To gain the real-time visibility necessary for tactical decision-making, organizations must be able to quickly find the various data relevant to their business questions. In fast-moving industries such as retail, financial services, and manufacturing, an operational data store makes this possible. In this post we’ll explain how an operational data store works, the potential benefits of using one, and how a modern approach to the operational data store can give businesses access to the data they need more quickly and efficiently.
What Is an Operational Data Store?
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.
Operational data stores integrate data from their source systems in their 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 operational data store 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.
How Does an Operational Data Store Differ from a Data Warehouse?
Although operational data stores 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. Operational data stores 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. Operational data stores, 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 operational data store is a short-term storage solution meant to hold just the most recent data received from the business systems that feed into it.
Operational data stores 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.
Current vs. historical data
Operational data stores 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.
Benefits of an Operational Data Store
Operational data stores are an important part of many organizations’ data strategy. Here are four unique benefits that an ODS offers.
Aggregate real-time data from multiple sources
Operational data stores are designed to provide a holistic view of current operational data, providing an end-to-end view of key business processes. Aggregating data from various business systems across the enterprise, an ODS unites real-time information received from disparate business systems into a unified stream of actionable data, helping business decision-makers take advantage of emerging business opportunities or troubleshoot issues.
Enable queries on real-time data
Querying data from multiple systems in real time makes it easier to complete time-sensitive tasks such as providing support to a customer for a recently placed order.
Access sophisticated reporting on operational data
Uniting data from multiple sources, an operational data store can create more in-depth, comprehensive reports. Rather than cobbling together data from each system individually, an ODS provides a comprehensive, consolidated view of all relevant operational data.
Set up automatic notifications using critical, time-sensitive business rules
An ODS can be configured with time-sensitive business rules designed to automatically send out alerts when time-sensitive events occur, such as a bank account being overdrawn.
Snowflake’s Unistore: A Modern Approach
Snowflake’s Unistore workload delivers a modern approach to working with transactional and analytical data, all within a single platform. Unistore removes the burden of moving data between systems and eliminates the need to manage redundant data sets across multiple solutions. Access your data when it’s needed, and be able to work with virtually all your data in one convenient location.
Data governance and security are simplified and standardized. Unistore also facilitates building enterprise transactional apps with simplicity, performance, and near-infinite scale. Teams can build transactional business applications directly on Snowflake. Ultimately, Unistore empowers you to act on transactional data immediately, build better customer experiences, and gain new insights by integrating transactional and analytical data in a single data set.
See what you can do with Snowflake. To give it a test drive, sign up for a free trial.