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A consultant is setting up Data Cloud for a multi-brand organization and is using data spaces to segregate its data for various brands.
While starting the mapping of a data stream, the consultant notices that they cannot map the object for one of the brands.
What should the consultant do to make the object available for a new data space?
When setting up Data Cloud for a multi-brand organization, if a consultant cannot map an object for one of the brands during data stream setup, they should navigate to the Data Space tab and select the object to include it in the new data space. Here's why:
Understanding the Issue
The consultant is using data spaces to segregate data for different brands.
While mapping a data stream, they notice that an object is unavailable for one of the brands.
This indicates that the object has not been associated with the new data space.
Why Navigate to the Data Space Tab?
Data Spaces and Object Availability :
Objects must be explicitly added to a data space before they can be used in mappings or transformations within that space.
If an object is missing, it means it has not been included in the data space configuration.
Solution Approach :
By navigating to the Data Space tab , the consultant can add the required object to the new data space.
This ensures the object becomes available for mapping and use in the data stream.
Steps to Resolve the Issue
Step 1: Navigate to the Data Space Tab
Go to Data Cloud > Data Spaces and locate the new data space for the brand.
Step 2: Add the Missing Object
Select the data space and click on Edit .
Add the required object (e.g., a Data Model Object or Data Lake Object) to the data space.
Step 3: Save and Verify
Save the changes and return to the data stream setup.
Verify that the object is now available for mapping.
Step 4: Complete the Mapping
Proceed with mapping the object in the data stream.
Why Not Other Options?
A . Create a new data stream and map the second data stream to the data space : Creating a new data stream is unnecessary if the issue is simply object availability in the data space.
B . Copy data from the default data space to a new DMO using the Data Copy feature and link this DMO to the new data space : This is overly complex and not required if the object can simply be added to the data space.
C . Create a batch transform to split data between different data spaces : Batch transforms are used for data processing, not for resolving object availability issues.
Conclusion
The correct solution is to navigate to the Data Space tab and select the object to include it in the new data space . This ensures the object is available for mapping and resolves the issue efficiently.
What are the two minimum requirements needed when using the Visual Insights Builder to create a calculated insight?
Choose 2 answers
Introduction to Visual Insights Builder:
The Visual Insights Builder in Salesforce Data Cloud is a tool used to create calculated insights, which are custom metrics derived from the existing data.
Requirements for Creating Calculated Insights:
Measure: A measure is a quantitative value that you want to analyze, such as revenue, number of purchases, or total time spent on a platform.
Dimension: A dimension is a qualitative attribute that you use to categorize or filter the measures, such as date, region, or customer segment.
Steps to Create a Calculated Insight:
Navigate to the Visual Insights Builder within Salesforce Data Cloud.
Select 'Create New Insight' and choose the dataset.
Add at least one measure: This could be any metric you want to analyze, such as 'Total Sales.'
Add at least one dimension: This helps to break down the measure, such as 'Sales by Region.'
Practical Application:
Example: To create an insight on 'Average Purchase Value by Region,' you would need:
A measure: Total Purchase Value.
A dimension: Customer Region.
This allows for actionable insights, such as identifying high-performing regions.
Which data model subject area should be used for any Organization, Individual, or Member in the Customer 360 data model?
: The data model subject area that should be used for any Organization, Individual, or Member in the Customer 360 data model is the Party subject area. The Party subject area defines the entities that are involved in any business transaction or relationship, such as customers, prospects, partners, suppliers, etc. The Party subject area contains the following data model objects (DMOs):
Organization:A DMO that represents a legal entity or a business unit, such as a company, a department, a branch, etc.
Individual:A DMO that represents a person, such as a customer, a contact, a user, etc.
Member:A DMO that represents the relationship between an individual and an organization, such as an employee, a customer, a partner, etc.
The other options are not data model subject areas that should be used for any Organization, Individual, or Member in the Customer 360 data model. The Engagement subject area defines the actions that people take, such as clicks, views, purchases, etc. The Membership subject area defines the associations that people have with groups, such as loyalty programs, clubs, communities, etc. The Global Account subject area defines the hierarchical relationships between organizations, such as parent-child, subsidiary, etc.
A financial services firm specializing in wealth management contacts a Data Cloud consultant with an identity resolution request. The company wants to enhance its strategy to better manage individual client profiles within family portfolios.
Family members often share addresses and sometimes phone numbers but have distinct investment preferences and financial goals. The firm aims to avoid blending individual family profiles into a single entity to maintain personalized service and accurate financial advice.
Which identity resolution strategy should the consultant put in place?
To manage individual client profiles within family portfolios while avoiding blending profiles, the consultant should recommend a more restrictive design approach for identity resolution. Here's why:
Understanding the Requirement
The financial services firm wants to maintain distinct profiles for individual family members despite shared contact points (e.g., address, phone number).
The goal is to avoid blending profiles to ensure personalized service and accurate financial advice.
Why a Restrictive Design Approach?
Avoiding Over-Matching :
A restrictive design approach ensures that match rules are narrowly defined to prevent over-matching (e.g., merging profiles based solely on shared addresses or phone numbers).
This preserves the uniqueness of individual profiles while still allowing for some shared attributes.
Custom Match Rules :
The consultant can configure custom match rules that prioritize unique identifiers (e.g., email, social security number) over shared contact points.
This ensures that family members with shared addresses or phone numbers remain distinct.
Other Options Are Less Suitable :
A . Configure a single match rule with a single connected contact point based on address : This would likely result in over-matching and blending profiles, which is undesirable.
B . Use multiple contact points without individual attributes in the match rules : This approach lacks the precision needed to maintain distinct profiles.
D . Configure a single match rule based on a custom identifier : While custom identifiers are useful, relying on a single rule may not account for all scenarios and could lead to over-matching.
Steps to Implement the Solution
Step 1: Analyze Shared Attributes
Identify shared attributes (e.g., address, phone number) and unique attributes (e.g., email, social security number).
Step 2: Define Restrictive Match Rules
Configure match rules that prioritize unique attributes and minimize reliance on shared contact points.
Step 3: Test Identity Resolution
Test the match rules to ensure that individual profiles are preserved while still allowing for some shared attributes.
Step 4: Monitor and Refine
Continuously monitor the results and refine the match rules as needed to achieve the desired outcome.
Conclusion
A more restrictive design approach ensures that match rules perform as desired, preserving the uniqueness of individual profiles while accommodating shared attributes within family portfolios.
A retail customer wants to bring customer data from different sources
and wants to take advantage of identity resolution so that it can be
used in segmentation.
On which entity should this be segmented for activation membership?
