Emerging Use Cases for Data Transformation

As the amount of information they receive or generate on a daily basis continues to increase at exponential rates, many companies are discovering new ways of capitalizing on the value hidden in their stores of current and historical data. 

The result has been the emergence of a number of innovative use cases that rely on combining and correlating information from a wide variety of internal and external sources. But because each source has its own data structure and formatting conventions, the information it supplies must be converted into a common format so that the entire corporate data pool can be accessed by the various applications and functions that need it. Accomplishing that is what data transformation is all about.

What is data transformation?

Simply put, data transformation is the process of converting data from one format to another. For example, making transaction data from a mainframe accessible to users who are looking to track their purchases in an online banking portal.

In general, data transformation is used to consolidate information from disparate sources into a single pool of compatibly formatted data that can be correlated and analyzed to produce actionable business intelligence or operational directives.

Data transformation use cases

Big data analytics is a classic data transformation use case. Here are some others that are becoming more relevant today.

1. Corporate data integration: 
   • A corporate merger or acquisition often requires that data from distinct database management systems, such as Db2, Oracle, or SQL Server be merged into a single target database.
   • Large enterprises, and many smaller companies as well, have business units scattered throughout the world. To improve operational efficiency and use data to automate processes, data must be collected and integrated from many different sources throughout the organization.
2. E-commerce: Companies engaged in e-commerce will continue to integrate data from a wide variety of internal systems, such as ERP and CRM, with expanding use cases in customer marketing using information from customer-facing systems such as online and social media marketing applications and mobile apps.
3. Cloud migration: Migrating applications and data, including entire databases, to the cloud is an accelerating trend among businesses today.
4. Combining structured and unstructured data: Companies in a variety of industries, including, for example, many in the financial and banking sectors, are creating huge data lakes consisting of both structured and unstructured data that they mine, often in real-time, to be used for applications such as smarter risk and fraud processes.

Enabling use cases in your organization

At the heart of data transformation is the ETL (Extract, Transform, Load) process, in which data is extracted from its source, transformed into a common format, and then loaded into the target system. A number of on-premises and cloud-based applications have historically been used for this function. But traditional ETL tools struggle to keep up with the ever-growing volumes of data modern corporations must deal with.

Precisely Connect is specifically designed to be a high-performance software solution that processes a large volume of data transformations on the fly. Whether implemented on-premises or in the cloud, Connect integrates data from a wide variety of sources, including mainframes. A particular strength is that once a Connect job is designed, it can be deployed on-premises or in the cloud, and without change, on a variety of platforms, including Unix, Linux, and Windows.

Make this the year of your smarter data integration strategy: choose Connect for your data integration needs. To learn more about how Connect can help, read our eBook.