Podcast
Questions and Answers
Which of the following accurately describes one of the consolidation states?
Which of the following accurately describes one of the consolidation states?
- MATCH_INDEXED (correct)
- MATCH_READY_FOR_VALIDATION
- MATCH_PENDING
- MATCH_IRRELEVANT
What does the base state NOT include?
What does the base state NOT include?
- Deleted
- Active
- Archived (correct)
- Pending
Which of the following is a possible state in SearchIndex?
Which of the following is a possible state in SearchIndex?
- Search_cached
- Search_dirty (correct)
- Search_ready
- Search_previous
Which consolidation state signifies that data has been successfully matched?
Which consolidation state signifies that data has been successfully matched?
What is NOT a type of record state mentioned?
What is NOT a type of record state mentioned?
What is a significant advantage of using Informatica MDM in a SaaS model?
What is a significant advantage of using Informatica MDM in a SaaS model?
Which feature of Informatica MDM allows for a comprehensive view of key entities?
Which feature of Informatica MDM allows for a comprehensive view of key entities?
What is a key implementation consideration when utilizing Informatica MDM?
What is a key implementation consideration when utilizing Informatica MDM?
Which of the following best describes a benefit of using Informatica MDM's cloud-native design?
Which of the following best describes a benefit of using Informatica MDM's cloud-native design?
What challenge does Informatica MDM face regarding data management?
What challenge does Informatica MDM face regarding data management?
In which area can Informatica MDM significantly enhance business operations?
In which area can Informatica MDM significantly enhance business operations?
What aspect of Informatica MDM architecture enables ease of updates and modular functionality?
What aspect of Informatica MDM architecture enables ease of updates and modular functionality?
What does high-quality data management in Informatica MDM assure?
What does high-quality data management in Informatica MDM assure?
What is the primary purpose of the match and merge processes in Master Data Management?
What is the primary purpose of the match and merge processes in Master Data Management?
Which method focuses on finding records that are identical in Master Data Management?
Which method focuses on finding records that are identical in Master Data Management?
What strategy retains the most recently updated record during the merging process?
What strategy retains the most recently updated record during the merging process?
Which of the following challenges is associated with the matching process in MDM?
Which of the following challenges is associated with the matching process in MDM?
What is a benefit of implementing effective match and merge processes?
What is a benefit of implementing effective match and merge processes?
Which of the following best describes a recommended best practice in match and merge?
Which of the following best describes a recommended best practice in match and merge?
What technological tool is used to analyze data quality before matching in MDM?
What technological tool is used to analyze data quality before matching in MDM?
What is one of the major complexities faced in the matching process?
What is one of the major complexities faced in the matching process?
Which of the following is NOT a business entity that a car manufacturing company might define?
Which of the following is NOT a business entity that a car manufacturing company might define?
A business entity consists solely of a single type of field and cannot have multiple fields.
A business entity consists solely of a single type of field and cannot have multiple fields.
What is the significance of defining fields in a business entity?
What is the significance of defining fields in a business entity?
A company that manufactures cars might define business entities such as customers, employees, suppliers, factories, and __________.
A company that manufactures cars might define business entities such as customers, employees, suppliers, factories, and __________.
Match the following terms with their definitions:
Match the following terms with their definitions:
A crosswalk represents a two-way relationship between code values in a pair of code lists.
A crosswalk represents a two-way relationship between code values in a pair of code lists.
A reference data set can contain multiple code lists with various code values.
A reference data set can contain multiple code lists with various code values.
Crosswalks provide a way to translate between identical code lists only.
Crosswalks provide a way to translate between identical code lists only.
Each code list in a reference data set can contain different types of code values.
Each code list in a reference data set can contain different types of code values.
Crosswalks are essential for effectively managing relationships between varied code values in reference data.
Crosswalks are essential for effectively managing relationships between varied code values in reference data.
What is the primary function of a crosswalk in code lists?
What is the primary function of a crosswalk in code lists?
How does a reference data set relate to code lists?
How does a reference data set relate to code lists?
What is a major benefit of using crosswalks in data management?
What is a major benefit of using crosswalks in data management?
Which statement about code lists in a reference data set is accurate?
Which statement about code lists in a reference data set is accurate?
What type of relationship does a crosswalk illustrate?
What type of relationship does a crosswalk illustrate?
What is required to use the DaaS services effectively?
What is required to use the DaaS services effectively?
Which DaaS rule association is used for real-time phone number verification?
Which DaaS rule association is used for real-time phone number verification?
How does the predefined address verifier asset contribute to batch processing?
How does the predefined address verifier asset contribute to batch processing?
What can DaaS rule associations be used for in terms of email addresses?
What can DaaS rule associations be used for in terms of email addresses?
What distinguishes the DaaS rule association for real-time address validation from the batch processing method?
What distinguishes the DaaS rule association for real-time address validation from the batch processing method?
What defines a basic rule association?
What defines a basic rule association?
Which of the following functions can you configure for basic rule associations?
Which of the following functions can you configure for basic rule associations?
What is a defining characteristic of advanced rule associations?
What is a defining characteristic of advanced rule associations?
Which rule specification is supported for advanced rule associations?
Which rule specification is supported for advanced rule associations?
What can DaaS rule associations be applied to?
What can DaaS rule associations be applied to?
Which condition can basic rule associations NOT validate?
Which condition can basic rule associations NOT validate?
Which function would NOT be typically performed by an advanced rule association?
Which function would NOT be typically performed by an advanced rule association?
What is one primary use of basic rule associations?
What is one primary use of basic rule associations?
A DaaS rule association can be used to validate email addresses in batches.
A DaaS rule association can be used to validate email addresses in batches.
To validate addresses in bulk, a predefined address verifier asset is utilized from Cloud Data Quality.
To validate addresses in bulk, a predefined address verifier asset is utilized from Cloud Data Quality.
Informatica Global Phone Validation is used to validate email addresses in real time.
Informatica Global Phone Validation is used to validate email addresses in real time.
Business 360 Console defaults to Informatica Email Verification for real-time email validation.
Business 360 Console defaults to Informatica Email Verification for real-time email validation.
Custom field groups can be validated and enriched in batches using a DaaS rule association.
Custom field groups can be validated and enriched in batches using a DaaS rule association.
A basic rule association can validate input fields that are required.
A basic rule association can validate input fields that are required.
Advanced rule associations require the mapping of both input and output fields to the business entity fields.
Advanced rule associations require the mapping of both input and output fields to the business entity fields.
DaaS rule associations can only be applied to fields that contain names and addresses.
DaaS rule associations can only be applied to fields that contain names and addresses.
A transformation function in a basic rule association alters the source data.
A transformation function in a basic rule association alters the source data.
The DUNS SSN Validation rule is an example of a predefined rule specification for advanced rule associations.
The DUNS SSN Validation rule is an example of a predefined rule specification for advanced rule associations.
Basic rule associations can be used for high concurrent cleansing transactions.
Basic rule associations can be used for high concurrent cleansing transactions.
Integrity checks are considered a type of rule association mentioned in the content.
Integrity checks are considered a type of rule association mentioned in the content.
Input fields that are empty are always validated by advanced rule associations.
Input fields that are empty are always validated by advanced rule associations.
What is the primary purpose of the survivorship process in data management?
What is the primary purpose of the survivorship process in data management?
How are source systems ranked during the survivorship configuration?
How are source systems ranked during the survivorship configuration?
What happens to the ranking label when modifications are made to source system rankings?
What happens to the ranking label when modifications are made to source system rankings?
Which type of survivorship rule identifies trusted values based on decay rates and trust levels?
Which type of survivorship rule identifies trusted values based on decay rates and trust levels?
What does a higher rank indicate in source system reliability?
What does a higher rank indicate in source system reliability?
In survivorship configuration, what defines the conditions for fields and field groups to survive?
In survivorship configuration, what defines the conditions for fields and field groups to survive?
Which of the following best describes a modification in survivorship rules?
Which of the following best describes a modification in survivorship rules?
What does the term 'decay rate' in survivorship rules refer to?
What does the term 'decay rate' in survivorship rules refer to?
What purpose does configuring a decay rule for a field serve?
What purpose does configuring a decay rule for a field serve?
What is the function of the Source Last Updated Date field in the survivorship process?
What is the function of the Source Last Updated Date field in the survivorship process?
What happens if block survivorship is enabled for a field group?
What happens if block survivorship is enabled for a field group?
How does deduplication criteria function within a field group?
How does deduplication criteria function within a field group?
When should a minimum rule be applied to a field value during survivorship?
When should a minimum rule be applied to a field value during survivorship?
What configuration must be set to enable fields and field groups to survive as a single unit?
What configuration must be set to enable fields and field groups to survive as a single unit?
What is the outcome if multiple fields have identical field values during survivorship evaluation?
What is the outcome if multiple fields have identical field values during survivorship evaluation?
What does configuring a maximum rule for a field help achieve?
What does configuring a maximum rule for a field help achieve?
In the case where source systems have equal rankings, what decides which field value survives?
In the case where source systems have equal rankings, what decides which field value survives?
What would happen if the Source Last Updated Date field is disabled?
What would happen if the Source Last Updated Date field is disabled?
What defines the winner in a survivorship configuration when two records have identical availability dates?
What defines the winner in a survivorship configuration when two records have identical availability dates?
What happens if both records have the same last updated date during survivorship evaluation?
What happens if both records have the same last updated date during survivorship evaluation?
How can the trust score of source systems impact the data survivorship process?
How can the trust score of source systems impact the data survivorship process?
What is the role of deduplication criteria when applied to mandatory fields?
What is the role of deduplication criteria when applied to mandatory fields?
An exact match strategy is designed to identify similar records.
An exact match strategy is designed to identify similar records.
A predefined match model can be edited to suit specific business needs.
A predefined match model can be edited to suit specific business needs.
Adding at least one exact match rule in a match model helps reduce overmatching.
Adding at least one exact match rule in a match model helps reduce overmatching.
Machine learning (ML) models do not require any training processes.
Machine learning (ML) models do not require any training processes.
Custom match models can be created from scratch or copied from existing models.
Custom match models can be created from scratch or copied from existing models.
Flashcards
MATCH_DIRTY
MATCH_DIRTY
A state that indicates records are awaiting matching criteria checks.
MATCH_INDEXED
MATCH_INDEXED
A state that indicates records have been indexed for matching.
MATCHED
MATCHED
A state that indicates records have been successfully matched against other records.
CONSOLIDATED
CONSOLIDATED
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NOT_READY_FOR_MATCH
NOT_READY_FOR_MATCH
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Search_dirty
Search_dirty
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Search_indexed
Search_indexed
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CreateMode
CreateMode
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Validation
Validation
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Informatica MDM SaaS
Informatica MDM SaaS
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Match and Merge
Match and Merge
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Fuzzy Match
Fuzzy Match
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Exact Match
Exact Match
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Merging
Merging
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Rule-based matching
Rule-based matching
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Probabilistic Matching
Probabilistic Matching
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Last Writer Wins
Last Writer Wins
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Field Prioritization
Field Prioritization
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Master Data Management (MDM)
Master Data Management (MDM)
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Improved Data Quality
Improved Data Quality
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Operational Efficiency
Operational Efficiency
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Informed Decision Making
Informed Decision Making
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Data Complexity
Data Complexity
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Scalability
Scalability
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False Positives and Negatives
False Positives and Negatives
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Establish Clear Rules
Establish Clear Rules
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Iterative Process
Iterative Process
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Data Stewardship
Data Stewardship
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Monitoring and Auditing
Monitoring and Auditing
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Data Profiling Tools
Data Profiling Tools
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Machine Learning Algorithms
Machine Learning Algorithms
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Integration with Other Systems
Integration with Other Systems
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Crosswalk
Crosswalk
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Crosswalks in Reference Data Sets
Crosswalks in Reference Data Sets
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Basic Rule Associations
Basic Rule Associations
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Advanced Rule Associations
Advanced Rule Associations
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DaaS Rule Associations
DaaS Rule Associations
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Study Notes
Record States
- A record can be in one of three states: Active, Pending, or Deleted.
Consolidation States
- Various consolidation states represent the progress of record matching and consolidation.
MATCH_DIRTY
: Indicates records are awaiting matching criteria checks.MATCH_INDEXED
: Records have been indexed for matching.MATCHED
: Records have been successfully matched against other records.CONSOLIDATED
: Records have been consolidated into a single representative record.NOT_READY_FOR_MATCH
: Records are not yet ready for matching due to missing or incomplete data.
SearchIndex States
- SearchIndex is a component used for efficient record matching.
Search_dirty
: Indicates the SearchIndex needs to be updated.Search_indexed
: The SearchIndex is up-to-date and ready for matching.
CreateMode
- A state that determines how new records are handled during data consolidation.
Validation
- A process for verifying the accuracy and completeness of data in an Informatica environment.
Informatica MDM SaaS Overview
- A cloud-based solution that focuses on ensuring consistent and accurate master data across an organization.
- Offers scalability, flexibility, and reduced on-premises IT costs through a Software as a Service (SaaS) model.
###Â Key Features
- Integrates seamlessly with various data sources and applications.
- Ensures high-quality, consistent data through cleansing and validation processes.
- Facilitates management of workflows and business rules for master data.
- Provides a comprehensive view of customers, products, and other key entities.
- Manages multiple domains (e.g., customers, suppliers, products) within a single platform.
Benefits
- Easily adapts to growing data needs without significant infrastructure changes.
- Reduces overhead costs related to hardware and maintenance.
- Rapidly deploys master data management capabilities.
- Enables cross-departmental data collaboration for enhanced decision-making.
Architecture
- Designed for optimal performance and resilience in a cloud environment.
- Utilizes microservices architecture for modular functionality and ease of updates.
- Built-in security measures to comply with industry regulations and protect sensitive data.
Use Cases
- Enhances customer engagement and personalized service through Customer 360 initiatives.
- Maintains compliance with data governance standards for regulatory purposes.
- Ensures accurate and consistent supplier and product data for supply chain management.
Implementation Considerations
- Establish clear policies for data stewardship and governance.
- Plan for stakeholder engagement and training during implementation.
- Identify critical integrations with existing systems early in the process.
Challenges
- Overcome issues related to fragmented data storage across various systems.
- Ensure that end-users are adequately trained and supportive of the MDM processes.
- Maintain ongoing data quality efforts post-implementation.
Master Data Management (MDM)
- Centralizes and manages key business data like customers, products, and suppliers.
- Aims to ensure a single, consistent view of data across an organization.
Match and Merge in MDM
- Processes used to identify and consolidate duplicate records.
Matching
- Goal: Identify records that refer to the same real-world entity.
- Techniques:
- Exact Match: Identifies identical records.
- Fuzzy Match: Identifies similar records based on defined criteria, accommodating misspellings or variations.
- Matching Criteria:
- Name, address, phone number, email, etc.
- Methods:
- Rule-based matching: Uses pre-defined rules to determine matches.
- Probabilistic matching: Uses statistical algorithms to assess match probability.
Merging
- Goal: Combine duplicate records into one single, accurate record.
- Merge Strategies:
- Last Writer Wins: The most recently updated record is retained.
- Field Prioritization: Certain fields are prioritized over others.
Benefits of Match and Merge
- Improved Data Quality: Ensures accuracy, consistency, and reliability of data across the organization.
- Operational Efficiency: Eliminates redundant data, streamlining operations.
- Informed Decision Making: Provides a single source of truth for analysis and reporting.
Challenges of Match and Merge
- Data Complexity: Variations in data formats and structures make matching difficult.
- Scalability: Managing large volumes of data can strain matching algorithms.
- False Positives and Negatives: Mistakes in matching can lead to data inaccuracies.
Best Practices for Match and Merge
- Establish Clear Rules: Define criteria for matching and rules for merging.
- Iterative Process: Continuously refine matching algorithms based on feedback and results.
- Data Stewardship: Involve human oversight in verifying matches and merges.
- Monitoring and Auditing: Track match/merge activities and outcomes to ensure quality.
Technologies used in Match and Merge
- Data Profiling Tools: Analyze data quality and prepare it for matching.
- Machine Learning Algorithms: Enhance matching accuracy over time through learning.
- Integration with Other Systems: Connect MDM with CRM, ERP, and other data sources for comprehensive data management.
Business Entities
- A business entity is an essential component of an organization's data structure.
- Entities represent real-world objects relevant to the business, like customers, employees, or products.
- The fields within an entity define the data points needed to effectively manage and analyze information.
- Business entities are designed to integrate data from different source systems into a unified master data repository.
- For example, a car manufacturing company might use entities like customers, employees, suppliers, factories, materials, and products to organize its data.
- The specific entities and fields used will depend on the organization's data needs and the information provided by its source systems.
- Once defined, business entities can be configured and customized to include the required fields for data management.
Crosswalks
- A crosswalk is a visual representation of a one-way relationship between code values in a pair of code lists
- Crosswalks enable translation between different variations of the same type of code value within a reference data set
- Reference data sets often contain many code lists, each of which represents a variation of the same type of code value
Crosswalks in Reference Data Sets
- A crosswalk is a visual representation of a one-way relationship between code values.
- This relationship exists between a pair of code lists.
- A reference data set can contain multiple code lists.
- Each code list in a reference data set contains a variation of the same type of code values.
- Crosswalks enable translation between these variations in different code lists.
Rule Associations for Business Entities
-
Basic Rule Associations
- Link a business entity field to a simple rule.
- Conditions use predefined functions like "Regular Match" and "Concatenate".
- Best for high volume cleansing transactions.
- Example: combining first and last names into a full name field.
- Used for input validation and transformation.
-
Advanced Rule Associations
- Connect a business entity field to a Cloud Data Quality rule specification.
- Use predefined or customized rules from Cloud Data Quality.
- Map input and output fields of the rule with entity fields.
- Example: DUNS SSN Validation for verifying numbers.
- Validate empty fields with a returned value.
-
DaaS Rule Associations
- Link a field group to Informatica Data as a Service (DaaS).
- Only applicable to postal addresses, email addresses, and phone numbers.
- Requires valid license keys in Global Settings.
- Used for batch and real-time validation and enrichment.
DaaS Rule Association Types
-
Batch Processing
- Uses a predefined address verifier asset from Cloud Data Quality.
- Predefined mapping of input and output fields.
- No support for email, phone, or custom field groups in batches.
-
Real-Time Processing
- Uses Informatica Address Verification as the default DaaS provider.
- Predefined mapping of address verification fields.
- Supports real-time enrichment of email addresses and phone numbers.
- Can create custom DaaS rules for field groups with custom mappings.
Rule Associations in Business Entity Fields
-
Basic rule associations are simple conditions-based rules linked to a business entity field.
- Conditions are predefined functions like Regular Match and Concatenate.
- Use Cases:
- High concurrent transactions for data cleansing, e.g., merging first and last names for full name generation.
- Validating empty input fields.
- Not applicable for required fields.
- Transformation or validation functions can be used to set conditions:
- Validation functions check if data matches the specified condition.
- Transformation functions modify source data.
-
Advanced rule associations link business entity fields to Cloud Data Quality rule specifications.
- Predefined rule specifications can be used, or custom specifications can be created within Cloud Data Quality.
- Mapping Input/Output: Input and output fields of the rule specification must be mapped to business entity fields.
- Use Cases:
- Validating empty input fields.
- Validate specific formats like DUNS and Social Security numbers using the "DUNS SSN Validation" rule specification.
-
DaaS (Data as a Service) rule associations link field groups to DaaS services.
- Applicable Fields: Postal addresses, email addresses, and phone numbers.
- License Requirements: Valid license keys must be added in Global Settings.
- Use Cases:
- Batch processing: Use a predefined address verifier asset from Cloud Data Quality to validate and enrich addresses in bulk.
- Real-time processing:
- Addresses: Use Informatica Address Verification.
- Email addresses: Use Informatica Email Verification.
- Phone numbers: Use Informatica Global Phone Validation.
DaaS Rule Association Details
-
Batch processing:
- Uses the predefined address verifier asset from Cloud Data Quality.
- This asset has pre-defined field mappings.
- Not applicable for validating/enriching email, phone, or custom field groups in batches.
-
Real-time processing:
- Business 360 Console uses Informatica Address Verification as the default DaaS provider for real-time address validation.
- Predefined field groups come with a default DaaS rule association and pre-mapped fields from the address verification DaaS provider to business entity fields.
- Custom DaaS rules: Can be created for custom field groups and mapped as needed.
Survivorship Process
- After matching source records, the Survivorship process creates a master record with the most trusted values
- Requires configuring survivorship rules and ranking source systems
Source Ranking
- Determines the reliability of source systems
- Higher rank = more reliable data
- Each ranking is saved with a unique label (e.g., Rank 1, Rank 2 etc)
Survivorship Rules
- Define conditions to determine the trusted value for a field
- Can be applied to individual fields, field groups, or all fields in an entity
- Types include Decay, Maximum, Minimum, Source Last Updated, and Block Survivorship
Decay Survivorship
- Trusted value based on trust levels and decay rate of field values
- Trust level: confidence in the source system
- Decay rate: rate at which trust level decreases over time
- Uses a picklist to determine the code value for survivorship
Maximum Survivorship
- Identifies the maximum value of a field as the trusted value
- Uses a picklist to determine the code value for survivorship
Minimum Survivorship
- Identifies the minimum value of a field as the trusted value
Source Last Updated Survivorship
- Helps determine the trusted value during the merge process
- When trust score and source ranking are equal, the Source Last Updated Date field determines the trusted value
- Can be disabled, in which case trust score, source ranking, and last updated date determine survivorship
Block Survivorship
- Treats a field group as a single unit, applying survivorship configuration to all fields within the block
- By default, field groups are treated as blocks
- Can be disabled, allowing for specific configurations for fields within the group
- Nested field groups can be disabled from block survivorship as well
Deduplication Criteria
- Used to identify and merge duplicate field group values
- Applies survivorship rules to determine which values survive
- Can be configured for mandatory, nested, and optional fields
- Duplication identification is case-insensitive
Survivorship Configuration Summary
- Survivorship rules: Determine trusted value based on field value
- Source system ranking: Field values from higher ranked systems are trusted
- Source last updated date: Most recent updated record wins
- Last updated record in MDM SaaS: Most recently updated record in MDM SaaS wins
- Latest created record in MDM SaaS: Most recently created record in MDM SaaS wins
Match and Merge Process
- The match and merge process is configured to identify and resolve duplicate records.
- This configuration, known as the match model, uses either machine learning (ML) models, declarative match rules, or both.
- Declarative match rules employ exact or fuzzy match strategies for identifying similar records.
- To ensure optimal performance, including at least one exact match rule in the match model is recommended.
- ML models require a training process based on user-labeled record pairs, which minimizes the need for extensive declarative match rules.
- Users can configure custom match models by creating them from scratch or copying existing models, but predefined models cannot be edited.
- Declarative match rules define conditions and business entity fields to detect duplicates.
- Configurable properties within declarative match rules include:
- Description
- Match strategy
- Merge strategy
- Match criteria
- Match level
- Merge threshold
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