...
Validate the mapped and created values against the standard set of dimension values.
If any values are found to be incorrect or inconsistent, update the dimension master table and correct the values.
3. Reporting:
Generate KPIs and visualizations using the mapped and standardized dimension values.
Provide feedback to stakeholders on the quality and accuracy of the dimension values, and incorporate their feedback into the preprocessing and matching steps.
The above design allows for combining the benefits of a standard set of dimension values with the flexibility of fuzzy matching. The standard set of dimension values provides a consistent and structured way of organizing the data, while fuzzy matching allows for capturing variations and errors in the data. The preprocessing step ensures that the incoming data is standardized and normalized before the matching process, which improves the accuracy of the fuzzy matching algorithm. The validation step ensures that the mapped and created values are accurate and consistent with the standard set of dimension values. Finally, the reporting step generates KPIs and visualizations using the standardized dimension values and provides feedback to stakeholders to improve the overall quality of the data.
Low-level design for a combination of standard dimensions and fuzzy matching:
...