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.
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.
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:
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.
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.
Data transformation is helping many organizations achieve dramatic business success with their data transformation efforts. Here are a few examples:
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:
Data 4.0: The Soul of Digital Transformation
Enterprise Management Associates Report: AI/ML-Powered Data Management Enables Digital Transformation