Data Transformation Rules are set of computer instructions that dictate consistent manipulations to transform the structure and semantics of data from source systems to target systems. There are several types of Data Transformation Rules, but the most common ones are Taxonomy Rules, Reshape Rules, and Semantic Rules.
These rules map the columns and values of the source data with the target. For instance, a source can describe its transactions as having two columns: a settlement amount and a type, where the type can be one of three options.
These rules specify how to pull data elements together from the source side, and how to distribute them on the target side. For example, a retailer might provide all transaction data in a single file, but the aggregator needs to split it into three tables, one for transactions, another for retailer data, and yet another for consumers.
These rules articulate the meanings of data elements and how the business uses them to describe its domain. For example, what constitutes a successful transaction? And how should its final settled amount be computed after accounting for refunds? Each data provider has its own semantics that makes sense in the context of its operations, but one that the data aggregator must reconcile with all other providers’ data definitions.