Data remains the lifeblood of the business world, and its importance ultimately transcends any specific industry. For example, companies leverage data analytics – powered by machine learning – to glean actionable information to help them compete successfully. However, using data in an effective manner sometimes requires transforming it into different formats.
So data transformation is a critical, but sometimes overlooked, IT process used throughout the modern business landscape. In fact, it sees wide use as the middle step in ETL (extract, transform, load) processing. Organizations rely on it to prepare data for different purposes, like being fed into a machine learning model. So here’s a high-level overview of data transformation, its relevance, and some of its more popular use-cases.
Why is Data Transformation So Important?
Cloud-based Data Typically Requires Transformation
According to a recent study, the percentage of data stored in the Cloud continues to increase. In fact, since 2010, the amount of Cloud-based data grew from around 5 percent to nearly half. These metrics compare the Cloud to both enterprise data stores and personal devices.
This increase remains the most obvious reason for the greater relevance of data transformation throughout the tech world. Before storing it in a Cloud-based repository, data sometimes needs cleaned, enhanced, aggregated, or even reformatted. Ultimately, transforming the data makes it easier to use for a variety of purposes, including reporting or data analytics. Companies generally leverage ETL processing for this reason.
Other Relevant Use-Cases For Data Transformation
Of course, the use-cases for ETL go beyond Cloud storage. As noted earlier, data typically needs to be cleaned and formatted before using it when training machine learning models. Data cleaning also remains critical for a variety of other reasons. Because of this need, it’s not surprising that many ETL tools include data-cleaning capabilities.
Data warehousing provides a popular example of the importance of data transformation. DBAs and developers optimize database designs for high performance when used in transactional business applications. However, these data structures generally perform poorly when used in a data warehouse or reporting scenario.
For this reason, businesses use an ETL process when transferring data from an application to a data warehouse. This process extracts the data from the app database and transforms it into a different structure optimized for reporting. This cleaned and aggregated data is then loaded into the data warehouse for quickly generating informational reports. This example ultimately illustrates the critical nature of data transformation in today’s IT world.
Want to Learn More About Data Transformation?
If your company needs to hire talented data professionals, connect with the experts at Technology Partners. As one of the top technology staffing agencies in St. Louis, we provide exceptional candidates who understand the importance of ETL. Schedule a meeting with us to discuss your current hiring plans.