies, and processes, with lineage, quality, guardrails, and controls. Separately, Salesforce’s fiscal 2026 annual report said the acquisition closed in the fourth quarter of fiscal 2026 and described expanded connectivity to distributed systems with governance, lineage, and security controls across the data lifecycle.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to evaluate before relying on the data foundation
The product announcements describe intended capabilities and architecture. For a real deployment, the practical question is whether the relevant data is covered and governed well enough for the intended agent task. An evaluation can focus on:
- Coverage: Which enterprise systems and data assets are represented in the metadata catalog?
- Meaning: Are definitions and relationships clear enough to distinguish similar fields or objects?
- Lineage: Can users trace important values from their origins through transformations to consumption?
- Governance and quality: Which policies, privacy controls, and quality checks apply to the data an agent can use?
- Integration and availability: Are the needed connectors and announced capabilities available for the organization’s specific products and configuration?
- Evidence of outcomes: Are there measured results for the organization’s own tasks, rather than an assumption that added metadata automatically improves answers?
Salesforce EVP and GM Rahul Auradkar described the intended combination this way: “By combining Salesforce and Informatica’s enterprise metadata with Data 360’s harmonized sub-sec real-time context and MuleSoft’s integration strength, we replace guessing with reasoning.” That is Salesforce’s product vision. Whether it holds for a particular deployment depends on its actual data coverage, controls, and results.
Quick Recap
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