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2022-10-20

Evolution of Data Integration: From Traditional ETL to Self-service

Evolution of Data Integration: From Traditional ETL to Self-Service

Every company has a fundamental need to effectively manage the information available to it and coordinate as well as harmonize the tasks of its technology and human resources. This can be accomplished with several avenues in our current digital era. This includes enterprise data integration, usage of automation powered by AI and machine learning, and by enabling different systems to work with each other to deliver the value promised to customers and grow revenue. 

Let’s go back and take a quick look at the evolution of these enabling technological solutions and learn how their deployment supports organizations today.

Developments in the World of Data Integration 

The data integration process encompasses steps of consolidating different datasets from different sources into a unified, single location or database, where it can be analyzed for future use. 

The concept of data integration dates back to the 1980s. The first data integration system driven by structured metadata was designed at the University of Minnesota in 1991 for the Integrated Public Use of Microdata Series (IPUMS)

This approach involved extraction, transformation, and loading data from heterogeneous sources so that data from different sources becomes compatible. This kind of movement of the 1990s was actually the first of an enabling technology that eliminated the need to add data manually each time it is moved from system to system. 

However, this particular data integration technology posed many infrastructure and complexity-related issues. And so, like any other technology, data integration solutions also needed improvement. 

Today, data integration technology uses automation and self-service to consolidate data and build connections quickly, securely, and easily. Let’s find out how these modern data solutions can help. 

The Emerging Role of Automation and Self-service 

Automation powered by AI and ML has changed the world of data integration. AI-enabled technologies have transformed the way companies map and integrate data, making it much faster and more secure. 

Along with that, self-service helps companies perform data integration steps much more quickly by bringing non-technical business users to the business forefront. Self-service data integration empowers non-technical business users to implement data connections while freeing IT to focus on more strategic business priorities. Users can rely on features such as pre-built connectors, shared templates, dashboards and intuitive screens to implement data connections much more quickly than legacy data integration solutions. Further, users can leverage this modern solution to onboard customers 80 percent faster. That makes a powerful first impression on customers as it becomes easier for business workers to address and meet the needs of their business customers. That way companies can create new revenue streams and reduce overhead costs. 

When self-service is extended to customers, they can self-onboard and better manage their ongoing digital interaction, which helps companies create additional value. 

These solutions also have an end-to-end encrypted environment that allows only authenticated business users to access and work on data. This saves companies from encountering data breaches and thefts to a great extent. 

Both automation and self-service have reimagined the process of data integration. They have made it much faster, more secure, and simpler than ever. Companies that aspire to deliver the value promised to customers sooner and grow revenue faster must adopt these modern technological solutions.