What You Need to Know About Informatica MDM Cloud SaaS — From Someone Who's Actually Worked on It

 How do you configure Match and Merge in Informatica MDM Cloud Saas?

Configuring Match and Merge in Informatica MDM Cloud SaaS is a crucial part of managing and consolidating master data across multiple domains like customer, product, or supplier. The cloud-based version simplifies some of the setup processes while retaining the core functionality of matching duplicate records and merging them into a single, trusted "golden" record. Here's how to configure it:

Step 1: Access the Informatica MDM Cloud SaaS Console

Log into Informatica Cloud using your organization's credentials.

1. Navigate to the MDM Cloud module.
2. Select the Entity for which you want to configure Match and Merge (e.g., customer, product, etc.).

Step 2: Configure Match Rules

Match rules help the system identify duplicate or similar records that may need to be merged.

Steps:

• Create a Match Rule Set:

1. Go to the Match configuration section within the selected entity.
2. Create a New Match Rule Set for the entity.

• Select Matching Attributes:

1. Choose se the fields that will be compared for duplicates, such as Name, Address, Phone, or Email.
2. You can choose fields like customer name, product name, or supplier ID depending on the entity you're working on.

• Define Match Strategies:

1. Exact Match: Use this for fields where you expect the same exact value (e.g., email or customer ID).
2. Fuzzy Match: Use this for fields that may have slight differences, such as name or address, where the system needs to account for typos or variations.
3. Soundex Match: This is useful for names that sound similar but are spelled differently.

• Set Matching Weights:

1. Assign weights to different fields to indicate their importance in determining a match (e.g., give more weight to fields like email and less to fields like address).

• Define Match Thresholds:

1. Set a threshold score for matches (e.g., 90% confidence for strong matches, 80% for possible matches). If a match score exceeds the threshold, the records will be flagged as duplicates.

Step 3: Configure Merge Rules

Once records are matched, merge rules define how the system consolidates them into a single, "golden" record.

Steps:

1. Define Survivorship Rules:

• Field-Level Survivorship: Set rules for each attribute to decide which value to keep when merging. Options include:

1. Most Recent Record: Keep the most recently updated value.
2. Highest Source System Priority: Prioritize values from trusted or preferred source systems.
3. Field Completeness: Keep the most complete value for fields like addresses.

1. Auto or Manual Merge:

• Auto Merge: Set a rule to automatically merge records if the match score exceeds a certain threshold (e.g., >90%).

• Manual Merge: If the match score is lower or uncertain, you can opt to manually review and approve the merge before finalizing.

1. Test the Merge Rules:
   
   
   Run test scenarios on sample records to ensure that the configured match and merge rules work as expected.

Step 4: Run Match and Merge Process

Now that the rules are defined, it's time to run the match and merge process on your dataset.

Steps:

• Execute the Match Process:

1. Run the matching process as a batch job through Informatica Cloud.
2. Review the match results, including the scores, to verify that the system is correctly identifying duplicates

• Merge the Records:

1. After matching, execute the merge process to consolidate the duplicate records into a single "golden" record.
2. The merge process will follow the survivorship rules you’ve set, merging data automatically if applicable.

Step 5: Monitor and Fine-tune

After running the match and merge process, it’s essential to review the results and make adjustments if necessary.

Steps:

• Review Merged Records:
  
  
  Examine the merged records (golden records) to ensure that the match and merge rules have worked correctly.
• Adjust Match and Merge Rules:

  
  

  Based on the review, you can fine-tune the match weights, thresholds, and merge rules to improve accuracy.
• Set Up Ongoing Match and Merge Jobs:

  
  

  Configure ongoing or scheduled match and merge jobs for continuous data processing. This is especially useful if your data is regularly updated.
• Best Practices:

1. Preprocess Data: Use data quality tools to standardize, clean, and validate data before applying match and merge to improve accuracy.
2. Incremental Matching: For ongoing data updates, use incremental matching to only process new or updated records.
3. Monitor Match Accuracy: Regularly check the match accuracy and merge results to ensure data quality remains high

• Benefits of Configuring Match and Merge in MDM Cloud SaaS:

1. Improved Data Quality: Ensures that duplicate records are consolidated, resulting in cleaner and more accurate data.
2. Automated Process: By setting auto-merge rules, much of the deduplication and merging process can be automated.
3. Flexibility: Informatica MDM Cloud SaaS offers flexibility in defining match strategies and merge rules, making it adaptable to various data scenarios.

By following these steps, you can efficiently configure Match and Merge in Informatica MDM Cloud SaaS, resulting in clean, unified master data across your organization.

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