Managing data is at the core of both application and data integration. Both have the same goal — to make data more accessible and functional for the end user.
Both translate various data sources and transform them into a new, complete set of data. And both application integration and data integration are typically cloud-based, offering the accessibility and scalability that comes with cloud computing.
When it comes to use cases, however, there are several ways in which these types of integrations differ. Data integration is typically done in batches focused on creating a new data set that can reveal business insights. Application integration is used to create better workflows in day-to-day operations.
By unifying the apps’ workflows and merging the data in real time, the integration eliminates data silos and increases efficiency throughout the organization. For example, a company may want to integrate an instant messaging tool like Slack with Salesforce for faster, more efficient follow-up on leads. Here, application integration enables users to seamlessly share information between the two.
Application integration is also a way for companies to connect cloud-based and SaaS applications to on-premises and legacy systems so employees can use newer tools and technology with existing systems.
Benefits of application integration include following:
Saving time: Integrating the data from two or more applications can reduce the tedious work of transporting data back and forth manually.
Increasing functionality: Combining the capabilities of multiple applications, such as a banking app with a bookkeeping app, makes work more efficient, increases productivity and adds value to the user.
Encouraging the exchange of information: Connecting applications throughout the organization eliminates silos that prevent various teams and departments from sharing ideas. Application integration helps foster a culture of collaboration.
Data integration is the process of taking data from different sources and formats and combining it into a single data set.
But data integration goes beyond moving data from one database to another — it’s also the process of making the data more usable. Data integration takes structured and unstructured data from disparate sources to create valuable new data sets. This enhances the capabilities of your analytics, enabling you to better understand business operations and identify new opportunities for innovation.
Data integration’s most basic function is taking data from one source, transforming that data into a form that another application recognizes and loading it into the application. However modern data integration needs have pushed capabilities beyond extract, transform and load (ETL). Conducting batch integration as well as real-time integration and using automation to address errors are helping businesses break down siloes and make the most of their data today.
Benefits of data integration include the following:
Making data more accessible: Data integration can combine data from multiple locations and multiple sources into a unified view. Having these comprehensive insights helps companies improve collaboration and innovation.
Obtaining greater insights: Having a unified view of your data will give you better intelligence on your operations and customers. This, in turn, helps make more informed decisions and improve your processes.
Increasing data integrity: Your data quality is the true measure of its value. When data is incomplete or contains errors, it can hinder your ability to use it. Data integration tools can identify low-quality data.
How application integration and data integration are different
The main differences are the speed at which the data is transformed and the amount of data involved. Application integration works in real time with smaller data sets, so companies can quickly respond to new information or performance issues as they occur. It also enables people throughout the company to have access to the same information within the app instantly, even if information is being updated in different locations.
Data integration is typically done in batches, often after processes have been completed, to eliminate redundancies and ensure data quality. Typically, data integration deals with large sets of data at rest; it happens when the process that created the data has been completed. Application integration, on the other hand, is for integrating real-time data between two or more applications.
They also differ in how they are managed organizationally. Application integrations are managed by DevOps as part of the company’s overall software development operations. Their role is to connect applications to create efficient workflows, either through existing integration platforms or by building a custom integration. Data integration is overseen by DataOps, which focuses solely on the management and orchestration of data for business purposes.
When to use application integration vs. data integration
Generally speaking, data integration is used when organizations need to combine and analyze static data, while application integration is best for when you need to interact with data that is changing in real time.
Take, for instance, business intelligence. When using big data sets, data integration will ensure the data is consistent and accessible to analytics tools in a single view. Data integration analyzes disparate forms of data precisely, opening up new insights businesses can use to improve operations.
Application integration is for instances where speed is essential. While data integration ensures accuracy, it is much slower than application integration. Capturing data through the application, whether this is customer data or inputs from the manufacturing floor, means you can translate it to other tools and apps through application integration and act immediately in a variety of ways. Having greater access to data from disparate sources in one view helps expand the possibilities for innovation.
Common use cases
Application integration is typically used to do the following: