What is Data Transformation and How It Optimizes Business Processes

By |2022-04-19T11:17:10+00:00April 28th, 2020|

The IDC predicts that by 2020, more data will be stored on the public cloud and enterprise systems than on consumer devices. The data on these platforms can be in heterogeneous formats and structures. Thus, businesses will have to manage, enrich, and manipulate data before loading it into another system and performing data analytics. This is where data transformation comes into play. To get the most of your data it is important to use the right transformation techniques for the best possible results.

business intelligence

A chart showing how much data is stored on consumer devices, enterprise systems, and the public cloud

Data transformation tools can help businesses resolve compatibility issues and improve data consistency. This can be done through performing different transformation functions, such as aggregations, sorting, data cleaning, etc. to convert data in a format that suits the requirements of the destination system. The result is optimized, integrated data that can further be processed to draw insights, generate reports, and make data-driven decisions.

Let’s begin by understanding what is the data transformation process.

What is Data Transformation?

Data transformation is the process of converting data from one format into another format that is more usable by the target system or application. It includes multiple activities: you may ‘transform’ your data by filtering it based on certain rules and joining different fields to get a consolidated view. Transformation tools help achieve your end result with ease.

Transformation is an important middle step in the extract, transform, and load (ETL) process – a prerequisite to loading. Most ETL tools also come with predefined functions that can be used to transform your data quickly and efficiently. Companies often face transformation challenges due to poor quality data.

After discussing what transformation is, we can now understand some of its important steps:

  • Identify the structure of the source files
  • Extract data from the file source
  • Next, map the data from the source file to the transformation tool
  • Perform the transformation i.e. filter, sort, cleanse or aggregate the data
  • Finally, send the transformed file to the destination

Why is Data Transformation Important?

Businesses need to transform high volumes of data for several reasons, such as migrating data, consolidating records, deleting duplicates, and changing formatting, etc. Transformations are also applied to concatenate and validate data, perform lookups, or route data to different destinations. It is beneficial to have a data transformation tool with a wide array of transformation options to be able to manipulate data in the best possible way.

Let’s look at a transformation example: suppose a bank acquires an insurance firm operating in the same region. Once the acquisition is complete, it is decided that a single payroll will be generated for all the employees. The payroll generation process would have been straightforward if all the employee data was stored in a unified system, such as a data warehouse or database. However, in this case, one company stored employee data on an SQL Server and the other stored their payroll information in an Excel sheet. To create a consolidated payroll for the employees, the data needs to be transformed to fit the requirements of the target system i.e. Excel file.

Data transformation steps

Data from SQL Server and Excel source being transformed and mapped on to an Excel destination file

Here’s how the destination file looks after the transformation:

 Data transformation output

Preview of the consolidated payroll stored in the Excel file

Transformations can also be used to extract values from different data types. Instead of overwhelming your systems with multiple – often unnecessary – records, you can use different types of data transformations to filter out irrelevant data.

For instance, if you want to generate a report of all the sales conducted in a particular country, say the USA, applying the Filter transformation will prevent the destination system from being strained unnecessarily since only relevant records will be passed through. Storing relevant and comparatively fewer records in the destination system means less memory consumption during data pipeline processing, which will reduce execution time.

On-Premise, Cloud-Based, and Manual Data Transformation: Which One Should You Choose?

The data transformation capability is available in various data integration tools. A good thing about transforming data is that there are several approaches to go about it. Each approach, however, comes with its unique benefits and challenges. Let’s look at some of the common transformation techniques.

On-Premise Data Transformation

On-premise transformation allows businesses to extract, transform, and load crucial data very quickly. Having an on-premise data tool also translates to increased regulatory compliance and better security management.

The majority of these transformation tools come with drag-and-drop functionality, which means that non-technical employees will be able to transform data and convert it into a usable format.

One important thing to note is that on-premise tools are deployed on-site; therefore, you must ensure that you have enough resources in terms of storage capacity and the suitable systems to run these types of software.

Cloud-Based Data Transformation 

The pay-as-you-go feature of most cloud-based data transformation tools gives businesses the freedom to scale up and down as and when necessary. It is one of the reasons why cloud-based tools are popular. However, having crucial enterprise data saved on a third-party cloud-based server comes with its share of security concerns.
One benefit of transforming data on the cloud is that installation and configuration times are reduced significantly, which means that businesses can transform their data without too many delays.

Manual Data Transformation

Manual transformation involves a great deal of coding. Therefore, it requires you to hire a dedicated development resource to get the job done.
Manual transformation of data can take up a lot of time and resources, especially when you are dealing with multiple file formats. Moreover, the risk of human error and accidental deletion of important business data also becomes high when transforming data manually. Hence, it is advised to use an automated data transformation tool instead.

How to Transform Your Data

Data can be transformed in several ways, depending on your goal and destination system requirements. The user must be aware of certain transformation rules and data transformation examples when using the software. Pre-built transformations can not only be used to clean, filter, split, and join data but also to enrich it. Here are some types of data transformations:

Filtering Data

Businesses need to process numerous records to retrieve data that is relevant for a particular scenario. Data can be filtered based on one or more rules. You can then use this transformed data for further processing without making the destination system work on irrelevant records.

Filter transformation

Data from a Fixed Length Source being filtered to display records from the USA

In the data transformation example above, the Filter transformation is applied on a Fixed Length source document to show records only from the USA.

Filter transformation properties

Defining the expression that will be used to filter out data

Routing Data

You can use the Route transformation to direct source data to different paths of similar or different formats based on certain logical rules.

Here’s a data flow to illustrate the feature:

Route transformation

Data from SQL Server being routed on to two Excel destinations

The Route transformation here is used to send the data retrieved from SQL Server to two different Excel destination files. Conditions on the ShipVia field determine which Excel file the data will be routed to.

Router trasform Properties

Rules of the Route transformation in the Router Transformation Properties screen

The conditions here can be simple or complex, depending on your requirements.

Sorting Data

Large businesses often need to sort their data to make it more manageable. The Sort transformation can be applied to any field to arrange the output in ascending or descending order.

Here’s an example in which the Sort transformation is used to display Customer ID records in descending order:


Preview of customer data stored in SQL Server database is sorted in descending order of CustomerID

This was done by selecting the CustomerID field and selecting the Sort Order as descending as follows.

Sort Transformation Properties

Properties of the Sort transformation

The Return Distinct Values Only box could also be checked to remove redundancies and display unique records.

Benefits of Data Transformation

Transformation tools, when used correctly, can improve data quality significantly and improve process efficiency. Transformed data is easier to use, trustworthy, and compatible with end systems and applications. High-quality transformed data ensures that the destination system only has data with the required format and structure.

Here are some other benefits:

  • They can help businesses reap the maximum value from their data.
  • Standardizing data through transformations can improve data management.
  • Transformed data can be utilized by various tools for different applications, such as visualizations, reporting, analysis, etc

Astera Centerprise: The Easy Way to Transform Data

Astera Centerprise is a complete data integration platform that allows users to extract, map, transform, and load their data in a code-free environment. In addition, it also includes functionality for other types of data transformation such as data-profiling capabilities, allowing users to get a holistic view of their data. You can also profile data to compare its pre and post-transformed statistical overview.

Data profiling in Astera Centerprise

Data profile of the Customers table used for Sorting

Want to learn more about using Astera Centerprise as a data transformation software? Get in touch with us to schedule a demo, and find out how our pre-built transformations can help manage data.