An operational data store (ODS) is a central database that provides a snapshot of the latest data from multiple transnational systems for operational reporting. It enables organizations to combine data in its original format from various sources into a single destination to make it available for business reporting.
What is an operational data store?
An ODS contains up-to-date information integrated from operational sources, and supports business intelligence (BI) tools that aid in tactical decision-making. For example, an administrator can set up an ODS to pull weekly batches of data from a rarely updated billing application, ingest individual transaction records as they occur in a sales database (thanks to triggers within that database), then combine both into new relational tables. Querying and reporting on operational data in an ODS thus comes with a guarantee that these integrated tables contain the most recent, relevant snapshot of the enterprise.
Operational data store benefits
An ODS provides current, clean data from multiple sources in a single place, and the benefits apply primarily to business operations.
- The ODS provides a consolidated repository into which previously isolated or inefficiently communicating IT systems can feed.
- ODS reporting, which is focused on a snapshot of operational data, can be more sophisticated than reports from individual underlying systems. The ODS is architected to provide a consolidated view of data integrated from multiple systems, so reports can provide a holistic perspective on operational processes.
- The up-to-date view into operational status also makes it easier for users to diagnose problems before digging into component systems. For example, an ODS enables service representatives to immediately find a customer order, its status, and any troubleshooting information that might be helpful.
- An ODS contains critical, time-sensitive business rules, such as those automatically notifying a financial institution when a customer has overdrawn an account. These rules, in aggregate, are a kind of process automation that greatly improves efficiency, which would be impossible without current and integrated operational data.
Operational data stores and data warehouses: the differences
An ODS is designed for a different purpose than a data warehouse.
- An ODS may be used as an interim area for a data warehouse; it sits between the data sources and the data warehouse.
- An ODS is designed to perform simple queries on small sets of data, while a data warehouse is designed to perform complex queries on large sets of data.
- An ODS deals exclusively with current operational data and basic status-level reporting because an ODS continuously overwrites data. A data warehouse continually inserts records into existing tables and can aggregate data across historical views.
Businesses use a data warehouse’s centralised repository to inform enterprise-wide strategies, while the ODS is more tactical. Depending on use cases and business requirements, organizations may use one or the other, or both together within a tiered data architecture.
Businesses that need aggregated historical data for analytics often set up a complementary data warehouse. Similarly, if a business with a data warehouse needs current, integrated operational data for day-to-day functioning, it can implement an ODS.