Operational Data Store Expansion and Modernization

Businesses use an operational data store (ODS) to extract data from various systems of record and integrate it into a central repository. The traditional ODS did not manage as much data or handle such diverse data as an ODS is required to do today.

The velocity, variety, and volume of data that users want to access today has far exceeded what systems were designed to handle even a few years ago. This is why users need to expand and modernize the ODS to reduce the limitations and optimize its use.

What is an operational data store?

An operational data store (ODS) does not handle a large quantity of historical data like a data warehouse and isn’t built for handling complex queries. As the data comes from multiple sources, integrating it often involves cleaning it, resolving redundancy, and checking it against business rules for integrity.

Unlike a data warehouse, new data that comes into an ODS overwrites existing data. The data in an ODS does not go back to operational systems but it may be passed on to the data warehouse. The data exists in a volatile, integrated state and contains the specific level of detail required to support operational business functions.

How does an ODS help in business?

An ODS has many practical uses in business.

Tactical decision-making: It offers a snapshot of the most current data at a given moment and supports simple queries to help with tactical decision-making. The design of the ODS allows for querying of data in real-time or near real-time.

Better reporting: The ODS provides a comprehensive overview of operational processes instead of siloed versions of data. This allows for more sophisticated reporting than reports from individual underlying systems of record. The ODS also allows for wider reporting capabilities because it doesn’t contain historic data and is more secure from cyber-attacks.

Easier problem diagnosis: Employees can identify issues and troubleshoot without going into component systems. For example, they can detect issues relating to a customer order and address this before it becomes a problem.

Time-sensitive business rules: An ODS contains rules, such as those that can automatically notify an organization when a customer has overdrawn an account. These business rules can greatly increase efficiency.

Why modernization of the traditional ODS is necessary

Accessing legacy systems, bringing data together, and making it easily accessible has been a common goal for many years but there have been various hurdles to overcome.

Legacy platforms have been built over years, and data is often fragmented across the organizations with no coherent data strategy. Some industries, like Fintech, are not as encumbered by legacy platforms and they can modernize more rapidly.

There is an increasing need to unlock previously siloed data to offer real-time insights for a number of different business purposes, including personalization for retail purposes and optimization of production processes. By gaining flexibility and scalability that’s limited in a traditional ODS, users can view operational data processing in a whole new way, one with many more possibilities.

Ecommerce marketing in 2021 is more popular than ever and customers have far greater expectations than they did in the past. Successful modernization depends upon the ability to harness the power of data to achieve operational excellence, enable valuable insights for a business and provide a superior customer experience.

Specific problems that need addressing

Latency: Latency is defined as the time it takes for a request to travel from a sender to a receiver and for the receiver to process it. One of the problems faced by the traditional ODS is due to the huge increase in data. Traditional systems experience high latency when handling large amounts of data. This means they cannot support applications that demand low latency.

Scalability: Another problem is one of scalability. The traditional ODS is unable to cope when many users concurrently access data. Peaks in user volume affect back-end systems, impact performance and customers don’t have the response time speed they expect. The infrastructure needs to be able to scale to accommodate peak volumes and unexpected loads.

Refresh time: As the traditional ODS is only refreshed periodically, the insights it can provide are limited. The explosion of digital applications means that data has to be real-time or as close to real-time as possible. Access to real-time or near real-time data enables organizations to use predictive modeling effectively and make better business decisions regarding issues such as pricing, fraud and trade risk analysis.

Hybrid deployments: Hybrid cloud deployments are popular today and data often needs replicating on-premise, in the cloud, and sometimes a number of cloud deployments in various regions. The ODS has to replicate data in real-time without impacting performance.

How to go about modernizing an ODS

Transformation budgets may be limited and prevent organizations from taking action when it comes to modernizing an ODS. They may fear that they will have to replace their ODS with a new one but this is not always the case. Those that have a traditional ODS in place can add missing layers, such as a smart cache, event-driven architecture, analytics and API microservices.

Using an in-memory data grid can solve many of the challenges of a traditional ODS and make it unnecessary to replace. A distributed in-memory core is inserted between the back-end systems and the API management layer. Decoupling the API layer from the systems of record offers high availability and applications continue to work even if a system of record isn’t available.

Decoupling architecture through API management, scaling with hybrid cloud data architecture, and incorporating open source technologies are just some of the popular ways in which organizations are achieving digital transformation today.

A final word

Modernizing an ODS helps organizations to overcome any limitations of a traditional ODS and achieve the speed and agility they need. Autonomous scaling guarantees effective performance without a need to over-provision with expensive resources. This can give organizations a competitive edge in the market. They can make better operational decisions that improve their productivity, enhance customer satisfaction and much more.

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