Fact-Based Modeling is an effective approach for defining the structure and semantics of stored data. Data integration involves combining data residing in disparate sources into meaningful and valuable information. Data integration faces two key challenges: matching records from different sources corresponding to the same real-world entity, and transforming the associated data so that the combined information is meaningful. We show that a fact-based integration language can be tightly coupled with a data matching system and compiled to data transformation code for data integration.

We will apply this approach in two scenarios:
a Data Vault data warehouse environment, and
a project for sharing information nationally for child safety
We will show that automated integration code generation from a fact-based integration language reduces cost, contains fewer errors, and supports change and agility.

Our approach can be used to generate code directly, or as a conceptual front-end to traditional data management tools. Explaining the data management and governance problem to senior management,