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