transformation is the process of converting data from one format or structure into another format or structure. Data transformation is critical to activities such as payroll formatting, data integration and data management. Data transformation can include a range of activities: you might convert data types, cleanse data by removing nulls or duplicate data, enrich the data, or perform aggregations, depending on the needs of your project.
Code generation is the process of generating executable code (e.g. SQL, Python, R, or other executable instructions) that will transform the data based on the desired and defined data mapping rules. Typically, the data transformation technologies generate this code based on the definitions or metadata defined by the developers. Traditionally, data transformation has been a bulk or batch process, whereby developers write code or implement transformation rules in a data integration tool, and then execute that code or those rules on large volumes of data. This process can follow the linear set of steps as described in the data transformation process above.
Data review is the final step in the process, which focuses on ensuring the output data meets the transformation requirements. It is typically the business user or final end-user of the data that performs this step. Any anomalies or errors in the data that are found and communicated back to the developer or data analyst as new requirements to be implemented in the transformation process. Some systems can even find these errors or anomalies for you.
The goal of the data transformation process is to extract data from a source, convert it into a usable format, and deliver it to a destination. This entire process is known as ETL (Extract, Load, Transform). During the extraction phase, data is identified and pulled from many different locations or sources into a single repository.
Today, the reality of big data means that data transformation is more important for businesses than ever before. An ever-increasing number of programs, applications, and devices continually produce massive volumes of data. And with so much disparate data streaming in from a variety of sources, data compatibility is always at risk. That's where the data transformation process comes in: it allows companies and organizations to convert data from any source into a format that can be integrated, stored, analyzed, and ultimately mined for actionable business intelligence.
The ever-increasing volume of data offers your business limitless opportunities to make better decisions and improve results. How can Payroll and HR departments decrease their manual input and boost productivity? How can you take what you know about your business, customers, and competitors and make it more accessible to everyone in your enterprise? The answer is data transformation.