4 Data Integration Approaches – Which One is Right For You?

Data integration involves a process of combining technical as well as business processes employed to blend data from myriad sources into useful, meaningful, insightful information. In simple words, data integration is the careful and methodical blending of data gathered from disparate sources such as databases, files, mainframes, etc., making it more valuable and useful than ever before. Here blending does not signify a process of moving data from one place to another or transferring streams of data into a single repository. Actually, blending indicates a process of turning the incoming data more usable and comprehensive, and so organizations have a unified view of the data for business intelligence.

Data integration solution becomes a necessity when an organization decides to use a novel application system and migrate its data from the conventional legacy systems into a new application. Data integration platforms have a huge application in cases of enterprise mergers wherein, two or more firms merge and need to consolidate their applications. Another most common application of data integration platform is when a company needs to establish a data warehouse which enables businesses to have access to a unified view of data that can be used for further analysis and business intelligence requirements respectively.

Data Integration Approaches

In today’s digital market place, data can be integrated using many data integration approaches. Companies no more have to rely on ETL tools to gain a unified view of data.

Some of the most common data integration approaches have been discussed in this blog post. Let us explore the types briefly.

1. 1. Manual Data Integration Approach: Manual data integration approach involves the creation of a web-based user interface or a particular application for systems users in order to show meaningful information by directly accessing all source systems. In reality, there is no unification of data in this approach.

2. 2. Middleware Data Integration Approach: In this approach, a layer present between the two disparate systems is central. It allows communication and offers transparency between various systems. In other words, middleware integration serves like a glue that keeps a multitude of legacy applications together, allowing seamless connectivity without requiring applications to communicate directly.

3. 3. Data Virtualization Integration Approach: In the data virtualization approach, an interface is used to provide a unified view of data collected from various sources with myriad data models. Though the data can be viewed in one location, it is not stored in that particular location, hence the term data virtualization. This approach retrieves and interprets data without the need for relying on uniform formatting or a single point of access.

Data virtualization method helps organizations to leave data in respective source systems and allow the creation of a series of unified views. Hence, business users can access this unified view to use data for extracting valuable insights.

Due to a large number of benefits, there are a number of organizations that use data virtualization approach today. The primary benefit offered by this approach is its real-time data visibility capability. Data when available in real-time allows organizations to meet the needs of customers, offering delightful customer experiences and increased satisfaction. Another main benefit offered by data virtualization approach is its ability to eliminate the need for creating a separate data repository where consolidated data can be stored.

With multiple benefits, companies choose data virtualization approach to meet their data integration needs. However, this approach has a number of drawbacks too. Some of the drawbacks include the availability of limited possibility of data’s history or data version management that inhibits the capability of organizations to use data properly. Additionally, this approach offers an extra load on the source systems that can severely damage the company’s overall performance and operational efficiency.

4. Data Warehouse Approach: Data warehouse approach is the most commonly used approach that involves the creation of a new data warehouse wherein, unified versions of data gathered from all source systems are stored and managed independently. This approach serves useful for business users due to its ability to easily manage a history of data or data versioning. In addition, the data warehouse approach offers the ability to merge data from various sources including, flat files, databases, mainframes, etc. and store it into a central repository.

Depending on the requirements and end-objectives, you can make use of any of the aforementioned approaches to fulfill your data integration needs.

My name is John Scutz, I'm a digital strategist at Adeptia Inc.. I was born on January 1990 in Chicago. I contribute Adeptia efforts related to Blogs, Newsletters, Articles,