Data transformation is the process of converting data from one format or structure into another format or structure). It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, and data preparation. Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data). The process of data transformation involves identifying data sources and types, determining the structure of transformations that need to occur, and defining how fields will be changed or aggregated. The key steps in data transformation include extracting data from its original source, transforming it, and sending it to the target destination, such as a database or data warehouse.

Data transformation can involve converting data types, removing duplicate data, enriching the source data, and refining metadata to make it easier to manage data more effectively. Data transformation can increase the efficiency of analytic and business processes and enable better data-driven decision-making. The benefits of data transformation include better-organized data that is easier for both humans and computers to use, improved data quality, and protection of applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats.

Data transformation can be performed manually or automatically, and tools and technologies used for data transformation can vary widely based on the format, structure, complexity, and volume of the data being transformed). The final step in the data transformation process is data review, which focuses on ensuring the output data meets the transformation requirements). It is typically the business user or final end-user of the data that performs this step, and any anomalies or errors in the data that are found are communicated back to the developer or data analyst as new requirements to be implemented in the transformation process).