Business organizations are driven by big data. A wide variety of data from different sources is generated daily, and businesses can derive insights that aid business intelligence. Understanding how to leverage big data is key to gaining a competitive advantage and completing a digital transformation. Those business organizations that leverage big data have a greater likelihood of gaining new customers, improving customer retention, and seeing above-average profitability. We will discuss “Business Data Integration” in this article.
What Is Data Integration?
One of today’s best practices in business is data integration. It’s the process of combining data from disparate sources to create a unified view. Integration makes data easier to access and consume for both systems and business users. Integration helps reduce IT costs, free up resources, improve data quality, and aid in technological innovation without the need to change existing applications or data architectures. Undergoing application integration is a smart way to stay ahead of the competition. Integration results in increased operational efficiency and workflow, better data quality through the automation of data transformation, and more valuable business insights.
Data Integration 101
Organizations collect a wide variety of structured and unstructured data from disparate data sources daily. Business users need a reliable way to capture the potential of any data format through data valuation and other processes. Doing this requires a means of supporting enterprise reporting and business processes. Data is typically spread across a variety of software applications, databases, and data sources hosted on-premises, in cloud applications, on IoT devices, or through third parties.
The traditional approach to integration involves the physical movement of data from different data sources to a staging area for cleansing, mapping, and transformation before deployment to a data warehouse. A second approach to integration is data virtualization. This approach uses a virtualization layer to connect physical data sources.
The Benefits Of Integration
There are several benefits of implementing integration software for business users. It’s a good idea to use an integration tool from a vendor versus writing custom integration code. This results in improved data quality, optimal performance, and time savings. Application integration streamlines development by allowing users to create a catalogue of formats and sub-processes for reuse. Integration logic can be tested in real-time, which reduces the time necessary for implementation and maintenance.
Data integration processes are configured to connect applications and systems and can reflect any change immediately. The use of an API with enterprise integration software verifies data integration technology and target systems, which helps reduce the amount of time spent testing. Data integration tools ease future integrations by establishing a common data model through which all integration processes will communicate. Because services and events can be easily created, it’s easier to subscribe to the right events to improve business insights.
Many business processes rely on legacy applications, and this data needs to be integrated with other systems. Core business functionalities provide assets for reuse in other services, despite many of these components and capabilities being replaced by other applications. Integration helps infuse data in legacy systems into modern environments.
The significance of data integration is not to be overlooked. Modern business capabilities are augmented by leveraging standard SaaS applications and custom applications. Integrated SaaS, custom, and partner applications and their respective data are key to a successful digital transformation. The key to standing out from the competition is uniquely combining business functionalities. Integration helps organizations analyze data in motion and at rest to create business rules and apply those rules to new data to improve business operations.
Data integration is a prerequisite for processing data, specifically analytics. Being able to bring data from different systems into one single view facilitates analytical reporting to produce a full, unified view of all information.
