Results Management
The ultimate goal of a Data Science Management (DSM) project is to deliver trusted results that support better business decisions. Managing results effectively is just as important as managing the input data and models that produced them.
Result Statistics
Predictions and analytical results should be accompanied by statistics that help users understand the quality, confidence, and performance of the results. These metrics allow stakeholders to evaluate whether the results are suitable for operational and business decisions.
Traceability
Results must be fully traceable. Users should be able to determine:
- Which model generated the prediction
- Which input data was used
- Which rules and parameters were applied
- When the prediction was created
- Who approved or validated the results
Traceability provides transparency and helps build trust in the results generated by the DSM process.
Migration to Enterprise Systems
Once results have been validated, they should be transferred back to the enterprise data warehouse or other corporate systems where they can be shared, governed, and used by the wider organization.
Maintaining a clear connection between source data, models, and final results ensures that knowledge gained during a project remains available long after the project has been completed.