An ODS makes it possible for businesses to consolidate data from multiple sources and store it in one place in its original format, making it available for making business reports. Data in an ODS is current and is integrated from several business operational departments.
It helps support BI tools that help businesses make strategic decisions. It guarantees to make real-time queries and reports on operational data. Operational data stores have many uses in a business environment and here are some of its use examples.
Characteristics of operational data store
An ODS has several characteristics that qualify it to be called an ODS.
It is currently valued: an operational data store keeps updating information into its store and thus keeping it current all the time. No historical data can be found in an ODS and anytime a business wants to retrieve older data, they have to get it from other storage solutions.
Its data is subject-oriented: all data in an ODS system is organized according to data collected within a specific business. For example, an ODS system for a university will have its subjects as courses, students, or teachers. In the case of a business, subjects can be customers (users), products, services, and workers.
An ODS system is an integrated system: its entire system is composed of data from several subject-oriented systems.
It is a detail-oriented system: An ODS system contains enough data that can serve the needs of every department within a company.
It is volatile: To be volatile means the data is updated every time newer data is released in the ODS system to keep it ever current.
Operational Data Store Use Examples
The primary role of an ODS system is to enable data integration from multiple sources into a single entity. To achieve this role, it uses different technologies that enable your business to access data that can be useful in marketing your business.
Data virtualization
When companies want to get a clearer understanding of market trends, they can use data in various ways and one of them is data virtualization. Businesses integrate their data from several sources to produce real-time data from a centralized virtual view. This combined data has several use cases.
Data warehousing: it helps to store up-to-date data in an entire business while securing the underlying systems from suffering major impact. Businesses can use virtualization tools to reap every benefit that comes from a smart data warehouse cloud storage. These tools make it possible for businesses to replicate data from multiple sources into cloud storage without affecting the system’s uptime and performance.
Business intelligence and analysis: When data is integrated from multiple platforms like mainframe and cloud to a single view, businesses can use data virtualization tools to query data, and the system will directly query the relevant syntax, send the query and retrieve the result.
Self-service tools: The data virtualization self-service tools enable businesses to produce reports, view performance, do data analysis and ask ‘what if’ queries. These tools act as a catalyst to product creation, promotion and provide streamlined processes.
Application development: With data virtualization tools, programmers will require to use fewer codes and that can put more focus on creating applications that can provide total real-time data important for business growth.
Data extraction
When a business needs only a portion of data from cloud storage or data warehouse, data extraction technology becomes relevant. A business might have big real-time data in ODS, and the entire data might not be useful to a specific department.
If, for example, the sales department wants to retrieve specific information about a certain transaction, extraction tools in ODS can be used to retrieve that specific information. The big data might not be structured and extraction tools help add structure to the data through rules such as text analytics, pattern matching, and tables.
Data federation
Data federation is working more like data virtualization but the difference is that the former is software the enables system users to create a virtual information center where data can be collected, stored, and used for business transactions. Data stored in a data federation center is metadata and not the actual data.
Data federation infrastructure is appealing to the field of data governance. Other names used for this infrastructure are EII (Enterprise information integration) and IaaS (Infrastructure as a Service).
Advantages of using an operational data store
It enables access to current data: businesses use simple queries that contain all commands required to help obtain data from ODS. An ODS system is not complicated and gives easy access to the most up-to-date data, whether it is not aggregated or compressed.
Efficient communication: an ODS system enables smooth communication through CIT systems.
Up-to-date view: companies can access an up-to-date view of all their operations and can therefore detect errors fast and correct them before a customer learns about the error.
Operational data store use in daily business transactions
Many business fields are using ODS in their daily operations to enable fast, effective, and reliable transactions and the use of apps. For example, in the banking sector, ODS enables a broad spectrum of operations giving banks a 24 hour, 360 degrees’ view of every single transaction done in its branches, ATMs, and online banking applications.
Administrative data: Administrative data is another area of ODS use. Governments process a lot of data daily in their registration services, records storage, and government transactions. Through ODS, governments can store, retrieve, and use the data for analysis and projecting future projects.
Autonomous vehicles data: autonomous vehicles generate data every second and that data is required to be processed in real-time to help the car maneuver on the road without causing an accident. The data must remain current until the car completes its journey and ODS is providing a solution to this.
Universities and colleges: institutions of higher learning process a lot of information as they prepare courses, process admission information, and daily running of the institutions. Students apply online for admission, use apps, and make multiple queries on the institution’s website. ODS can allow these institutions to process and store large volumes of data without causing delays accessing their website.
