data transformation process

4 Powerful Stages of Data Transformation

Data transformation is a process of converting data in one format to other. The most common type of data transformation is converting raw data into clean and usable form.

Here are the steps involved in the process of data transformation

4 Powerful Stages of Data Transformation

The precise nature of data transformation will differ from case to case, the steps below are the most standard and essential stages of the data transformation process.

Source Analysis

The initial stage in data transformation is analyzing your data to define what sort of data you presently have, and what you intend to transform it into. It can be more tedious than it looks. For this reason, analyzing data precisely requires tools that can look deeper into the structure of a file and find what is really inside, rather than what a file name suggests is inside. You also need to specify the target format, the format that your data should be transformed. If you are not clear with the format already, you will have to read the documentation for the tool or system that will receive your transformed data to understand which data formats it supports.

Quality Assessment (Pre-Transformation)

Once you have identified which type of data formats you are operating with and which forms you will be transforming data into, you should perform a data quality assessment. It allows you to pinpoint problems, such as absent or corrupt values within a database or in the source data that could possibly lead to concerns during later stages of the data transformation process.

Data Transformation

After checking the data quality of your source data, you can initiate the process of Data Transformation. It means handling each element of your source data and substituting it with data that fits within the configuration conditions or your target data format. 

Quality Assessment (Post-Transformation)

To ensure your transformed data has maximum quality, you have to perform a data quality assessment. In this stage, you have to look for inconsistencies, lost information, or other mistakes that may have occurred during the data transformation process.

Even if your data had zero errors before transformation, there is a considerable possibility that problems would have been introduced during transformation.

In most possible scenarios, the data transformation stages defined above would be performed by software tools as a result of automation. So, if these stages sound like a job that you are not trained for, then worry not.

But it is essential for us to understand what the data transformation tools are doing at each stage of the data transformation process, and how each activity adds up to make data transformation possible.

Why Nallas?

At Nallas, we understand the significance of data as a foundation of any organization and help you to shape your data through modern data transformation approaches. We prepare and curate data for actionable insights, and manage enterprise data warehouses.

Get connected with us for the most trusted and reliable data transformation services.