A Modern Approach to the Operational Data Store

To gain the real-time visibility necessary for tactical decision-making, organizations must be able to quickly find the data relevant to their business questions. In fast-moving industries such as retail, financial services and manufacturing, an operational data store (ODS) makes this possible. Let’s explore how an ODS works, the potential benefits of using one and how a modern approach to the ODS can give businesses access to the data they need more quickly and efficiently. 

An operational data store (ODS) is a central database that aggregates data from multiple systems, providing a single destination for housing a variety of data. With information constantly updated, an ODS enables a current snapshot of relevant business metrics, allowing decision-makers to take advantage of time-sensitive opportunities and make data-informed decisions while business operations are occurring. 

An ODS integrates data from its source systems in its original format. Once loaded, data in the ODS can be scrubbed, resolved for redundancy and checked for compliance with relevant business rules. An ODS is especially useful for light-duty analytical processing such as operational reporting. And since data contained in the ODS is always the most recent data available, this system is ideal for real-time data analysis on business processes as they are happening. 

Unlike extract, transform, load (ETL) systems, an ODS ingests raw data from production systems in its original format, storing it as is. There’s no need to transform the data before it can be analyzed or used for making operational decisions about the business. 

Although ODSs and data warehouses share some similarities, their functions are rarely interchangeable — and their overall purposes are different. Instead, these two systems typically play complementary roles in the processing and storage of analytical data. ODSs can act as a bridge between the source systems and the enterprise data warehouse, serving as an interim area to prepare incoming data for long-term storage.

Typically, data in an ODS will be structured similarly to its source systems. In contrast, data warehouses require an ETL process prior to storage and can be used to store structured, semi-structured and unstructured data. 

Data warehouses can hold enormous amounts of historical data, making them ideal platforms for running complex queries on large data sets. Data they contain is used to inform large-scale, enterprisewide decision-making. ODSs, on the other hand, are designed for light-duty queries on small data sets as they only store the most recent operational data, making them suitable for strategic, in-the-moment actions such as determining how much product was sold in the past hour or the region where the majority of today’s online sales originated from.

Data warehouses are high-capacity data storage repositories designed to hold historical business data. An ODS is a short-term storage solution meant to hold just the most recent data received from the business systems that feed into it.

ODSs continuously overwrite existing data as new data arrives, making the data they contain much more volatile than data stored in a data warehouse. Rapid changes in data received from source systems can quickly impact the data contained in the ODS.

ODSs contain only the most current operational data, providing a useful snapshot of business operations as they are in the moment. Data warehouses are designed to store massive amounts of historical data useful for performing large-scale analysis on complex data sets.