Data Mapping Assessment (Data Model)

To assess data mapping validation in the data model within the Pricefx data readiness methodology, you can perform the following key tasks:

1. Review Data Mapping Documentation: Start by reviewing the documentation that outlines the data mapping process and specifications. Understand the mapping rules, transformations, and logic applied to map the source data to the data model.

2. Identify Source Systems: Identify the source systems that provide data to the data model. Determine the data extraction methods, data formats, and data structures used by each source system. Gain a clear understanding of the data elements and their meanings in the source systems.

3. Validate Data Mapping Rules: Assess the accuracy and completeness of the data mapping rules defined for each source system. Ensure that the mapping rules accurately transform and map the source data to the corresponding data entities and attributes in the data model.

4. Verify Data Transformation: Validate the data transformation processes used to convert the source data into the required format and structure of the data model. Ensure that the transformation logic accurately represents the business rules and requirements.

5. Perform Data Mapping Testing: Execute data mapping tests by comparing a subset of source data with the mapped data in the data model. Verify that the data mapping process produces the expected results and that the data is correctly transformed and mapped.

6. Assess Data Consistency: Evaluate the consistency of the data mapping across different source systems and data entities. Verify that the same data elements from different source systems are mapped consistently to the corresponding data attributes in the data model.

7. Validate Data Integrity: Ensure that the data mapping preserves the integrity of the data. Check that primary key constraints, referential integrity, and other data constraints are maintained during the mapping process. Validate that data dependencies and relationships are accurately represented in the data model.

8. Address Data Mapping Discrepancies: Identify any discrepancies or inconsistencies between the source data and the mapped data in the data model. Investigate the root causes of the discrepancies and take appropriate actions to resolve them, such as adjusting the mapping rules or data transformation processes.

9. Document Data Mapping Findings: Document the findings from the data mapping validation process, including any issues, discrepancies, or improvements identified. Maintain a record of the corrective actions taken to address the mapping issues and ensure transparency and traceability.

10. Engage Stakeholders: Involve relevant stakeholders, such as data analysts, data stewards, and IT teams, in the data mapping validation process. Share the validation results and gather their feedback and validation. Address any concerns or recommendations raised by stakeholders.

By performing these key tasks, you can assess data mapping validation in the data model within the Pricefx data readiness methodology. This validation ensures that the data mapping process accurately and consistently transforms the source data into the required format and structure of the data model, maintaining data integrity and reliability for pricing analysis and decision-making.