Integrated Operational Data also known as operational data stores is a volatile collection of data that support an organization’s daily business activities.

Integrated Operational Data are the output of the operational data store. An operational data store is an architectural construct which is part of the larger enterprise data management system. It is subject-oriented, integrated and time-current.

In an integrated enterprise data management, there are several computer server hosting database systems and these computers are connected by a network so they can work together to achieve an efficient operation. Although having a network of computer servers has more benefits compared to implementing an enterprise data management system in a monolithic structure, there are several great challenges that need to be tackled.

One of the biggest problems with a networked enterprise data management system is in the area of integration. First and foremost, the environment is networked and so the system will have to deal with different kinds of network protocol. But the problem associated with networking pales in comparison to problems arising from system and data disparity. Different computer servers on the network may be running on different platforms. Within each computer, there could be different database systems. And some departments may be still be using flat files and legacy data and these are equally important that need to be processed by the system.

An integrated operational data store answers all these problems related to system disparity. For sake of clarity and simplicity, the term operational data store literally means that it is a storage for all data currently used in operation. Perhaps the easiest term we can find as the opposite to the term operation is the term archive.

In technical description, an integrated operational data store is a subject-oriented, integrated, volatile, current-valued, detailed-only collection of data in support of the needs of an for up-to-the-second, operational, integrated, collective information.

An example of how an integrated operational data store operates can be illustrated by a banking environment wherein a large bank may be managing thousand of individual accounts. At any given money hundreds of these accounts may be changing status. And then there are large customers of the bank which has many accounts as well. In order to manage these status changes involves a complex array of customers, an operational data store handles the processes.

An integrated operational data store works closely with the data warehouse. In fact the data warehouse itself is a data store. But while the operational data store deals with current data, the data warehouse usually stores data for storing and archiving. They are both database systems with the operational data store acting as an interim area for a data warehouse. The operational data store contains dynamic data constantly updated through the course of business operations while the data warehouse generally contains relatively static data.

In a large business enterprise, data demand is very high and so it is not uncommon to find an information system with several operational data stores is designed for very quick performance of relatively simple queries on small amounts of data like finding tracking status of shipments instead. Several operational data stores share the load of enterprise data processing. An integrated operational data store connects all scatted operational data store with a software tool so that they work as one large efficient system.

Operational data stores need to implemented with top of the line and robust computer hardware and sophisticated software tools due to the nature of its processes that involve complex computing of very high quantity of data coming various sources. They need to work very fast because they exist to give very up to date information to data consumers.