Data Transformation and How Does It Work?

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Written By Nidhi Sharma

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Data transformation can be defined as the process of changing data from one format, standard, or structure to another. This is done without altering the data content. It is usually used to prepare the data for use by apps or users or improve data quality.

What is Data Transformation?

Data Transformation is the process of changing data from one format, structure, or standard without altering the data. It's used primarily to make data available for users or apps to use, or to improve data quality.

Data transformation refers to the modification of data's format, organization, and values. When data analytics projects are being undertaken, data can be modified at two points in their data pipeline.

On-premises data warehouses are often used with an ETL (extract transform, load) method, with data transformation acting as an intermediate step. Cloud-based data warehouses can increase computational and storage capacities with delay estimates of seconds or minutes.

ELT (extract-load, transform) allows organizations to load raw data directly into their data warehouses, without any preload adjustments, and then convert it when they receive a query. Data transformation can be used in many operations such as data migration, integration, and wrangling.

It is essential for any company that wants to use its data to deliver timely business insights. As the amount of data is increasing, organizations need reliable methods to use it. Data transformation is an important part of using this data because it ensures that information is consistent, safe, and easily accessible to the intended business users.

Let's now look at the steps of data transformation.

1. Data Discovery

The first step is to identify and interpret the original data format. This is usually done using a data profiling tool. This stage helps you to determine what data needs to be transformed into the desired format. Data professionals use data profiling or scripts to understand the data's structure and features and decide how users should modify it.

2. Data mapping

This stage is where the actual transformation takes place. Data specialists use this stage to match or connect data elements from different resources.

3. Code Generation

To make the conversion process successful, you must create a code that executes the task. These codes are usually produced using a data transformation platform or tool. This phase is where the code required to transform data is created by either data transformation technology or data experts creating scripts.

4. Code Execution

Code execution is the process of transforming input data into the desired output. It follows the previously planned and codified data transformation process. Data is taken from the source(s), which can be structured, streamed, or log files. After data has been collected, operations such as aggregation and format conversion or merging are performed.

5. Review

To ensure accuracy, formatting is checked on the transformed data. Data professionals and end-users verify that the data flows to meet the specified transformation criteria. If not, they address any errors or anomalies. Not all data needs to be transformed; some data can be used as is.

Notably, depending on the technique used, the meaning or process of data conversion can differ. These include:

  • Revision: Revising data must be done in a way that supports the intended use. There are many ways to accomplish this. Dataset normalization is a process that removes duplicates from the data set. This improves the data. Data purification ensures that data can be formatted.
  • Manipulation: This involves generating new values or altering the state of data by computing. Through manipulation, users can transform unstructured data into structured data that machine-learning algorithms can use.
  • Separating: Granular Analysis entails dividing the data into its parts. Splitting allows you to create separate columns for each value in a column that contains multiple values. This allows you to filter based on particular values.
  • Data smoothing: This method cleans up the data set by removing noisy or corrupt data. Trends can be best found by eliminating outliers.
  • Data aggregation: This method gathers data from multiple sources and converts it into an easily analyzed summary form. An example of raw data is statistics such as sums and averages.
  • Interval Labels: This technique is used to simplify analysis and increase the effectiveness of continuous data. Decision tree algorithms are used to convert big data sets into categorical information.
  • Generalization: By using the idea of hierarchies, and building layers of sequential summaries data, low-level attributes can be transformed into high-level attributes. As a result, clear data snapshots can be produced.
  • Building attributes: This method speeds up the mining process by allowing the creation of a new set of characteristics from an existing one.


Data transformation is an integral part of any IT organization. You need interoperability in order to fully utilize the potential of your IT systems. Data transformation allows information assets to be shared across platforms and systems. Data transformation also allows for greater standardization and quality improvement of enterprise data, allowing you to use it to create exponential value.

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