Data Transformation: How It’s Work And Benefits

Meaning

The process of changing data from one format to another, often from the format of a source system to the required format of a destination system, is known as data transformation. Most data integration and data management operations, such as data wrangling and data warehousing, need data transformation.

Working Process Of Data Transformation

The goal of the data transformation process is to take data from a source, change it into a format that can be used, and send it somewhere else. This whole process is called ETL (Extract, Load, Transform). During the extraction phase, data is found in many different places or sources and pulled into a single repository.

The data that is taken from the source location is often raw and can't be used as is. To get around this problem, the data must be changed. In the ETL process, this is the step that gives your data the most value by letting it be mined for business intelligence. During transformation, many steps are taken to change it into the format that is wanted. Sometimes, data needs to be cleaned up first before it can be changed. Data cleansing gets the data ready to be changed by fixing mistakes or filling in missing values. After the data has been cleaned, the next steps in the process of transformation are:

• Data Discovery: The first step in the process of changing data is to find and understand the data in its original format. Most of the time, this is done with the help of a tool for data profiling. This step helps you figure out what needs to be done to the data to get it in the format you want.

• Data Mapping: During this phase, plans are made for how the transformation will happen.

• Coming Up With Code: To finish the process of transformation, a code must be made to run the job of transformation. Often, these codes are made with the help of a tool or platform for transforming data.

• Putting The Code To Use: The planned and coded data transformation process is now put into action, and the data is changed into the desired output.

• Review: Data that has been changed is checked to make sure it has the right format.

Benefits

• Getting The Most Out Of Data: Companies can use data transformation tools to standardise data and make it more accessible and usable.

• Organizing And Understanding Data More Effectively: As data is created from a growing number of sources, metadata inconsistencies can make it difficult to organise and interpret data. Data transformation refines information to make your data set easier to manage and interpret.

• Executing Queries More Quickly: Transformed data is standardised and saved at a source place from which it may be retrieved quickly and simply.

• Improving Data Quality: Due to the risks and costs of obtaining business intelligence with faulty data, data quality is becoming a major concern for enterprises. Inconsistencies and missing values can be reduced or eliminated through the data transformation process.

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