Definition:
An operational data store (or "ODS") is a database designed to integrate data from multiple sources for additional operations on the data. Unlike a master data store the data is not passed back to operational systems. It may be passed for further operations and to the data warehouse for reporting.
Detail about ODS:
An Operational Data Store (ODS) is an integrated database of operational data. Its sources include legacy systems and it contains current or near term data. An ODS may contain 30 to 60 days of information, while a data warehouse typically contains years of data.
An operational data store is basically a database that is used for being an interim area for a data warehouse. As such, its primary purpose is for handling data which are progressively in use such as transactions, inventory and collecting data from Point of Sales. It works with a data warehouse but unlike a data warehouse, an operational data store does not contain static data. Instead, an operational data store contains data which are constantly updated through the course of the business operations.
ODS is specially designed such that it can quickly perform relatively simply queries on smaller volumes of data such as finding orders of a customer or looking for available items in the retails store. This is in contrast to the structure of a data warehouse wherein one needs to perform complex queries on high volumes of data. As a simple analogy, a data store may be a company’s short term memory storing only the most recent information while the data warehouse is the long term memory which also serves as a company’s historical data repository whose data stored are relatively permanent.
The history of the operational data store goes back to as early as the year 1990 when the original ODS system were developed and used as a reporting tool for administrative purposes. But even then, the ODS was already dynamic in nature and was usually updated every day as it provided reports about daily business transactions such as sales totals or orders being filled.
The ODS that time are now referred to as a Class III ODS. As information technology evolved, so did ODS with the coming of the Class II ODS which was already capable of tracking more complex information such as product and location codes, and to update the database more frequently (perhaps hourly) to reflect changes. And then came the Class I ODS systems from the development of customer relationship management (CRM).
Many years, IT professional were having great problems with integrating legacy applications as the process would entail so many resources for maintenance and other efforts had done little to care of the needs of the legacy environments. With experimentations and development of new technologies, there was little left for company IT resources. As IT people had experienced with legacy applications, the legacy environment has become the child consuming its parent.
There were many approaches done to respond to the problems associated with legacy systems. One approach was to model data and have information engineering but this proved to be slow in the delivery of tangible results. With the growth of legacy systems came the growth in complexity as well as the data model.
Another response done to address legacy system problems was the establishment of a data warehouse and this has proven to be beneficial but a data warehouse only addresses the informational aspect of the company.
The development of an operational data store has greatly addressed the problems associate with legacy systems. Much like a data warehouse, data from legacy systems are transformed and integrated into the operational data store and once there, data ages and then passed into a data warehouse. One of the main roles of the ODS is to represent a collective, integrated view of the up-to-the-second operations of the company. It is very useful for corporate-wide mission-critical applications.
