What Is Operational Data Store?

An Operational Data Store (ODS) is a centralized database designed to integrate and store real-time or near-real-time data from multiple sources.

What Is an Operational Data Store?

An operational data store (ODS) is a centralized database that consolidates and integrates data from various transactional systems to provide a real-time or near-real-time view of current operations. It is designed to support the immediate reporting and analysis needs of an organization by maintaining a continuously updated repository of operational data.

Unlike traditional data warehouses, which are optimized for historical analysis and long-term storage, an ODS focuses on short-term, up-to-date data that is used for daily operations. The ODS enables businesses to access and query current data without impacting the performance of transactional systems, ensuring that operational decisions can be made based on the most recent information available.

It is particularly useful for organizations that require rapid access to fresh data for tasks like monitoring, reporting, and responding to operational events. While the data in an ODS is not typically transformed to the same extent as data in a data warehouse, it is still cleansed and integrated to provide a consistent and accurate view of ongoing operations.

How Does an Operational Data Store Work?

Here’s a step-by-step explanation of how an ODS typically functions:

1. Data collection. The ODS collects data from multiple transactional systems, such as enterprise resource planning (ERP), customer relationship management (CRM), or point-of-sale (POS) systems. These systems are designed for day-to-day operations but querying them directly slows down their performance. To avoid this, data is continuously or periodically extracted from these systems.
2. Data integration. Once data is gathered, it is processed and integrated within the ODS. This step involves cleansing the data to ensure accuracy and consistency across different sources. The data is standardized into a common format, enabling a unified view of operational activities.
3. Real-time updates. Unlike data warehouses, which typically update data in batch processes, the ODS supports real-time or near-real-time data updates. This ensures that the most current operational data is available for reporting and analysis.
4. Data storage. The ODS stores data temporarily, usually for a short period, to support ongoing operations. It typically focuses on current, live data rather than historical data, making it ideal for operational reporting. The ODS does not generally retain data for long-term analysis, which is the purpose of a data warehouse.
5. Data access. Users or systems can query the ODS to generate reports, run analyses, or monitor key performance indicators (KPIs) without impacting the performance of the source transactional systems. The ODS provides a consistent and reliable view of the latest operational data, making it ideal for daily decision-making processes.

Operational Data Store Uses

Here are key uses of an operational data store, along with explanations:

• Real-time operational reporting. One of the primary uses of an ODS is to provide up-to-date operational reports. Since the ODS is updated in real-time or near-real-time, it can be queried to generate current operational reports without affecting the performance of source systems. This makes it ideal for monitoring daily activities such as sales transactions, customer service interactions, or inventory levels.
• Data integration across systems. An ODS integrates data from various transactional systems, such as ERP, CRM, and POS systems, into a unified format. This integrated view helps organizations manage and understand cross-departmental operations, ensuring that data from different systems is consistent and easily accessible. It solves the problem of data silos by creating a centralized repository of operational data.
• Operational decision-making support. The real-time nature of an ODS enables organizations to make timely decisions based on the most recent data. It supports managers and decision-makers who need to act on current operational data, such as in cases of inventory management, customer interactions, or monitoring system performance.
• Data cleansing and validation. An ODS is often used to clean, validate, and standardize data from multiple sources before it is pushed to downstream systems or used for operational purposes. The process ensures that the data being analyzed or used for reports is accurate and free from discrepancies, preventing errors that might arise from inconsistencies in the source systems.
• Source system offloading. Querying transactional systems directly degrades their performance, especially if there are frequent or complex queries. By offloading queries to an ODS, organizations can maintain the performance of their source systems while still allowing users to access important operational data. This ensures that business-critical transactional systems, like order processing or billing, continue to function smoothly.
• Staging area for data warehousing. An ODS can serve as a staging area for data before it is moved to a data warehouse for long-term storage and analysis. The ODS can help preprocess data, ensuring it’s cleaned and integrated before entering the data warehouse.
• Real-time monitoring and alerts. Many organizations use an ODS for real-time monitoring and alerting purposes. By continuously receiving updates from transactional systems, the ODS tracks specific metrics or conditions in real time, triggering alerts when certain thresholds are reached. This is crucial for time-sensitive operations, such as fraud detection, system failures, or critical business events that require immediate attention.
• Support for business processes. An ODS supports day-to-day business processes that rely on timely data, such as order fulfillment, customer service operations, or supply chain management. Since these processes often require current data to operate efficiently, the ODS ensures they have access to fresh information, enabling smoother operations and improved response times.

Operational Data Store Benefits

Here are the key benefits of an operational data store:

• Real-time access to data. An ODS provides real-time or near-real-time access to data, allowing organizations to monitor and analyze current operational activities. This is particularly beneficial for businesses that need up-to-the-minute information, enabling them to make timely decisions and respond quickly to changing conditions.
• Improved decision-making. By offering a unified and current view of operational data, an ODS enables better decision-making. Decision-makers can rely on accurate and consistent data when evaluating performance, managing resources, or resolving issues. This leads to more informed, data-driven decisions and improves overall operational efficiency.
• Reduced load on transactional systems. An ODS offloads query processing from source transactional systems, helping to preserve their performance. Instead of running complex reports or analyses directly on systems like ERP or CRM, which could slow them down, users can query the ODS. This allows the core systems to continue handling transactions without disruptions.
• Data consistency across systems. An ODS integrates and standardizes data from multiple sources, ensuring consistency across the organization. This eliminates the problem of data silos, where different departments or systems may have conflicting or incomplete data. The ODS creates a single source of truth for operational data, improving data quality and consistency across the business.
• Supports real-time reporting and monitoring. With real-time data updates, the ODS is ideal for generating operational reports and monitoring key performance indicators (KPIs). This helps businesses stay on top of their performance, detect issues early, and take corrective actions swiftly. It’s especially useful for industries that rely on real-time data, such as retail, logistics, or customer service.
• Enhances data quality. Before data is stored in the ODS, it undergoes cleansing and validation, ensuring that inaccurate, incomplete, or duplicated data is corrected. Improvement in data quality benefits downstream processes, reducing errors and ensuring that operational decisions are based on reliable information.
• Flexibility for operational changes. The ODS is designed to be flexible and adaptable to changing operational needs. It easily integrates new data sources or accommodates changes in business processes, making it a valuable tool for organizations undergoing digital transformation or experiencing rapid growth.
• Faster data integration. Data from multiple systems is integrated and consolidated in the ODS, allowing businesses to quickly access a comprehensive view of operations. This reduces the time needed to manually gather data from different systems, providing faster insights and enabling real-time analysis.
• Staging area for data warehousing. The ODS can be set up to handle short-term, operational data processing while the data warehouse is reserved for historical analysis. This division of labor between the ODS and the data warehouse enhances overall data management and analysis capabilities.
• Improved operational efficiency. The ODS enhances overall operational efficiency by providing real-time data and reducing reliance on transactional systems for reporting. Teams can access the data they need without waiting for batch processing or slowing down core systems, leading to faster response times and smoother operations across the organization.

Operational Data Store vs. Data Warehouse

An operational data store and a data warehouse serve different purposes in data management. The ODS is designed for real-time or near-real-time data integration from multiple sources, providing up-to-date information for operational reporting and day-to-day decision-making. It focuses on short-term, current data, which is continuously updated and queried without affecting the performance of transactional systems.

In contrast, a data warehouse is optimized for historical data storage and analysis and is typically updated in batch processes. It stores large volumes of historical data for long-term trends, complex analyses, and strategic decision-making.

While the ODS supports immediate operational needs with real-time data, the data warehouse focuses on deep, retrospective analysis and reporting over extended periods.

Here’s a table comparing an operational data store and a data warehouse.