When data is in a format that is too hard to use then the appropriate transformation methods can be used to transform it into usable format. From changing data types to flattening hierarchical patterns, these changes transform raw data into something that modern software systems like statistical analysis programs or business intelligence toolsets can use.
The first step is to determine the data that needs to be transformed. This is done using data profiling or other similar processes that provide a high-level view of what the data appears like. This data is then used to define the transformation that takes place. This can include character encoding, database or file changes and aggregation, or joining of data. When the mapping process is completed, the code to execute the transformations is generated. This is usually done with an exclusive data transformation tool or platform.
Once the code has been created, it can be run. The data that has been transformed will be ready to be loaded in the system of destination such as a data store or analytics platform.
It is vital to note that data transformation should occur prior to when data is loaded into a system. In the event of a problem any issues that may arise during the transformation could impact the accuracy of the final data that is loaded into the system. This is a crucial aspect of the process known as end-to-end, a process that ensures consistent and accurate data across the entire organization. Banks that use this approach have found that they are able to improve regulatory compliance while reducing costs and increasing revenue.
