Orchid Connect

Data cleansing and transformation are critical steps in ensuring the accuracy and integrity of data during migration. Below is a sample template for a Data Cleansing and Transformation Strategy document:

Data Cleansing and Transformation Strategy Document

1. Objectives:

  • Data Cleansing Objectives:
    • First, identify and correct inaccuracies, inconsistencies, and errors in the source data.
    • Enhance data quality to meet Salesforce standards.
  • Data Transformation Objectives:
    • Second, translate source data into a format compatible with Salesforce.
    • Apply necessary transformations for data standardization and compatibility.

2. Data Assessment Findings:

  • Next, summarize key findings from the data assessment and profiling phase.
  • Identify specific data quality issues that need cleansing and areas requiring transformation.

3. Cleansing Steps:

  • Data Profiling and Analysis:
    • Additionally, perform a detailed analysis of data profiling results.
    • For instance, identify patterns, outliers, and shared data quality issues.
  • Error Identification:
    • Define rules to identify errors, inconsistencies, and inaccuracies in the source data.
    • Specify criteria for flagging records that require cleansing.
  • Data Standardization:
    • Standardize formats, naming conventions, as well as coding schemes for consistent data representation.
    • Implement rules for cleaning up common data quality issues.
  • Handling Duplicates:
    • Identify and merge duplicate records using appropriate matching criteria.
    • Implement procedures for preventing the creation of new duplicates during migration.

4. Transformation Steps:

  • Data Mapping Alignment:
    • Ensure alignment with the data mapping document.
    • Furthermore, validate that transformation rules are accurately defined.
  • Field Mapping and Conversion:
    • Map source fields to Salesforce fields, considering data types and formats.
    • Apply conversions for data types that differ between source and target systems.
  • Null and Default Value Handling:
    • Define rules for handling invalid or missing values in source data.
    • Specify default values for Salesforce fields when applicable.
  • Lookup Table Usage:
    • Utilize lookup tables to map source data values to standardized values in Salesforce.
    • Document the usage and maintenance of lookup tables.
  • Data Enrichment:
    • Explore opportunities for enriching data during the transformation process.
    • Integrate additional information from external sources, if applicable.

5. Validation and Testing:

  • Validation Rules:
    • Importantly, develop validation rules to check data integrity during and after transformation.
    • Identify conditions for rejecting records that do not meet validation criteria.
  • Testing Scenarios:
    • Define testing scenarios for data cleansing and transformation.
    • Include unit testing, integration testing, and user acceptance testing.

6. Incremental Loading:

  • Handling Incremental Data:
    • Establish procedures for handling incremental data loads.
    • Specify how changes to existing records will be managed during subsequent migrations.

7. Monitoring and Audit:

  • Monitoring Process:
    • In addition, implement continuous monitoring processes to identify and address data quality issues.
    • Establish checkpoints for ongoing data quality assurance.
  • Audit Trails:
    • Enable and review audit trails in both the source and target systems.
    • Document changes made during the cleansing and transformation process.

8. Data Governance and Ownership:

  • Ownership and Stewardship:
    • Assign data ownership and stewardship responsibilities.
    • Define processes for ongoing data governance.

9. Backup and Rollback Plan:

  • Develop a comprehensive backup plan to safeguard data before cleansing and transformation.
  • Establish a rollback plan in case of unexpected issues.

10. Communication Plan:

  • Communicate the data cleansing and transformation strategy to stakeholders.
  • Also, provide updates on progress and any significant findings.

11. Documentation:

  • Document the data cleansing and transformation process.
  • Meanwhile, create runbooks and manuals for ongoing reference.

12. Training:

  • Additionally, provide training for individuals involved in the data cleansing and transformation process.
  • Educate end-users on changes to data formats or structures.

13. Change Management:

  • Establish a process for managing changes to the cleansing and transformation strategy.
  • Explicitly specify approval procedures for modifications.

14. Roles and Responsibilities:

  • Clearly define roles and responsibilities for individuals involved in the cleansing and transformation process.
  • Also, identify who is responsible for reviewing and approving the strategy.

15. Document Approval:

  • Lastly, include a section for documenting approvals from relevant stakeholders.

Overall, this Data Cleansing and Transformation Strategy document serves as a guideline for systematically improving data quality and ensuring a smooth transition during the migration process. Adjust the template based on the specific needs and intricacies of your organization’s data migration project.

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