Operational Data Stores (ODS): An Overview and Use Cases in Data Engineering
Modern organizations are constantly striving to utilize their data more effectively. Data engineers facilitate this using different data management strategies. One such strategy is using an Operational Data Store (ODS).
An ODS integrates operational data from multiple systems within an organization in real time. Operational data is the data produced by an organization’s daily operations, like customer and sales information.
By using an ODS, engineers get a holistic and up-to-date understanding of current operations. It also facilitates efficient data integration, transformation, and delivery.
In this article, we will delve into the basics of an operational database system, how it fits into a data architecture, and its use cases across different industries.
Understanding Operational Data Stores
An ODS is a centralized data repository for data collected from the operational systems within an organization. It stores and processes operational data in real-time.
Data from multiple transactional systems, like CRM systems, log files, and external data feeds, is extracted and loaded into an ODS in their raw formats. Data engineers can then transform and cleanse this raw data to make it available for reporting, analysis, and operational decision-making.
In essence, an ODS is a bridge between transactional and analytical systems.
An ODS is updated in near real-time, making it an ideal choice for data scientists to analyze business processes as they are happening. Source data within an ODS can be directly fed into business intelligence (BI) tools for faster analysis.
An ODS can support reporting, ad hoc querying, and data archiving. It enables organizations to leverage their operational data for immediate insights and data-driven decision-making.
Most operational data stores can only store a small amount of data and cannot be used for historical analysis.
How an ODS differs from databases and data warehouses
While an ODS, databases, and data warehouses all store and manage data, they serve different purposes and have distinct characteristics:
- Purpose: An ODS stores current operational data and enables light-duty reporting and data analysis, while data warehouses are optimized for large-scale data storage and complex data analytics. Databases are built for transactional processing.
- Data Integration and Consolidation: An operational database integrates data in its original schema and does not require transformations. A data warehouse uses a schema on-write approach and Extract, transform, and load (ETL) to centralize high volumes of data from many data sources.
- Data Latency: An ODS provides real-time data updates, while most data warehouses are updated periodically through batch processing. Databases can also provide real-time updates but within the scope of a specific application.
- Data Structure and Granularity: Operational databases store source data in its raw format, preserving the original data fidelity, while relational databases use data models optimized for OLTP (Online Transaction Processing). Data warehouses have a structured schema ideal for OLAP (Online Analytical Processing).
- Scalability and Performance: ODS architectures are built for high scalability and performance to handle real-time data processing. Databases are designed to handle transactional workloads efficiently, ensuring ACID (Atomicity, Consistency, Isolation, Durability) properties. Cloud data warehouses are optimized for complex analytical queries on petabyte-scale workloads.
Key features of an ODS
1. Near real-time data integration: An ODS integrates transactional data from different production systems in real time or with minimal latency. This allows business users to access the latest information and make timely decisions on current operations.
2. Simple data transformations: An operational data store can perform simple data transformation processes to ensure data integrity and consistency. This may involve data cleansing, validation, and standardization.
3. Support for operational and analytical queries: Users can perform ad hoc queries and analysis in an ODS. It offers an intuitive interface for data exploration, custom report generation, and gaining insights from current data.
The Architecture of an Operational Data Store
An operational data store has many components and layers that enable real-time data capture. These include:
- Data Sources
- Operational systems: Transactional databases, CRM systems, and other operational applications that generate data.
- External data feeds: Data from external sources such as market data providers, social media platforms, or third-party APIs.
- Data Integration Layer
- Data extraction: This component extracts data from various sources. It may involve direct database connections, API calls, data ingestion pipelines, or other mechanisms.
- Change data capture (CDC): CDC mechanisms capture incremental changes to the source systems, enabling real-time updates in the ODS.
- Data transformation: Data transformation processes convert and standardize the data to ensure consistency and integrity.
- Data integration: The transformed data is integrated based on predefined mappings, business rules, and data models. This integration ensures a unified and coherent view of operational data within the ODS.
- Data Storage and Processing
- ODS database: The integrated data is stored in the ODS database, which is optimized for fast data ingestion, updates, and retrieval.
- Data partitioning: Large datasets in the ODS can be partitioned to improve query performance and optimize storage utilization.
- Indexing and caching: Indexes are created on frequently queried attributes to speed up data retrieval. Caching mechanisms may be employed to further enhance query performance.
- Data archiving: Historical or infrequently accessed data can be archived in separate systems, like a data warehouse, keeping the primary ODS database optimized for current operational data.
- Data Access and Presentation
- Reporting and analytics: An operational data store enables users to generate real-time reports, visualizations, and dashboards.
- Self-service interfaces: User-friendly interfaces, query tools, or visualization tools allow business users to interact independently with the ODS.
- Access control: Access controls and data security mechanisms are implemented to keep data secure.
- Data Governance and Metadata Management
- Metadata repository: A metadata repository stores information about the data sources, data mappings, transformations, and other metadata.
- Data lineage and auditing: Tracking data lineage and maintaining audit trails help ensure data traceability, compliance, and governance.
- Data quality management: Data quality processes, such as data profiling, validation, and monitoring, are implemented to maintain data integrity.
Where an ODS fits in a data architecture
The role of an ODS in the overall data architecture of an organization depends on its specific requirements and goals. Some common positions where an ODS fits in are:
1. Source systems and data extraction
In architectures that involve data integration and transfer between multiple systems, an ODS can serve as a data hub for transactional data. It can integrate data from many transactional systems and centralize it. This data can be modified and loaded into other systems, like cloud data warehouses, as needed.
Data engineers can use an ODS as a central point for data synchronization, ensuring consistency and coherence across the enterprise.
2. ODS as an intermediate layer between OLTP and OLAP systems
An ODS acts as an intermediate layer between transactional and analytical systems. It can be a staging area or intermediate hub where data engineers can transform and aggregate data before loading it into an analytical solution like a data warehouse.
This ensures that only relevant and high-quality data is transferred to the analytical environment, improving the efficiency and accuracy of downstream analytics and reporting.
3. Data flow to downstream data warehouses and data marts
Data pipelines transfer data from the ODS to downstream data warehouses or data marts. These processes extract relevant data from the ODS, apply further transformations or aggregations if needed, and load it into target systems.
This allows organizations to create a comprehensive data ecosystem that supports both real-time operational reporting and historical analytics.
ODS design considerations
Designing an operational data store requires careful consideration to ensure its effectiveness. Here are some key considerations to keep in mind:
- Data freshness and update frequency: Consider whether you need real-time data updates, the frequency of data extraction, and the integration mechanisms. Choose a strategy that balances data freshness, performance, and scalability.
- Data volume and performance: Anticipate data growth, increasing data volumes, and the number of concurrent users accessing the ODS. Design the architecture to accommodate future expansion by considering partitioning, indexing, and data archiving techniques.
- Data quality and consistency: Implement mechanisms for data cleansing, validation and deduplication to improve data consistency. Monitor data quality over time and have processes in place to address issues.
Use Cases for Operational Data Stores
Operational reporting and analysis
An operational data store can provide real-time or near-real-time reporting capabilities. Since it makes data available for immediate reporting and monitoring, it enables organizations to:
- Track key performance indicators (KPIs)
- Monitor operational metrics
- Make timely decisions based on up-to-date information
Data cleansing and transformation
Operational data stores can serve as a platform for data quality management. They allow engineers to validate and transform data before it is moved to storage, like a data warehouse, or fed into BI and analytics tools.
Organizations can improve data accuracy, completeness, and consistency by implementing strong data quality controls.
Data integration and consolidation
An ODS is a central hub for integrating and consolidating data from operational systems. It provides a unified view of the organization’s data. This is vital when an organization has to harmonize data for reporting, analytics, or data exchange purposes.
Advantages and Disadvantages of an ODS
Let’s take a deeper look at the pros and cons of an operational data store:
There are three key advantages of including an ODS in your data architecture:
1. Near real-time data availability
An ODS makes source data readily available for reporting, analytics, and decision-making. This near real-time data access enables organizations to monitor and respond to events promptly, improving operational agility.
2. Improved data quality and consistency
An operational data store helps data teams improve the quality of operational data. It is a staging area that enables data cleansing, deduplication, and enrichment processes. This ensures data accuracy and leads to better decisions, reduces errors, and enhances operational productivity.
3. Flexibility for operational and analytical queries
An ODS has the flexibility to support operational and analytical workloads. It enables operational queries focused on monitoring and managing ongoing operations. This includes tracking orders, managing inventory, or supervising customer interactions.
The operational database can also serve as a data source for analytical systems, like a data warehouse.
Using ETL or ELT, relevant source data can be moved to a data warehouse, where analysts can perform in-depth analysis, root cause analysis, pattern identification, and trend exploration. They can run complex queries and analyze historical data to derive insights.
1. Complexity of managing an additional data layer
A key disadvantage of using an operational database is the complexity associated with managing an additional data layer. This adds one more step in data pipelines that engineers must orchestrate. While using an ODS, data engineers must account for increased data integration, transformation, and governance processes.
2. Potential performance challenges for large data volumes
There can be challenges associated with handling high volumes of data and supporting concurrent queries in an ODS. As the volume of data grows, query response times may increase, impacting the user experience.
3. Maintenance and data governance considerations
Operational data stores can introduce additional complexities to database maintenance and quality control processes. They require continuous monitoring to accommodate changing source systems while providing real-time updates.
To combat these problems, organizations must establish robust data governance practices, assign data stewardship responsibilities, and implement data quality controls.
Examples of Operational Data Stores in Practice
Here are three examples of real-life use cases of an operational data store:
Use of an ODS in a retail business for inventory management
By leveraging an ODS for inventory management, retail businesses can:
- Centralize and synchronize inventory data across multiple sales channels, such as physical stores, online marketplaces, and mobile applications.
- Gain real-time visibility into inventory levels and an up-to-date view of stock availability.
- Feed forecasting algorithms and generate accurate demand forecasts. This helps retailers optimize inventory levels, minimize excess stock, and reduce carrying costs.
- Analyze supplier-related data within the ODS to identify reliable suppliers, negotiate favorable terms, and optimize the supply chain.
- Retailers can leverage ODS data to identify slow-moving or obsolete inventory, make informed pricing decisions, and optimize stock levels.
Use of an ODS in a healthcare organization for patient data management
In the healthcare industry, an ODS can:
- Consolidate and standardize patient data from multiple systems, including electronic health records (EHRs), laboratories, pharmacies, and medical imaging tools.
- Provide real-time access to patient information, presenting healthcare professionals with the latest data during patient visits.
- Support care coordination and continuity by ensuring that patient data is accessible across different healthcare settings and providers.
- Offer a comprehensive and standardized dataset for analysis. This data can be used for population health management, quality improvement initiatives, clinical research studies, and outcomes analysis.
- Implement access controls, audit trails, and data encryption to safeguard patient information.
Use of an ODS in a financial institution for real-time fraud detection
A financial institution can use an ODS to:
- Continuously monitor incoming transactions and customer behaviors in real time. By applying rule-based and anomaly detection algorithms, the ODS can identify suspicious activities that indicate potential fraud and alert investigators.
- Gain a unified view of customer transactions and activities.
- Correlate and analyze data across many channels to identify cross-channel fraud patterns.
- Calculate real-time risk scores for individual transactions or customer profiles based on predefined rules and risk models.
- Enable fraud analysts to perform retrospective analysis and forensic investigations.
- Block or flag potentially fraudulent transactions in real-time via integrations with transactional systems.
Operational data stores play a crucial role in data engineering and data management. They centralize data from various operational systems and provide near-real-time access to critical business data.
When used as part of a comprehensive data architecture, it can enhance data integration and data quality. This enables businesses to make informed decisions, optimize business processes, and improve overall efficiency.
However, implementing an operational data store comes with challenges. Organizations must design an efficient ODS architecture with clear data integration and data governance processes to ensure success.
You can learn more about data integration and how to gain valuable insights by reading our blog.