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How do operational data integration and analytical integration differ?

Get a definition of operational data integration, plus learn how it differs from analytical data integration and find out if you should implement both styles.

What is operational data integration? How is it different from analytical data integration? Should companies plan for both?
When folks discuss analytical data integration, they are typically referring to moving and integrating large volumes of data in a manner that allows a delay between the actual time the data is created and when it is loaded into the target system (the delay can be minutes, hours, days or longer). This is the most common approach to integrating and loading data into a data warehouse.

When people talk about operational data integration, they are typically referring to migrating and loading small quantities of data (usually individual records or transactions) moments after the data is created. An example of this is when a customer updates his or her address on a website and within seconds all of the other operational systems within the company that owns the site have access to that updated information.

Unfortunately, many organizations implement analytical data integration and operational data integration as completely separate and autonomous islands. The problem with this approach is that there is no mechanism to ensure that the actual logic or rules involved in integrating multiple data sources is consistent across these two different islands. The disparity of integration rules and logic is inevitable because the development teams are typically different people in different groups.

One of the strengths of a successful master data management (MDM) program is to establish a centralized repository, or hub, that will store and maintain the integration logic required to integrate data from multiple systems. The MDM hub provides operational access to subject-area information based on the various contributing operational systems.


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