Data Transformation Defined

The ever-increasing volume of data offers your business limitless opportunities to make better decisions and improve results. But how can you take what you know about your business, customers, and competitors and make it more accessible to everyone in your enterprise? The answer is data transformation.

Data Transformation Defined

Data transformation is the process of converting data from one format to another, typically from the format of a source system into the required format of a destination system. Data transformation is a component of most data integration and data management tasks, such as data wrangling and data warehousing.

One step in the ELT/ETL process, data transformation may be described as either “simple” or “complex,” depending on the kinds of changes that must occur to the data before it is delivered to its target destination. The data transformation process can be automated, handled manually, or completed using a combination of the two.

Today, the reality of big data means that data transformation is more important for businesses than ever before. An ever-increasing number of programs, applications, and devices continually produce massive volumes of data. And with so much disparate data streaming in from a variety of sources, data compatibility is always at risk. That’s where the data transformation process comes in: it allows companies and organizations to convert data from any source into a format that can be integrated, stored, analyzed, and ultimately mined for actionable business intelligence.

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How Data Transformation Works

The goal of the data transformation process is to extract data from a source, convert it into a usable format, and deliver it to a destination. This entire process is known as ETL (Extract, Load, Transform). During the extraction phase, data is identified and pulled from many different locations or sources into a single repository.

Data extracted from the source location is often raw and not usable in its original form. To overcome this obstacle, the data must be transformed. This is the step in the ETL process that adds the most value to your data by enabling it to be mined for business intelligence. During transformation, a number of steps are taken to convert it into the desired format. In some cases, data must first be cleansed before it can be transformed. Data cleansing prepares the data for transformation by resolving inconsistencies or missing values. Once the data is cleansed, the following steps in the transformation process occur:

  1. Data discovery. The first step in the data transformation process consists of identifying and understanding the data in its source format. This is usually accomplished with the help of a data profiling tool. This step helps you decide what needs to happen to the data in order to get it into the desired format.
  2. Data mapping. During this phase, the actual transformation process is planned.
  3. Generating code. In order for the transformation process to be completed, a code must be created to run the transformation job. Often these codes are generated with the help of a data transformation tool or platform.
  4. Executing the code. The data transformation process that has been planned and coded is now put into motion, and the data is converted to the desired output.
  5. Review. Transformed data is checked to make sure it has been formatted correctly.

In addition to these basic steps, other customized operations may occur. For example,

  • Filtering (e.g. Selecting only certain columns to load).
  • Enriching (e.g. Full name to First Name , Middle Name , Last Name).
  • Splitting a column into multiple columns and vice versa.
  • Joining together data from multiple sources.
  • Removing duplicate data.

After it has been transformed, the data is ready to be loaded into its target destination so it can be put to work.

Finally, it’s important to note that not all data needs to be transformed. In some cases, data from the source will already be in a usable format. This is referred to as “direct move” or “pass-through” data.

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Benefits of Data Transformation

Whether it’s information about customer behaviors, internal processes, supply chains, or even the weather, businesses and organizations across all industries understand that data has the potential to increase efficiencies and generate revenue. The challenge here is to make sure that all the data that’s being collected can be used. By using a data transformation process, companies are able to reap massive benefits from their data, including:

  • Getting maximum value from data: Forrester reports that between 60 percent and 73 percent of all data is never analyzed for business intelligence. Data transformation tools allow companies to standardize data to improve accessibility and usability.
  • Managing data more effectively: With data being generated from an increasing number of sources, inconsistencies in metadata can make it a challenge to organize and understand data. Data transformation refines metadata to make it easier to organize and understand what’s in your data set.
  • Performing faster queries: Transformed data is standardized and stored in a source location, where it can be quickly and easily retrieved.
  • Enhancing data quality: Data quality is becoming a major concern for organizations due to the risks and costs of using bad data to obtain business intelligence. The process of transforming data can reduce or eliminate quality issues like inconsistencies and missing values.

Data Transformation in Action

Companies and organizations in every industry have data transformation needs. Whether it’s an e-commerce business that has to manage millions of transactions in hundreds of countries, or a nonprofit that needs to combine donor data from many different sources, data transformation tools remove obstacles to productivity and provide deep-level insights into the data they’ve invested in.

  • RingCentral provides cloud-based telecommunication, messaging, and collaboration solutions to small businesses and enterprise customers. With over 100 different systems in use, streamlining and standardizing data processes is critical for their success. By using a data integration solution including ETL, RingCentral has automated key HR processes so that their employees can spend more time on strategy and less time on administrative tasks.
  • Nonprofit Save the Children UK protects and saves lives by preparing for and responding to natural disasters and humanitarian crises. In order to fulfil their goals, the organization must effectively manage huge volumes of data related to donors, volunteers, and compliance initiatives. By employing a data management platform, Save the Children can integrate data from multiple CRM sources to create unified databases that enable them to find the information they need quickly.
  • Johnson Controls, a global technology and manufacturing company, relies on 200 ERP and CRM systems to manage their international operations. And with 120,000 employees, and customers in more than 150 countries around the world, having fast access to actionable data is a non-negotiable. Johnson Controls uses a comprehensive data management platform to consolidate and streamline data processes across their operation.

Data Transformation Tools

It’s tempting to use hand coding to accomplish data transformation functions, but it is often more cost-effective and efficient to use a data transformation tool or platform. Hand coding increases opportunities for errors and is not easily replicable. Codes must be often be rewritten each time the process takes place. As a result, the costs of hand-coding are often much higher than the costs of implementing an ETL tool.

ETL tools offer additional benefits beyond cost savings. They can generate visual representations of a data flow to make them easier to understand, and ETL tools often incorporate parallelization, monitoring, and failover features. Finally, custom code inhibits scaling and innovation because the skills needed to work with custom-coded integrations are hard to find. Any upfront savings achieved by hand coding is typically canceled out by the vast increase in maintenance costs and an inability to scale.

When considering options for data transformation, it’s also important to realize that today’s hybrid data processing environments are much more complex than in the past. Conventional servers are linked to big data analytics platforms, and more data lives both on-site and in the cloud. There’s also a greater reliance on a growing number of “as-a-service” offerings to manage a wide range of data assets. ETL tools often include the connectors needed to migrate data from these various sources.

Finally, ETL tools are designed to optimize each stage of the ETL process, which reduces the amount of time it takes to turn raw data into business insights.

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Ready, Set, Transform!

Data transformation allows organizations to turn data from various locations and formats into actionable insights. It accomplishes this by streamlining the processes which refine, standardize, and consolidate these many types of data.

Talend Open Studio for Data Integration provides a single platform to extract, transform, and load your data, no matter the format or where it’s stored. Graphical drag-and-drop tools and a range of components and connectors make it easy to get your ETL/ELT jobs up and running quickly. Download it today.

| Last Updated: August 29th, 2019