Understanding the Basics of Data Transformation


What is Data Transformation?

Data transformation is the process of converting data that exists in one format or state into a format or state that is useful for another purpose. This can include validating data for quality, restructuring it, or applying statistical data transformation. Examples include validating acceptable date ranges, adding geographic segment for location accuracy, or applying mathematical functions for machine learning or data science.

The purpose of data transformation is to make data useable, manageable, and compliant with prevailing data governance standards. Once data is transformed, you can use analytics to gain trustworthy and actionable business intelligence from it.

Data transformation is the middle action taken in an “extract, transform, and load” (ETL) process of preparing data for analysis. Modern enterprises extract data from a variety of sources such as customer transactions, files, databases, or streaming data from machines and sensors before transforming and loading the data into a cloud data repository.

Data 4.0 is the Soul of Data Transformation

5 Key Capabilities of Data Transformation

In modern businesses, data transformation takes place in the cloud as part of the overall data integration process. It can be performed on a dedicated cloud data integration environment or inside the cloud data warehouse. A dedicated environment provides scalability and high availability while also reducing the workload on the data warehouse during peak hours. On the other hand, performing data transformation inside the data warehouse may be necessary because the data transformation relies on data already in the database, therefore reducing unnecessary data movement.

Many commercial data integration solutions include data transformation capabilities to minimize the effort needed to transform data. Here are the five capabilities you should look for when building a successful data transformation program.

Simple but powerful data discovery and cataloging 

The first step in data transformation is identifying your data. This is important because competing in today’s markets requires a comprehensive and up-to-the-minute view of everything about your business. Yet data volumes are growing exponentially and come from an increasing number of diverse sources: structured and unstructured, streaming and telemetry, applications, websites, mobile devices, customers, partners, Internet of Things (IoT), and more. This makes discovering data challenging. 

Leading data transformation solutions have data catalogs that use artificial intelligence (AI) to automatically scan, find, and catalog all data across the enterprise and index it for discovery using simple Google-like searches.

Automatic connectivity to data sources

The second step in data transformation is to connect to the many data sources you’ve identified, so you can extract their contents and load it into your cloud data repository. Leading data transformation solutions offer out-of-the-box connectors that seamlessly connect to and integrate from both on-premises and cloud data sources. By accessing data where it's stored and delivering it where and when it's needed, businesses can act swiftly and agilely to seize opportunities, maximizing resources while deriving the most value from their data.

Easy-to-use, automatic mapping transformations without hand-coding

Extracting the data is next. Data mapping is used to define how the source data is modified before it reaches its destination. Mapping specifies the precise transformation rules by which individual fields are changed, joined, filtered, and aggregated. For example, an employee name in a source system, “Jane Smith,” may be changed to be “Smith Jane” in the target system. Because businesses today possess vastly more data sources and types, it’s important to have a codeless point-and-click mapping interface as a part of the overall data transformation solution.

Prebuilt data-quality transformations

With data transformation, data quality is always a requirement. Without clean, trusted data, enterprises cannot make good decisions—tactical or strategic—when attempting to succeed in today’s fast-moving markets. Leading data transformation solutions will have prebuilt data quality transformations that accelerate results by ensuring that only high-quality data will reach the end user—whether human or machine. These prebuilt business rules and accelerators allow you to reuse common data quality rules across any data from any source to save time and resources and can manage the quality of both multi-cloud and on-premises data.

Capability to operationalize and process data at petabyte scale

The final key aspect of data transformation is being able to do it at enterprise scale—and in today’s world, that means petabyte scale. Most organizations use cloud data warehouses, which can scale compute and storage resources with latency measured in seconds. Leading data transformation solutions also perform “pushdown” optimization, which pushes the transformation logic to the source database. The logic is then translated into SQL queries, which execute instructions such as filter, join, aggregate, or sort at the query level. This makes high-performance processing at scale possible.


How Data Transformation Drives Digital Transformation

Most people intuitively comprehend that better data will result in better business outcomes. The reverse is also true. A recent survey by Experian found that an astonishing 95% of organizations said poor data quality was undermining their business performance.

And digital transformation has put data at the center of every organization. 

How? The explosion in data over the past few decades—coupled with the dramatic fall in storage and processing costs, and the increasing regulatory focus on data quality, policy, and governance—has prompted enterprises to reshape their business models. The goal: to harness the immense potential of the data that is now a core—sometimes the core—business asset they possess.

Successful data transformation means that businesses can extract data of all kinds—from the cloud, mobile, streaming, IoT, social data, and more—and leverage it for the good of the business. They can drive better decision-making at all levels by feeding high-quality, transformed data into different applications. They can streamline operations with machine-to-machine communications that are free of potential landmines due to dirty data. In fact, a total reimagining of business in the digital age revolves around data transformation. And what is that, but digital transformation?

Alternatively, think of what happens if you don’t do data management correctly. The business landscape is littered with corpses of previously successful companies that were blindsided by data-driven disruption. Virtually every industry has been shaken to its core by new players who use data in innovative ways.

Today, data that has been transformed from its original state to one that is usable in other ways is improving business processes across the entire enterprise. This allows you to:

  • Deliver greatly improved customer experiences
  • Make better and more accurate decisions, more swiftly
  • Streamline operational processes to drive cost savings
  • Seize new revenue opportunities

Most importantly, data transformation determines whether digital transformation initiatives powered by the emerging innovations now being deployed—from AI to machine learning, to IoT, big data, and more—are successful. In that way, data transformation is digital transformation. 


What are the benefits of data transformation?

Data transformation ensures that data that enters your enterprise is usable and manageable. It facilitates cost-efficient storage, ease of analysis for greater business intelligence, and operational efficiency.  On the flip side, storing data that has not been transformed wastes resources and creates the possibility of compliance risk because the data cannot be managed under the organization’s data governance rules.

Customer Success Stories

Data transformation is helping many organizations achieve dramatic business success with their data transformation efforts. Here are a few examples:

  • Avis Budget Group optimized vehicle rental services using real-time data. By extracting and transforming data from a massive connected fleet of 650,000 vehicles in real-time, Avis Budget Group was able to enhance efficiency, reduce costs, and drive additional revenue.
  • Equinix, a cloud-based IT services provider, had data duplicated and siloed through multiple data warehouses. Equinix cleaned and transformed the data, enabling it to roll out new solutions to customers faster, as well as prepare it to deploy new initiatives based on AI and machine learning.
  • Lagardère Travel Retail, a global leader in the travel retail industry, overcame a reliance on internally developed, hand-coded data transformation efforts and manual processes. It did this by deploying an automated data transformation platform that enabled it to achieve significantly faster data sharing between a large number of diverse systems.


Learn More About Data Transformation

Over time, businesses, markets, and technologies evolve and change. The one constant for providing a sustainable competitive advantage is data. By unleashing the power of data in new and intelligent ways, you can accelerate data-driven digital transformation, outmaneuvering other market players, and reap substantial benefits for your business.

Informatica helps organizations accelerate their data transformation journeys so they become next-generation intelligent enterprises—and are themselves capable of disrupting the markets they compete in.

Want more information on data transformation and how Informatica can help? Start with these resources: