Data - the most precious element in the digital world, is of little use in its raw form. Akin to extracting metals from their ores, data has to be converted into useful information for getting the maximum use out of it in algorithms.Any form of analytics needs to use transformed data, be it visualization, analysis, or reporting.This brings us to the question, what exactly do we mean by data transformation?

Data Transformation - The Definition

The process of enriching, managing, and manipulating the raw data to make it comply with different processing platforms is called data transformation. In simpler words, it is the process of converting one form of data to another form that improves the data quality and makes it more compatible with the target system.

Advantages of Data Transformation

  1. Transformed data is more organized and hence it is easier for humans and computers to decipher.
  2. Transformed data can be compressed orderly and stored securely.
  3. Transformed data is easier to port within various analytics platforms, saving time and effort.

After learning about the ‘what’ of data transformation, it is interesting to note ‘how’ is data transformed.

Steps in Data Transformation

Data Discovery and Interpretation

Identifying the type of data at hand is the first step in data transformation. To get it in the format of our interest, we should first determine the format it currently is in. Data profiling tools like Atlan, Microsoft DOCS help in identifying the data.

Quality checks and Data Mapping

This involves eliminating corrupt values, redundant data and identifying missing values in the data set, and fixing it. Further on, analysts define how individual data fields should be modified, mapped, and filtered. 

Data Extraction and translation

In this step, data is extracted from its original format and is suitably replaced by the data as expected by the destination format. Data translation also involves converting the overall format of the source file into the format expected by the target destination. For instance, converting a .xls into .csv file

Quality checks and Data Encryption

Translated data is once again checked for missing fields and other translation-induced errors. When it is found to be satisfactory, data is encrypted for security purposes and stored in the destination.

It is tedious to perform these steps manually each time one needs a transformed data set. To avoid the inconvenience of re-inventing the wheel, several tools are available that do this procedure

How is Data Transformed?

There are two types of Data transformation

  1. Batch Type - Best suited to transform bulk data in one shot
  2. Interactive Type - Here the users can visualize the data and apply different transformation techniques to different elements in the data set

Data transformation tools

There are many ways in which data can be transformed. Some of the most popular methods are listed here

Scripts: For small projects, analysts can write Python or SQL-based scripts to perform their specific operations of extraction and translation. The advantage of this method is that you can write scripts specific to your dataset and for your use case. However, the responsibility of debugging the script in case of erroneous conversion and the maintenance of the script lies on the analyst.

For scenarios in which scripts are not quite feasible, analysts can explore the possibility of using readily available tools that simplifies the transformation and also offers the benefits of automation.

ETL tools are generally used for data transformation. ETL stands for Extract, Transform and Load, a combination of steps involved in data transformation.

There are two types of ETL tools, On-Premise, and Cloud-based ones.

On-Premise ETL tools: These are hosted on the company’s servers and can be used to automate the data transformation.

Cloud-based ETL tools: Cloud-based tools serve the same functions as on-premise ETL tools but are hosted on cloud servers instead of on-premise servers.


Data transformation is essential for better organizing the data and converting its structure and semantics to match the desired format. Transformed data is indispensable for activities such as data integration and data management. 

For organizations, having an encyclopedic view of their data gives them a solid foundation to take productive decisions and enhance their growth prospects. Data transformation proves to be crucial for such applications.