When data is in the format that is hard to use then the appropriate transformation steps can be applied to transform it into a usable form. These alterations, which range from flattening hierarchical structures, to changing data types, transform raw data into an appropriate format that can be used by modern software applications like statistical analysis tools or business intelligence toolkits.
The first step is to determine the data that needs to be transformed. This is done by using data profiling and other similar methods that give an overview of what data appears like. This information is then used to determine the transformation that will take place. This can involve things such as character encoding conversion and database or file structure changes, aggregation or joining of data. When the mapping process is completed, the code to run the transformations is generated. This is usually done with a dedicated data-transformation platform or tool.
Once the code is ready to go, it can be executed. The data that has been transformed will be ready to be loaded into the destination system, such as a data store or analytics platform.
It is vital to note that data transformation should take place before the data is loaded into the system. Otherwise any issues that may arise during the transformation process could impact the quality of data that is loaded into a system. This is a crucial element of end-to-end data management which is the process of sustaining the same and accurate data throughout the entire organization. Banks that use this approach have seen that they can to improve regulatory compliance, reduce costs and increase revenue.