Data Transformation: Types, Process, Benefits & Definition

What is data transformation? What are the benefits of data transformation? What are the processes? Learn all this and more!

Overview of the data transformation process and data transformation definition

Today’s businesses have more tools for collecting data than ever before. The never-ending amount of data gives businesses more opportunities to make more informed decisions.

But, raw data can be challenging to work with. To make data usable across different systems, many businesses take advantage of data transformation.

What is data transformation? What are the benefits of it? Read on to learn all that and more regarding data transformations.

Types, Process, Benefits, Definition, and Methods of Data Transformation

What is Data Transformation?

Data transformation is the process in which data gets converted from one format to another. The most common data transformation process involves collecting raw data and converting it into clean, usable data.

Data transformation increases the efficiency of business and analytic processes, and it enables businesses to make better data-driven decisions. During the data transformation process, an analyst will determine the structure of the data. This could mean that data transformation may be:

  • Constructive: The data transformation process adds, copies, or replicates data.
  • Destructive: The system deletes fields or records.
  • Aesthetic: The transformation standardizes the data to meet requirements or parameters.
  • Structural: The database is reorganized by renaming, moving, or combining columns.

They’ll also perform data mapping and extract the data from its original source before executing the transformation. Finally, they’ll store the transformed data within the appropriate database technology.

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The Data Transformation Process

When data is extracted from its local source, it’s typically raw and unusable. To overcome this issue, the data needs to be transformed.

The entire process for transforming data is known as ETL, which stands for Extract, Load, and Transform. Through the ETL process, analysts can convert data to its desired format. Here are the steps involved in the data transformation process:

  1. Data Discovery: During the first stage, analysts work to understand and identify data in its source format. To do this, they’ll use data profiling tools. This step helps analysts decide what they need to do to get data into its desired format.
  2. Data Mapping: During this phase, analysts perform data mapping to determine how individual fields are modified, mapped, filtered, joined, and aggregated. Data mapping is essential to many data processes, and one misstep can lead to incorrect analysis and ripple through your entire organization.
  3. Data Extraction: During this phase, analysts extract the data from its original source. These may include structured sources such as databases or streaming sources such as customer log files from web applications.
  4. Code Generation and Execution: Once the data has been extracted, analysts need to create a code to complete the transformation. Often, analysts generate codes with the help of data transformation tools or platforms.
  5. Review: After transforming the data, analysts need to check it to ensure everything has been formatted correctly.
  6. Sending: The final step involves sending the data to its target destination. The target might be a data warehouse or a database that handles both structured and unstructured data.

In addition to these necessary steps, other customized operations may take place along the way. For example, analysts may filter the data by only selecting certain columns to load. Or, they may enrich the data by adding names, locations, etc. Analysts may also remove duplicate data and join data together from multiple sources.

Want to automate your ETL processes to enable all of your data to flow into a single destination? Learn more about Zuar’s Mitto platform that allows you to transform, model, report, and manage your data more effectively.

data discovery and mapping

Data Transformation Types

There are several different types of data transformation. These include:


Data transformation through scripting involves using Python or SQL to write the code to extract and transform data.

Python and SQL are scripting languages that allow you to automate certain tasks in a program. They also allow you to extract information from data sets. Scripting languages require less code than traditional programming languages and are therefore less intensive.

You can also update multiple jobs utilizing the Python Mitto SDK, enabling your business to interact remotely with schedules, jobs, and other business functions. Want to see Mitto in action? Schedule a demo of the Python Mitto SDK.

On-Premises ETL Tools

As mentioned, ETL tools allow you to extract, transform, and load data. ETL tools take the painstaking work it requires to script the data transformation by automating the process. On-premises ETL tools are hosted on company servers. While these tools can help save you time, using them often requires extensive expertise and significant infrastructure costs.

Cloud-Based ETL Tools

As the name suggests, cloud-based ETL tools are hosted in the cloud. These tools are often easiest for non-technical users to utilize. They allow you to collect data from any cloud source and load it into your data warehouse.

With cloud-based ETL tools, you can decide how often you want to pull data from your source, and you can monitor your usage. Zuar's Mitto is an example of a product that has ETL/ELT capabilities, but also helps you manage the data both earlier and further along in its journey. Mitto can be hosted either on-premise or in the cloud.

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Data Transformation Techniques

There are several data transformation techniques that can help structure and clean up the data before analysis or storage in a data warehouse. Here are some of the more common methods:

  • Data Smoothing: This is the data transformation process of removing distorted or meaningless data from the dataset. It also detects minor modifications to the data to identify specific patterns or trends.
  • Data Aggregation: Data aggregation collects raw data from multiple sources and stores it in a single format for accurate analysis and reports. This technique is necessary when your business collects high volumes of data.
  • Discretization: This data transformation technique creates interval labels in continuous data to improve efficiency and easier analysis. The process utilizes decision tree algorithms to transform a large dataset into compact categorical data.
  • Generalization: Utilizing concept hierarchies, generalization transformslow-level data attributes into high-level data attributes to create a clear data snapshot.
  • Attribute Construction: This technique allows a dataset to be organized by creating new attributes from an existing set of attributes.

Normalization: Normalization transforms the data so that the attributes stay within a specified range for more efficient extraction and data mining algorithm application.

data smoothing and data aggregation

Data Transformation: Benefits

Transforming data can help businesses in a variety of ways. Here are some of the biggest benefits of data transformation:

  1. Better Organization: Transformed data is easier for both humans and computers to use.
  2. Improved Data Quality: There are many risks and costs associated with bad data. Data transformation can help your organization eliminate quality issues such as missing values and other inconsistencies.
  3. Perform Faster Queries: You can quickly and easily retrieve transformed data thanks to it being stored and standardized in a source location.
  4. Better Data Management: Businesses are constantly generating data from more and more sources. If there are inconsistencies in the metadata, it can make it more of a challenge to organize and understand it. Data transformation refines your metadata, so it’s easier to organize and understand.
  5. More Use Out of Data: While businesses may be collecting data constantly, a lot of that data sits around unanalyzed. Transformation makes it easier to get the most out of your data by standardizing it and making it more usable.

While these methods of data transformation comes with a lot of benefits, it’s also important to understand that there are a few drawbacks. Data transformation can be expensive and resource-intensive.

Also, if you’re not working with experienced data analysts with the right subject matter expertise, problems may occur during the data transformation process. Overall though, the benefits of data transformation outweigh the drawbacks.

improving data quality for faster queries

Data Transformation: Wrap Up

If your organization isn’t taking advantage of data transformation, you’re constantly going to be a step behind your competitors.

Organizing, transforming, and structuring data can be an overwhelming task for many organizations. Before you start looking at your data, you need to have a strategy in place so you can understand where you want your data to take your business. Zaur offers several products and services that can enable more efficient and accurate data management and analysis by automating your data transformation. And don't forget to check out Mitto to spearhead your data transformation!

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