A quick guide to doing data transformation the right way

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Our world has become increasingly data driven. Organizations of all sizes are incorporating increasing amounts of data every day, and making the most of it is vital to unlock new possibilities.

However, the data transformation process is not simple due to the large amount of raw data. It may surprise you to learn that approximately 2.5 quintillion bytes of data are generated worldwide every day. 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 optimized for your specific business goals, making it usable for your business.

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

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

Data transformation can be done manually or automatically using a data transformation tool, and you 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 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 doesn’t provide much value. Raw data alone makes it difficult to make decisions or take action. A human being or a machine can make use of the data when it is transformed into a format that can be understood. During this process, algorithms and rules are applied to the data to obtain information and patterns that can be used.

According to Gartner research, companies have suffered losses totaling nearly $15 billion each year due to poor data quality. Data quality issues will worsen for companies that have a large number of business divisions and operations across a wide geographic region, as well as many employees, customers, vendors, and products to manage.

Business cases that require data transformation

For any business to be successful, data transformation must take place regardless of size and the industry in which they operate. However, we have outlined some examples of data transformation applications that can provide the most benefit to a business:

electronic commerce

The e-commerce business produces a large amount of data every day, and the success of the business largely depends on how the company collects valuable information from it. Therefore, the importance of data transformation is inevitable for e-commerce companies.

Banking

The banking sector is also highly dependent on data. From customer information to creating a personalized offer for customers, banks used to consume vast amounts of data. Data transformation can help banking institutes generate valuable insights from raw data.

Health care

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

Financial

Financial institutions receive information about their customers from a variety of sources. This customer information cannot be used directly to conduct business. Therefore, data transformation is a must to convert 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 data quality can be not only costly, but also useless. A company must be able to extract and transform data into useful information so that it can remain agile and adaptable.

Below we’ve outlined some of the benefits of data transformation services for your business.

Improved data quality

Various problems can arise as a result of incorrect data. When you transform your data, you can give your organization the opportunity to eliminate quality issues and reduce the chance of misinterpretation to ensure your business runs smoothly.

risk reduction

When you use inconsistent and discrepant data, you put your reputational and financial interests at risk. Data standardization and quality are crucial to reduce these risks.

Have more business intelligence and analytical data available

Most companies don’t analyze their data to get business intelligence for their businesses. Data transformation tools are very 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 from a variety of sources is integrated, there is an increasing challenge in terms of metadata consistency. Data transformation will help you improve your metadata and understand your data set more accurately.

data visualization

Among the various steps involved in the data transformation process, data visualization is one of the most important. Analyzing data with precision and insight becomes easier when noise is reduced and 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 in the source files. Analysis of the source data requires consideration of data quality, quality attributes, and the structure of the source data. With this method, better data analysis can be performed and valuable business intelligence can be generated.

data mapping

As part of this process, analysts define what criteria are needed to modify, join, filter, join, and add 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

One step in the migration process involves the movement of data from a source system to a destination system. Data can be retrieved from structured sources (eg, databases) or unstructured sources (eg, 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 is collected and converted into a format that businesses can use to manage their data efficiently.

Data review

Once the data has been transformed, you’ll need to check the data again to make sure the transformation was accurate. The review process can be compared to the quality assurance process.

What are the different methods of data transformation?

There are several data transformation methods available to extract valuable insights from data:

Manual data transformation

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

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

Data transformation with on-site ETL tools

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

Data transformation can be very expensive when using on-premises 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 cloud servers, which means they are more cost effective than local ETL methods.

The best data transformation tools to ease your journey

There are two types of data transformation tools available on the market to help your business dig deeper into data and extract valuable insights from it.

Script tools

These are the common types of data transformation tools that work with programming languages ​​like SQL or Python. This type of transformation is usually done within a repository and is executed by a system that orchestrates all the transformations to complete.

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

Low-code/no-code tools

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

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





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James D. Brown
James D. Brown
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