As of February 2026, we've rolled out support for exploring relationships and hierarchies in MDM SaaS as part of the November 2025 release.
You can now explore relationship and hierarchy metadata in MDM SaaS. Additionally, you can also explore relationship and hierarchy data in Customer 360 SaaS, Product 360 SaaS, and Supplier 360 SaaS. You can view only direct relationships.
You can use the following sample prompts to explore relationship and hierarchy data:
•Show relationship attributes defined for Person.
•Is <name of the person> married?
•Show relationship between <name of the person> and <name of the person> from the Person business entity.
•Does <name of the person> work with <company name>?
This release includes the following enhancements to MDM SaaS:
•You can now explore field groups and nested field groups with multiple entries. CLAIRE GPT displays the entry that matches the prompt and a link to view additional field group entries. When you click the link, the additional entries appear in a table. For example, when you ask CLAIRE GPT to display the billing address of a person, it displays the billing address of the person along with additional addresses.
•You can now filter data from Customer 360 SaaS, Product 360 SaaS, and Supplier 360 SaaS based on dates or date ranges. For example, you can find records created between October 2025 and November 2025.
You can enter the following sample prompts to drill down data based on a date or date range:
- How long has Sara Jones been associated with Informatica?
- Show customers born in 2020.
•You can start a conversation in CLAIRE GPT, seamlessly switch to an MDM SaaS business application, and continue it in CLAIRE Copilot. In CLAIRE Copilot, you can use the same MDM SaaS prompts that you use in CLAIRE GPT.
This release includes the following enhancements to data quality agent:
•If you upload a CSV or Microsoft Excel file to specify a data set from the catalog, the data quality agent uses the data quality cleansing skill to suggest cleansing rules for your data. It provides a preview of your data, displays profiling insights, and presents a detailed plan to cleanse your data. Once you approve the plan, the agent generates cleansed data, and creates mapplets based on the generated cleansed data. Mapplets enable a streamlined and repeatable data cleansing process for similar data sets in the future.
•If you upload a CSV or Microsoft Excel file containing data quality rule descriptions in natural language, the data quality agent uses the data quality rule generation skill to automatically extract business rules and convert them into data quality rule specifications in Data Quality. The agent can also extract rules from the rule descriptions you provide in your prompt. Once you allow the agent to publish the rules, the rule specifications are generated and you can see them in Data Quality. You can preview the rules that the agent could and couldn't generate in the agent log.