A Quick Guide For Doing Data Transformation The Right Way

Neeraj Agarwal -
A Quick Guide For Doing Data Transformation The Right Way
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Our world has become increasingly data-driven. Organizations of all sizes are ingesting increasing amounts of data every day, and it is vital to take full advantage of it to unlock new possibilities.

However, the process of data transformation is not simple due to the vast amount of raw data. It might surprise you to learn that every day, approximately 2.5 quintillion bytes of data are generated worldwide. Another problem is that most of the raw data is irrelevant to your business.

What is Data Transformation?

Generally speaking, data transformation is a process by which raw data is transformed into a format that is optimized for your specific business objectives, therefore making it usable for your business.

The raw data of your business can give you a lot of insight into your business, customers, and your competitors which is necessary for businesses to make informed decisions. However, when data is presented in its raw form, it cannot be trusted. The data contained therein is both irrelevant and relevant at the same time. Also, there could be errors or missing values in the data. Duplicate data can be found sometimes.

During the data transformation process, raw data is extracted, cleaned, and transformed into a format that is suitable for integration, analysis, storage, and many other processes.

Data transformation can either be done manually or automatically using a data transformation tool and can change the format, structure, content, or context of the data to make it more useful.

“During the data transformation process, raw data is extracted, cleaned, and transformed into a format that is suitable for integration, analysis, storage, and many other processes.”

-Neeraj Agarwal

Why is Data Transformation Necessary for my Business?

Businesses need to transform data for two reasons: first, to turn it into useful information, and second, to turn it into actionable information.

Raw data does not provide much value. Raw data alone makes it difficult to make decisions or take action. A human or machine can make use of data when it is transformed into a format it can understand. During this process, algorithms and rules are applied to the data to derive insights and patterns that can be used.

According to Gartner research, companies have suffered losses totaling nearly $15 billion every year due to poor data quality. Data quality issues are bound to get worse for companies that have a large number of business divisions, and operations across a wide geographical region, as well as many employees, customers, suppliers, and products that need to be managed.

Business Cases That Require Data Transformation

For any business to succeed, data transformation must be carried out regardless of the size and sector in which they operate. However, we have outlined a few examples of applications for data transformation that can provide the greatest benefit to a business:


E-commerce business produces a lot of data every day, and the success of the business greatly depends on how the business gathers valuable insights from it. Therefore the importance of data transformation is inevitable for eCommerce businesses.


The banking sector also greatly depends on the data. Starting from customer information to creating a personalized offer to customers, banks used to consume a vast amount of data. Data transformation can help banking institutes to generate valuable insights from the raw data.


Among all of the industries that are experiencing digital transformation, healthcare is at the forefront. Thousands of smart hospitals and medical facilities are incorporating artificial intelligence into how they identify possible illnesses and operate.


Financial institutions receive information about their customers from a variety of sources. This customer information can’t be utilized directly to bring business. Therefore data transformation is a must to convert the data from raw format to meaningful information.

How Will Data Transformation Benefit My Business?

A data analytics solution is not complete without a data transformation. Poor quality of data can not only be expensive, but it can also be useless. A business needs to be capable of extracting and transforming data into useful information so that it can remain agile and adaptable.

Below, we have outlined some of the benefits of data transformation services for your business.

Improved Data Quality

Several problems can arise as a result of bad data. When you transform your data, you can provide your organization with the opportunity to eliminate quality issues and reduce the possibility of misinterpretations to ensure your business runs smoothly.

Reducing Risks

When using inconsistent, discrepant data, you put your financial and reputational interests at risk. Standardizing and quality data are crucial to reducing these risks.

Having More Business Intelligence and Analytical Data Available

The majority of companies are not analyzing their data to gain business intelligence for their business.  Data transformation tools are highly effective in improving the accessibility of your company’s data, standardizing it, and using it in the context of intelligence.

Effective Data Management

When data is being integrated from a variety of sources, there is an increasing challenge in terms of metadata consistency. The transformation of data will assist you in improving your metadata as well as understanding the data set more accurately.

Data Visualization

Among the various steps that are involved in the data transformation process, data visualization is one of the most important. Analyzing data accurately and insightfully becomes easier when noise is reduced and the data structure is improved.

What are the Steps Involved in the Data Transformation Process?

Data Transformation
Algoscale Technologies

There are several steps involved in the data transformation process as mentioned below:

Data Discovery

To transform data, we must first identify and understand the information contained within the source files. Analysis of source data requires consideration of data quality, quality attributes, and the structure of the source data. Using this method, better data analysis can be done and valuable business intelligence can be generated.

Data Mapping

As part of this process, analysts define what criteria are needed to modify, match, filter, join, and aggregate individual fields within the set of data sources. Mapping involves extracting business value from multiple external and internal sources, unifying, and then transforming the data into an analytical and operational format.

Data Extraction

A step of the migration process involves the move of data from a source system to a target system. Data may be retrieved from either structured sources (e.g. databases) or unstructured sources (e.g. event streams, log files) of data.

Transform Data

This is the last step in the data transformation process. There are multiple sources of structured or unstructured data that are collected and converted into a format that can be used by businesses to efficiently manage their data.

Data Review

Once the data has been transformed, you will need to check the data again to ensure that the transformation has been accurate. The process of reviewing can be likened to the process of quality assurance.

What are the Different Data Transformation Methods?

There are several data transformation methods available to gain valuable insight from the data:

Manual Data Transformation

The next step involves writing a small piece of code manually to implement the transformation of the data.  R, Python, and SQL are a few of the most popular programming languages that can be used to perform manual data transformation.

Manual data transformation methods take time and effort to manually transform data. Additionally, the process requires significant amounts of time to manually code transforms, test transforms, and maintain transform codes.

Data Transformation With On-Site ETL Tools

ETL refers to the extraction, transformation, and load. It primarily involves extracting data from one or more sources, transforming it into a consistent format, and then loading it to the desired destination.

Data transformation can be very expensive when using on-premise ETL tools, and as a result, companies are now moving to cloud-based ETL methods to perform their data transformations.

Data Transformation With Cloud-Based ETL Tools

Another highly effective data transformation method is cloud-based ETL tools. With the help of these instruments, organizations can process large volumes of data from a variety of different sources in an efficient and timely manner.

The name implies that these tools work through the cloud servers, which means they are more cost-effective than on-premise ETL methods.

The Best Data Transformation Tools to Ease Your Journey

There are two types of data transformation tools available in the market to help your business dig deep into the data and extract valuable information from it.

Scripting Tools

These are the common types of data transformation tools that work using the programming languages such as SQL or Python. This type of transformation is usually performed within a repository and executed by a system that orchestrates all the transformations in order to complete them.

These tools require technical expertise in SQL and Python to make the most out of the business data.

Low/No-Code Tools

These are the easiest types of data transformation tools. With this tool, businesses can load data into the data warehouse from multiple sources using a simple and intuitive interface that makes managing data easy.

There is a great benefit to these tools, in that they don’t require any technical expertise to demonstrate their ability to generate valuable insights from the data.

Neeraj Agarwal - Founder, Algoscale Technologies

Guest Writer
Guest Writer
Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All.
Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All.