Businesses are faced with increasing amounts of data today but that data is useless to them unless they can gain insights from it. One of the main considerations they face apart from finding ways to process data quickly is to store it in such a way that it’s easy to access. The Operational Data Store (ODS) extracts data from various different sources and integrates it, offering businesses the chance to analyze and make informed business decisions. 

An introduction to the Operational Data Store

The basic purpose of an Operational Data Store is to integrate data from various systems of record to facilitate reporting in real-time or near real-time. Reporting is limited when data exists in different segregated systems but when it is integrated, reporting becomes more comprehensive. 

In essence, the ODS acts as a repository that stores a snapshot of the most current data of the business. As the data comes from more than one system, integrating it involves cleaning it, resolving redundancy and checking for integrity to ensure it obeys systematic business rules. 

The integration of data occurs quite frequently. It can happen on an hourly or daily basis or even straight after transactions take place. The data is loaded directly from operational data and not from the data warehouse. Reports are created on short time windows of data.

An ODS has to balance factors such as how often to refresh, the degree of transformation and integration of data required, and how current the data in the ODS needs to be. Depending on the application requirement, organizations may consider a number of refresh options. 

Characteristics of the Operational Data Store

• An ODS can be seen as a staging area for facilitating queries of a simple nature on small data sets. 
• It is highly available and offers easy access to real-time comprehensive data. By decoupling the API layer from the systems of record, it means applications are available all the time, even when one system goes down. 
• It does not occupy much space due to the compression of data and operations and creating back-ups is easy because of the small data size. 
• The data in the ODS is subject-oriented so it can support the functional requirements of a business. 
• Once data is cleaned and redundancies are resolved, it is loaded according to business rules for controlling and regulating data. 
• An ODS is highly volatile and does not store data history as its purpose is real-time analysis and strategy planning. Data is constantly updated and previous data is overwritten. 
• An ODS can act as a store for the data warehouse.
• An ODS is flexible because it is designed to ingest various data feeds. This means switching suppliers or source systems is easier to manage. 

A new generation operational data store

It is difficult to handle huge amounts of data and offer low latency at the same time and when many users access a data store concurrently, performance is slower. The refresh rate of a traditional ODS was good enough to serve end-of-day reporting but is not good enough for digital applications that need real-time data. 

Agility, speed, low latency and scalability are all important to drive business growth today and the traditional ODS has evolved to offer these qualities.

Organizations with a traditional ODS are augmenting it with missing layers rather than replacing it. They may add a smart cache, architecture that’s event-driven, and microservices API. This enables them to obtain real-time information so they can stay informed and make better decisions. 

A smart ODS is able to autonomously scale infrastructure up or out so it can accommodate unexpected loads and peak volumes. Businesses can avoid over-provisioning of expensive on-premise resources while still maintaining customer experience when volumes peak and adhering to SLAs. 

Back-end systems aren’t subjected to high workloads that can prevent quick response times. The speed and scale of a smart ODS support even very demanding digital applications. It also offers predictive modeling that is both robust and accurate. 

Reasons for implementing an ODS include:

• The reporting offered in source systems is limited and companies want to have more powerful, comprehensive reporting. 
• An ODS means more people can generate reports because access to source systems is limited for security reasons. 
• An ODS simplifies the process of diagnosing problems due to its view of the status of operations that makes it unnecessary to go into component systems. For example, service reps can locate a customer order, determine its status and troubleshoot effectively. 
• Gathering data from source systems provides a more integrated view of customers as it combines customer information, call logs, order information and support history. For businesses looking to provide outstanding customer support, this integrated view is important. 
• Integrating medical data can offer a more accurate view of a patient when it includes hospital records, pharmaceutical purchase records, diagnostic records etc.
• As an ODS works through time-sensitive business rules, it can improve efficiency. If the rules engine recommends an immediate action, that action is communicated to the appropriate enterprise system for execution. For example, a financial institution can receive an automatic alert if a customer account is overdrawn. 
• A company may own a number of retail stores that track orders in their own databases. Consolidating these databases can offer real-time inventory levels throughout the day. 
• A smart ODS can replicate data in real-time across sites and regions without impacting performance and with low network overhead. This helps global organizations, such as manufacturing companies and investment banks, with data centers in separate remote sites. 

Conclusion

An ODS can make current data analysis much easier and informed decision-making less complicated. It focuses on the operational requirements of a particular business process, such as customer service, and offers more flexibility and agility for running business operations. By modernizing their architecture with a next-generation operational data store, businesses can experience a great improvement in scalability, throughput, high availability and agility that drives innovation.