The July 2025 release of Business 360 Console includes the following new features and enhancements.
Enrichment and validation
The data enrichment framework is now called Enrichment and Validation Orchestrator. You can use this framework to validate, transform, enrich, and cleanse data through rule associations.
You can add multiple rule associations to an objective and run them sequentially. You can configure when to trigger an objective and determine the types of records to which you want to apply the objective.
The following image shows an objective and its configuration:
You can also determine whether a rule association can use the output from another rule association as its input within an objective. Optionally, you can now assign static values to input and output fields in rule associations.
When you configure validations, you can choose to reject records that fail validations. This setting overrides the error remediation property of business entities. When input fields fail validation, you can skip downgrading their trust scores. You can also perform cross-section validations. For example, if a record includes a supplier name, you can check whether the record also contains the supplier contact details.
When you create or update records by using REST APIs, you can now add or update dynamic field definitions and dynamic field values.
For more information about including dynamic field definitions and dynamic field values in a REST API request, see Create Master Record.
Limits on data access rules
To optimize performance, MDM SaaS now has a maximum limit of 50 record-level and 50 attribute-level data access rules for each business entity. The limit includes both active and draft data access rules. When the maximum limit exceeds for a business entity, you can't create new data access rules.
If a business entity already has more than 50 record-level and 50 attribute-level rules, you can no longer create data access rules for that business entity.
You can configure conditions for relationships, relationship attributes, and related business entity attributes in data access rules. These conditions verify whether a relationship exists for a record and then control access to specific values in the record.
For example, you can create a data access rule that grants access to product records only if the product has an active Product and Category relationship. Additionally, you can set another condition to allow records only when the type attribute of the related Category business entity is set to Electronics.
When you update or delete values of related business entity attributes that are part of a data access rule condition, or when users edit records in bulk, MDM SaaS runs the reapply data access rules job.
You can view the details of a reapply data access rules job on the My Jobs page.
You can now use the Manage Shared Objects privilege to determine whether a user role can update the sharing settings of shared objects, such as dashboards, saved searches, and search templates in business applications. This privilege also grants the ability to edit and delete shared objects, as well as add or remove a dashboard from everyone's Home page in business applications.
You can enable the Match Analysis and Explainability dashboard to explore the match results and gain insights into the match process. You can use the match results to fine-tune the match model and gain trust in the quality of the match results. After you enable the predefined dashboard, users can view the dashboard in their business applications.
When you configure a business application, you can specify a URL field to display preview images for records. The business application displays these images on the related records and search results pages.
You can now configure a match model to use geographic coordinates, such as latitude, longitude, and elevation, and match source records based on their proximity. For example, you can match source records with geographic coordinates that fall within a radius of 1000 meters.
For more information about using geographic coordinates in a match model, see Geocode field type.
Validation errors
You can now enable an existing business entity to save records that contain validation errors without purging data.
In Operational Insights, you can now view the total number of code values in an organization and the number of code values in each code list. You can also download the metrics as a CSV file.
The Overview page displays the usage statistics of business applications and Reference 360 separately.
The following image shows the usage statistics of Reference 360:
REST APIs
The responses of Read Master Record by Business ID and Read Master Record by SourcePKey REST APIs now include the ruleAssociation array. The ruleAssociation array includes data enrichment and validation details, such as rule IDs, trust score downgrades, and failed data enhancement rules.
For more information about the Read Master Record by Business ID and Read Master Record by SourcePKey APIs, see Business entity record APIs.
Retention period for historical data
You can now configure the retention period for the history of master data to comply with data retention policies. The retention period can be up to seven years. Historical data that's older than the retention period is permanently deleted and can't be restored.
You can now purge deleted source records of a specific business entity to permanently remove incorrectly loaded source records, comply with privacy regulations, and retire source systems. Purge deleted source records along with their dependencies, such as relationships, inactive master records, and history.
For more information about purging deleted source records, see Purging data.
Customer 360 Extension for Salesforce
You can now enhance the data onboarding experience in Salesforce by using Informatica Customer 360 Extension for Salesforce, a customizable Salesforce package. The Salesforce package includes a configurable Lightning component that seamlessly integrates with Customer 360 SaaS to detect and import similar records from Customer 360 SaaS.
After you complete training record pairs in a batch for an adaptive AI model, you can now view a trend graph of match training metrics, such as precision, recall, and accuracy. Use the graph to observe how the model adapts as you label record pairs across batches.
The following image shows the trend graph of the match training metrics of an adaptive AI match model:
For more information about match training metrics, see Training metrics.
CLAIRE Matching
To better reflect the capabilities of CLAIRE and advancements in MDM SaaS, the matching functionality in MDM SaaS is now renamed to CLAIRE Matching.
CLAIRE Matching uses the following AI-driven approaches:
•Directed AI Matching. Uses pretrained and rule-based methods to enhance match accuracy.
•Adaptive AI Matching. Uses a supervised learning model that continuously enhances its matching precision by learning from user actions and data profiles of your domain.
To reflect the name changes, the following match model objects have been renamed in the user interface:
Current Name
New Names
Declarative Rules
Directed AI Match Rules
Machine Learning (ML) Model
Adaptive AI Match Model
The following image shows the renamed objects in the user interface:
Note: The name change doesn’t affect the functionality of your existing match models or match jobs.
