Information is power. This age old adage couldn’t be more relevant today than it ever has been before. In this era of technology, most consumers have instant access to a nearly infinite sea of information with the click of a few buttons on their smartphone. This helps consumers make informed decisions on their purchases, it keeps organizations honest and transparent, and it can even lead to major societal progress. Information is also used just as heavily on the other side of the industry-curtain, though. Companies and organizations are constantly seeking new and innovative ways to utilize the consumer data they collect. One of the most common problems that organizations look to solve is how to get disparate consumer data to work together.
Two common practices that organizations implement to try and make the most out of their data are data integration and application integration. These two strategies are both aimed at getting data to synchronize between systems and work together for the benefit of the organization. However, the approach differs significantly.
Understanding Data Integration
Data integration is the bulkier of these two strategies and involves the transferring of data from one system to another. Whether these are internal systems or external systems doesn’t really matter as long as data is being moved from a source system to a target system. Common data systems that organizations utilize are production databases, data warehouses, and 3rd party SaaS products that generate and collect consumer data.
There are five main paths through which organizations can accomplish data integration which come with a variety of pros and cons. These five choices include, iPaas, CDP, ELT, ETL, and Reverse ETL.
Now some of these solutions are more robust than others. For example, iPaaS, or integration platform as a service, really excels at moving data between cloud-based apps. However, there is little-to-no data transformation that will take place.
CDPs also move data between cloud-based apps, but the CDP makes use of a somewhat centralized platform which enables some data transformation capability. ELT and ETL are fairly similar in their transformation power and their design to move data from cloud-based systems into a data warehouse, but the main difference here is where the data-transformation takes place. In ELT this transformation takes place once it’s been loaded into the warehouse whereas with ETL the transformation occurs before data is loaded into the warehouse.
Finally, reverse ETL facilitates the transfer of data from the warehouse to cloud-based apps. Any data-transformation takes place in the warehouse prior to the transfer, but there might be some minimal transformation that happens simply to fit data to the target source platform. This helps organizations seamlessly make use of their data in nearly any context.
These various data transfer methods excel in different environments and offer your organization a solution for any data-transfer need.
Taking a Look at Application Integration
Application integration is also designed to facilitate collaboration between data-based systems. However, application integration is more about data-sharing and synchronization than it is transferring and transformation.
In simple terms, application integration acts as a bridge between two or more applications so that they can work together and share information with one another. This can be helpful in the context of pairing Slack with SalesForce, for example – in order to create a more efficient lead-follow-up procedure. As you can imagine, there are many scenarios in which it would be useful for organizational systems to talk and work with one another.
APIs are also a powerful way for organizations to connect internal or legacy systems to cloud-based applications that offer more modern features and workflows. This helps bring businesses into the modern age while creating a seamless workflow.
The Main Differences
The main difference between data integration strategies and application integration strategies is the area of focus. Data integration is mainly focused on the moving and transformation of data from one system to another. This specifically falls in the realm of data engineering and is designed to boost the accuracy and impact of data-models utilized throughout the entire organization.
Application integration, on the other hand, enables cloud-based systems to work together and talk to one another, but has little-to-no data-transformation capability.
As such data integration is often used to accomplish data migration to a warehouse, to consolidate consumer data from disparate sources, and to assist with the transition to a multi-cloud or hybrid-cloud work environment. Application integration is typically used to collect data from the internet of things, build automations and workflows between applications, and to sync legacy systems with more modern applications.
Both data integration and application integration strategies can help a business make the most out of their consumer data by making sure that their systems are working together. Utilizing these strategies can improve efficiency, create better workflows, and help organizations deliver a top-tier customized consumer experience with consistency.
