Operational Data Store Best Practices

Operational Data Store Best Practices

An operational data store (ODS) collects data from multiple sources into a central database for reporting purposes. It helps businesses to make more informed, tactical business decisions.

The availability of the ODS and the accuracy and consistency of the data it provides can have a significant effect on business decisions. This is why using best practices when implementing an ODS is important. There are various questions businesses need to answer if they want to get the best use out of an ODS.

What is an Operational Data Store (ODS)?

An ODS integrates data from multiple different systems of record. It contains the most current data from the different systems and allows for more comprehensive reporting. Data in the ODS is refreshed on a daily or hourly basis or even on a more frequent basis to offer a real-time or near real-time view. The data in the ODS is optimized for making simple queries on small sets of data.

A Modern ODS

A traditional ODS has a number of challenges, especially for businesses that want to use digital applications because they require scalability, speed and agility. It is usually based on a relational database which is an issue when handling large data amounts and needing low latency.

A modern operational data store solves these problems, providing the performance, low latency and scalability that digital applications require today.

A modern ODS offers fast access to real-time data. The API layer is decoupled from the systems of record and this means applications are available consistently and function even if one system goes down. High availability is crucial today when customers expect quick responses.

Infrastructure can scale to accommodate peak volumes and back-end systems aren’t overloaded with excessive workloads.

A distributed in-memory core means many users can use apps concurrently without this affecting performance.

Identify Why an ODS is Required

Organizations need to identify reasons why an ODS is necessary. Here are some key reasons for needing an ODS.

  • It is unproblematic to handle huge data transactions as it does not involve a large amount of historical data.
  • Reports are more comprehensive when they are based on an overall view of data than when based on separate systems.
  • An ODS helps with real-time analysis and tactical decision-making as it offers a current snapshot of data available in a central repository from the different systems of record.
  • Only a few people have the security to access source systems and with an ODS, more people can have access to generate reports as it does not contain all the historical data, which makes it resilient to data hacking and cyber attacks.
  • Integrated data from source systems offer a better picture of the customer as it includes data like support history, contact details, and order information. This enables better customer service, an essential requirement to remain competitive today.
  • Back-up and recovery are effortless due to the small size.
  • By increasing operational efficiency, an ODS helps to reduce costs.

Analyze and Understand Data Needs

Understanding company data involves answering various questions. Different businesses require different solutions and so it is important to understand what data sources matter. What existing tools are in place to collect and analyze data? Are they sufficient or not? Which data needs to be highly available and accessible? If the business requires real-time analytic insights from data, how will they be used?

Decide on Degree of Integration and Transformation

The ODS should be designed and built based on the functional requirements of the business. The data in an ODS is subject-oriented, which means it relates to a specific area of business.

In order to get maximum value from the data in an ODS, it must be clean, accurate and consistent. This means it goes through an ETL process (extract, transform load) as it comes from all the different sources so it is in a meaningful format for use. Systematic business rules must be applied to it based on the policies for data control.

Find the Best Method of Implementation

Industries like healthcare and education still use legacy platforms. Big data in healthcare is an issue and changes need to be made but it is difficult for companies to discard what they are using and start from scratch.

It is not necessary for businesses to rip out and replace what they are already using. If they have a traditional ODS, they can augment it with missing layers such as event-driven architecture, a smart cache, analytics and microservices APIs.

An agile approach is an iterative one that allows solutions to evolve in response to business requirements and focus on current business problems.

Businesses that are not already using an ODS can deploy a solution that includes all the components they need.

Decide How Important Real-Time Analytics Are For The Business

A traditional ODS is usually refreshed on an hourly or daily basis. However, today’s need for real-time data makes this inadequate. The question businesses need to answer is what the ROI of a real-time refresh will be versus a less frequent update of the ODS. Real-time refreshing is more expensive but it allows organizations to take immediate action and it is therefore extremely useful in situations where timely action is required.

The Internet of Things (IoT) and mobile devices make real-time analytics important and enable businesses to react to data soon after it comes into a system. This makes it possible for businesses to predict a problem, such as when a device may fail and take corrective action before it happens. Real-time reporting also helps with things like analyzing trade risks and dynamic pricing.

Conclusion

A next-generation operational data store has many benefits in terms of scalability, agility and high availability. Best practices when implementing an ODS include deciding why it is necessary, analyzing business data and being guided by functional business needs. Businesses need to decide on factors such as how current the data in the ODS needs to be and the degree of transformation and integration they requ