Data transformation is a part of the overall data preparation process. However data transformation is also a process in itself, made of several operations: converting, cleansing, structuring. It can entail spotting and deleting duplicates, converting data types, as well as enriching the dataset as a whole.
The aim? Transforming raw data into clean, secure and standardized data – which will therefore become easily accessible and actionable in many ways. Data transformation is designed to get the data ready before it is used to guide and support decision-making in a Business Intelligence perspective.
It might now appear obvious that data transformation is a crucial process, especially in our Big Data era. It is the role of data engineers to ensure that the data used down the pipe is consistently functional or actionable, and thus truly enabling the company to be data-driven. This means converting data in order to match its destination system, which will depend on the BI tool used internally, or even the department using it.
The data transformation process encompasses many types of operations, among which:
Prior to this transformation process, it is crucial to follow a data discovery one. This will enable analysts to understand the dataset and determine which data transformation operations must be performed.
Data transformation is beneficial in many ways:
Organizations should prioritize ELT and cloud-based data warehouses because of their scalability: with ELT, raw data remains available in the database’s history – so it can be transformed again in the future.