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Enterprise Information Integration (EII) is simultaneously an old and new idea. Data integration was the earliest form of integration. Data warehouses and data marts were developed to create an aggregated view of corporate information residing in disparate systems, and to give business users better access to corporate information.

The workhorse of data integration has been ETL tools. They were created to extract the information, transform it into a consolidated view, and then load it into a data warehouse in a batch mode. The data volumes involved were generally large, the load cycles long, and information in the data warehouse typically a day to a week old. For synchronizing data across operational systems, operational data stores were created, which enabled the real-time update of information.

But the problem with each of these solutions was the need to physically move large volumes of data from source systems to multiple consolidated data stores including the data warehouse, distributed data marts, operational data stores, and analytical multi-dimensional databases. While these consolidated data sources continue to be important to organizations, latencies and inconsistencies are pretty much a given with such an architecture.

EAI vs. Information Integration

The batch ETL solutions of the past were not capable of meeting the real-time integration needs of the new breed of online systems. Information that is days old is not acceptable for real-time solutions. While the ETL tools continue to serve a valuable function in organizations, they became the step child of integration.

The newer Enterprise Application Integration (EAI) solutions came along and solved the data latency problem by synchronizing changes across systems in real time. However, EAI less adequately addressed the need to aggregate and consolidate data and information across the enterprise. EAI can effectively move data among systems in real time, but does not define an aggregated view of the data objects or business entities.


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