Application and data integration involve the interconnection of disparate applications and data management platforms. However, while they may sound like similar processes, their capabilities are uniquely different. Both these integration services are complementary in nature. The IT industry is working towards the convergence of these technologies. Despite understanding the scope of these technologies, businesses have yet to adopt an integrated approach. If the vastly different capabilities of these integration models came together, they would tremendously enhance digital business.
Transactional versus transformative
As most business models are based on Service Oriented Architecture (SOA), application integration works at a service level platform. Systems were integrated by a middleware in the past, but advances have been made today. The data simply changes hands from one application to another through a synchronous or asynchronous process. Information exchange is bi-directional.
Data integration has no such concept of exchange. It pulls data from all databases combines them and transforms them into relevant information. It is not only limited to intrinsic databases but can be programmed to include, within its domain, extrinsic data. With big data’s capacity for data generation, this transformative function is vital.
Point-to-Point versus compilation
Application integration begins with the user entering source information that exchanges hand with other applications, in order to pull out the relevant information. This is a point-to-point architecture model followed by application integration models. This system is intended to keep the applications synchronized and in real time. For example, a customer places an order, generates an invoice and the information is automatically updated in the financial database.
However, a large organization may have thousands of applications integrated. This means that they have an even larger amount of interfaces. Pragmatically, it is impossible for a customer to access all these various interfaces individually. Data integration hacks through the forest of cluttered data, providing the user only what is relevant.
Efficiency versus efficacy
There are multiple layers of application integration from the user interface to database. Application integration systems synchronize the customer database to relevant channels that can access this data. For example, linking your social media account to create a new account in an application. Simply making this data accessible in real-time is the primary goal of application integration.
Data integration takes this goal a step further by making the entire process seamless. Also, its transformative functions help in actually creating value from the large amounts of data. Thus, companies can leverage this relevant data to achieve desired goals.
The key challenges today are that there is no single product that can meet the integration infrastructure needs of an entire organization. Outdated database platforms are still preferred over more powerful tools. Relevant use cases that involve integration architecture are yet to be holistically implemented by IT firms. Using a separate data and application integration model is akin to paying your taxes twice. A singular business integration model will cut down your implementation costs.
