MDM Saas Sample MCQ Library

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.

### 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