This process involves thorough quality control and continuous improvement practices to ensure the correctness, completeness, and consistency of data models. Using tools like the Data Model Scorecard® and implementing feedback loops for refinement, this process results in high-quality models that accurately reflect business requirements and enhance data governance. Stakeholders such as business analysts, data modelers, and developers are involved in reviewing and approving the models.