How to Use an Operational Data Store for Maximum Efficiency

Operational Data Stores are increasingly growing in popularity due to their versatile and flexible implementation. Be that as it may, there are some recommended usage cases for this technology. On the other hand, there are some scenarios that might not be better suited by being resolved using Operational Data Stores. This might beg the question, how should you use an operational data store? Find out the best practices of implementing an operational data store, as well as a brief rundown of this technology and why it is important to implement it in some cases.

What is an Operational Data Store?

Operational Data Stores are temporary storage locations that use caching to store the most relevant insights at that particular time frame. This data management solution helps enterprise applications process requests quicker by layering data access. As a result, operational data is not searched for throughout a large dataset which takes time and creates an unpleasant user experience.

Instead, with an ODS system, the data is easily and quickly accessible from the layered store, which stores a small fraction of information operational at that time. From then on, the data is removed from the ODS and is stored in either a warehouse or removed completely from the system. This is why ODS can be likened to a caching system that stores temporary operational files on a machine’s RAM.

When to use an ODS

The key question to finding out how to use an ODS is when should it be used? The applications of operational data stores seem to be endless, which might make it hard to answer this question. However, there is a general rule of thumb when it comes to using operational data stores. The principle is based on the application you are designing.

If an application needs to access specific data within a large storage location without compromising the loading speed, that is when you should implement this technology. Alternatively, if you gather data from multiple sources, the best way to expedite the processing period is by implementing an ODS.

Best practices of implementing this technology effectively

When implementing this technology, you should have an Operational Data Store strategy on how it will all work. This is especially important when designing a more elaborate data access layer. You should plan the architecture carefully, taking into consideration all the aspects involved.

These aspects could involve the number of data sources and their type. Also, understanding where event-based integration systems and APIs go is crucial to implementing ODS systems in a layered data access architecture. If the data sources you’re using are linear, using an ODS might be overkill, so ensure that you really do need this management system before planning and executing everything.

Using ODS systems for analytical processing

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Analytical processing can get very complex, especially when using disparate sources to generate unified reports with actionable Business Intelligence. When looking for real-time analytical reporting, it is important to simplify the data management system to pull only relevant insights. Implementing an ODS system to power analytical systems can help streamline the flow of data, which simplifies the input of insights to the BI tool.

As a result, using an ODS system can improve the accuracy of Business Intelligence while also reducing the latency. Subsequently, you can gain access to insights in real-time without any delay to make tactical business decisions that are time-sensitive. Integrating these systems is very simple and straightforward since most BI tools are cloud-based and so is an ODS system.

Implementing an Operational Data Store for transaction processing

Platforms with a high frequency of transactions such as online shops, gambling apps, and websites, as well as trading platforms, need instantaneous data processing. This makes them a prime target for Operational Data Store implementation since ODS systems help expedite such online transactions. ODS systems expedite online transactions by collecting operational insights of the customer and caching them.

Whenever the customer wants to checkout or completes a transaction, all the pertinent details are easily accessible. Details such as banking information and shipping address are all rapidly accessible, which significantly reduces waiting periods when checking out. You can implement this by layering your data access specifically for transaction processing purposes.

Integrating hybrid data storage systems

ODS systems are highly advanced data management systems because they do not only collect insights from disparate sources but also facilitate hybrid storage locations. If you’re migrating to the cloud but wouldn’t like to rip all the data and upload it on the cloud, having a hybrid system can help.

Ripping the data all at once and uploading it to the cloud could lead to some downtime on your application. Therefore, going with a phased approach can help facilitate the migration without losing operations for a while. An ODS can be used in that case to integrate the data storage systems to be available in parallel while the data is uploaded to the cloud.

Importance of ODS systems

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ODS systems are very important in a variety of industries since they facilitate expedited performance which improves user experiences. At the same time, using an ODS improves the quality of Business Intelligence insights you get. Not to mention that when you have a hybrid system or disparate sources, this data management strategy can aggregate the data onto one store.

When developing enterprise-grade applications, implementing an ODS system is very important, especially when processing large amounts of data at once. You can use operational data sources to glean the current and relevant insights and input to the relevant applications. Application developers should consider using ODS data management systems when their project meets the above mentioned criteria.

The bottom line

Using an operational data store can help your company’s applications and analytical reporting in more ways than one. However, you should know when to use this technology and the best practices to implement it. The key to both of these questions is determining your requirements and carefully planning the data access layering architecture.

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