Data transformation refers to converting data from one format, structure, or type to another to make it more suitable for analysis, processing, or storage. This process can involve cleaning, normalizing, standardizing, aggregating, or merging data from multiple sources to create a unified view of the data. With data transformation solutions, companies aim to prepare data for analysis or to meet specific requirements for a particular application.
It is an integral part of data integration, data migration, and data warehousing, where the consolidated data from various sources is unified into a single format.
Data transformation involves various techniques, such as data mapping, data cleansing, data aggregation, and data normalization. On the other hand, it can comprise of more complex operations, such as data enrichment, where data is augmented with additional information, and data denormalization, where data is organized in a non-standard way to improve query performance.
Data transformation can be performed manually, using tools like Excel or SQL, or through automation with specialized data integration tools and platforms. Automated data transformation can reduce the risk of errors and improve efficiency by handling complex data transformations at scale.