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Operational databases usually use an online transaction processing database which is optimized for faster transaction processing (create, read, update and delete operations). An operational database is the source for a data warehouse. [1]
These terms refer to the level of sophistication of a data warehouse: Offline operational data warehouse Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented database. Offline data warehouse
An operational data store (ODS) is used for operational reporting and as a source of data for the enterprise data warehouse (EDW). It is a complementary element to an EDW in a decision support environment, and is used for operational reporting, controls, and decision making, as opposed to the EDW, which is used for tactical and strategic decision support.
An operational system is a term used in data warehousing to refer to a system that is used to process the day-to-day transactions of an organization. These systems are designed in a manner that processing of day-to-day transactions is performed efficiently and the integrity of the transactional data is preserved.
For example, an enterprise resource planning (ERP) system (such as SAP or Oracle e-Business Suite) may store a customer record; the customer relationship management (CRM) system also needs a copy of the customer record (or part of it) and the warehouse dispatch system might also need a copy of some or all of the customer data (e.g., shipping ...
It differs from an operational data store because a data hub does not need to be limited to operational data. A data hub differs from a data lake by homogenizing data and possibly serving data in multiple desired formats, rather than simply storing it in one place, and by adding other value to the data such as de-duplication, quality, security ...
Operational efficiency: With consistent and accurate data, operational processes such as reporting, inventory management, and customer service become more efficient. Regulatory compliance: MDM tries to help organizations comply with industry standards and regulations by ensuring that master data is accurately recorded, maintained, and audited.
A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.