Defining Data Model Quality Metrics for Data Vault 2.0 Model Evaluation

Abstract

Designing a database is a crucial step in providing businesses with high-quality data for decision making. The quality of a data model is the key to the quality of its data. Evaluating the quality of a data model is a complex and time-consuming task. Having suitable metrics for evaluating the quality of a data model is an essential requirement for automating the design process of a data model. While there are metrics available for evaluating data warehouse data models to some degree, there is a distinct lack of metrics specifically designed to assess how well a data model conforms to the rules and best practices of Data Vault 2.0. The quality of a Data Vault 2.0 data model is considered suboptimal if it fails to adhere to these principles. In this paper, we introduce new metrics that can be used for evaluating the quality of a Data Vault 2.0 data model, either manually or automatically. This methodology involves defining a set of metrics based on the best practices of Data Vault 2.0, evaluating five representative data models using both metrics and manual assessments made by a human expert. Finally, a comparative analysis of both evaluations was conducted to validate the consistency of the metrics with the judgments made by a human expert.