Data transformation is a process of transforming or mapping incoming data into another form, structured or unstructured, for further analysis or long-term storage. Data transformation is often performed as a pre-processing step for analytics, data warehousing, or other related activities. It is a specialized form of software engineering in which data is transformed from one structure to another in order to improve readability or usability.
Data transformation techniques can range in complexity from simple mappings of one data schema to another to complex operations that involve several different stages of transformations. Common examples of data transformations include data cleansing, normalization, denormalization, data blending and integration, aggregation and summarization, filtering, sorting, and validation. These data transformations are often governed by programming logic to ensure rules, structure, accuracy, consistency, and completeness of the data.
Data transformation is performed by a variety of tools, methods, and platforms. Commonly used platforms are ETL and ELT tools, software packages, big data platforms, cloud-based computing, and machine learning technologies.
Data transformation is essential to many types of analytics and other related activities, as it is used to transform data from disparate sources into user-friendly formats. By applying a combination of data cleansing, normalization, and other data transformation techniques, organizations can more easily derive meaningful insights from data.
In simple terms, data transformation is the process of taking raw data and turning it into usable information. It is a critical step in the data analytics process and one of the key drivers of successful data-driven decision-making.