Understanding Survivorship in Informatica IDMC - Customer 360 SaaS

 In Informatica IDMC - Customer 360 SaaS, survivorship is a critical concept that determines which data from multiple sources should be retained when records are merged or updated. It's a set of rules and strategies designed to ensure data accuracy, consistency, and reliability.

   

Key Concepts

1. Source Ranking:

   • Assigning Trust: Each source system is assigned a rank based on its reliability and data quality.   
   • Prioritizing Data: Higher-ranked sources are considered more trustworthy and their data takes precedence.
   • Example: If you have two sources, "HR" and "Sales," with HR being more reliable, you might assign it a rank of 1 and Sales a rank of 2. When a conflict arises, data from HR would be prioritized.

2. Survivorship Rules:

   • Defining the Rules: These rules dictate how conflicts between field values from different sources are resolved.
   • Common Rule Types:
     • Maximum: Selects the maximum value.
     • Minimum: Selects the minimum value.
     • Decay: Considers the trust level and decay rate of a source over time.   
     • Custom: Allows for more complex rules based on specific business requirements.
   • Example: For a "Customer Address" field, a decay rule might be applied, giving more weight to recent updates from a trusted source.







3. Source Last Updated Date:

   • Resolving Ties: When multiple sources have the same trust level and ranking, the source with the most recent update is prioritized.
   • Example: If two sources, both ranked equally, provide different values for a "Phone Number" field, the value from the source with the latest update would be chosen.

4. Block Survivorship:

   • Grouping Fields: Allows you to treat a group of related fields as a single unit.
   • Preserving Consistency: When a block survives, all fields within the block are retained together.
   • Example: A "Customer Address" block might include "Street," "City," "State," and "ZIP Code." If the block survives from one source, all these fields are retained.

5. Deduplication Criteria:

   • Identifying Duplicates: Defines the conditions for identifying duplicate records.
   • Resolving Duplicates: Determines how to merge duplicate records, often based on survivorship rules.   
   • Example: You might deduplicate customers based on a combination of "First Name," "Last Name," and "Email Address."

Practical Example: Customer Data Merge

Imagine you have two source systems: "HR" and "Sales." Both systems have customer data, but there are inconsistencies and missing information.

1. Source Ranking: HR is ranked higher than Sales.
2. Survivorship Rules:
   • For "Name," the maximum value is chosen.
   • For "Address," the most recent update from the higher-ranked source is selected.
   • For "Phone Number," a decay rule is applied, giving more weight to recent updates.
3. Block Survivorship: The "Address" block is treated as a unit.

If a customer record exists in both systems with conflicting data, the merge process would:

• Prioritize the "Name" from HR if it's different.
• Use the most recent "Address" from HR.
• Select the "Phone Number" with the highest trust score, considering recency.

Effective Survivorship Configuration

• Clear Understanding of Data Sources: Assess the reliability and quality of each source.
• Prioritize Critical Fields: Focus on configuring survivorship rules for fields that are essential to business operations.
• Consider Data Quality and Consistency: Analyze data quality issues and inconsistencies to optimize survivorship rules.
• Regular Review and Refinement: Continuously monitor and adjust survivorship configurations as data sources and business requirements evolve.
• Test Thoroughly: Implement a robust testing strategy to validate survivorship behavior and identify potential issues.

By carefully configuring survivorship rules, you can ensure that your master data is accurate, consistent, and reliable, enabling better decision-making and improved business processes.

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