Data mapping is the process of linking data fields from one source to their corresponding fields in a target destination. It's commonly used in data integration, migration, transformation, and synchronisation projects where data needs to be moved or synchronised between different systems, databases, or formats.

Here are the key aspects of data mapping:

Source and Target Identification: The first step in data mapping involves identifying the source(s) and target(s) of data. This could include databases, files, applications, APIs, or any other data repositories.

Field Mapping: In this step, each field or attribute in the source data is mapped to its corresponding field or attribute in the target data. This ensures that data is correctly transferred and aligned between the source and target systems.

Data Transformation: Data mapping often involves data transformation, where data values may need to be converted, modified, or aggregated to match the format or requirements of the target system.

Data Validation: Once the mapping is defined, it's essential to validate the accuracy and completeness of the mapped data. This may involve testing the data mapping process with sample datasets to ensure that data is transferred correctly and without errors.

Complex Mapping Rules: In some cases, data mapping may involve complex mapping rules or logic, such as conditional mappings, lookups, or calculations. These rules ensure that data is transformed accurately according to business requirements.

Documentation: It's crucial to document the data mapping process, including the mapping rules, source-to-target relationships, and any data transformations applied. Documentation helps in understanding and maintaining the data mapping process over time.

Automated Mapping: With the advent of data integration tools and platforms, much of the data mapping process can be automated. These tools often provide visual mapping interfaces, drag-and-drop functionality, and built-in transformation functions to streamline the mapping process.

Data mapping is essential in various scenarios, including:

Integrating data from multiple sources into a data warehouse or data lake.

Migrating data from one system to another during system upgrades or replacements.

Synchronising data between different applications or databases in real-time.

Standardising data formats and structures across an organisation.

Overall, data mapping ensures that data is accurately transferred, transformed, and synchronised between different systems or formats, enabling organisations to make informed decisions and derive value from their data.