When data is in the format that is difficult to use then the appropriate transformation methods can be used to transform it into an usable format. These alterations include flattening hierarchical structures, to changing data types to transform raw data into a format that can be used by modern software programs such as statistical analysis tools or business intelligence toolkits.
The first step is to determine the data that needs to be transformed. This is accomplished using data profiling or similar processes that provide an overall view of what data appears like. This information is then used to determine the changes that will take place. This could be as simple as conversion of characters to encoding, database or file structure changes, aggregation, or joining of data. Once the mapping process is completed, the code to run the transformations is generated. This is usually done via an appropriate data transformation tool or platform.
When the code is prepared to go, it can be executed. This will result in transformed data ready to be loaded into the system that will be used as a destination, like an analytics or data warehouse platform.
It is important to note that data transformation should be completed prior to the time that data is loaded into an application. If not any issues that occur during the process can affect the quality of final data loaded into a system. This is a vital component to end-to-end management of data, which is the process of maintaining consistent and correct data throughout the entire enterprise. This approach has helped banks improve their compliance with regulations and reduce costs, while increasing revenues.