Operational Data Stores are very popular because of their versatile use cases. Some applications of this technology might seem a bit impossible; for example, using Operational Data Stores in the insurance sector seems impractical.
The insurance sector is known for being a little slow-paced and laid back. However, it does need some high-tech data storage and processing. This is where operational data stores come into the picture. There are various use cases for Operational Data Stores in the insurance sector. Learn more about how this technology is revolutionizing the insurance industry and its future.
Snapshot of Operational Data Stores
Operational Data Stores (ODS) are highly advanced data management systems that help reduce latency and aid with real-time statistics. This cutting-edge data management system is offered as a Software as a Service. Since it is hosted in the cloud, ODS systems are available to any industry as an off-shelf solution to all their data processing woes.
Operational data stores only show insights that are current and relevant at that time. Therefore, they can be regarded as a short-term data storage location aimed at just hastening up the data processing periods. These systems that are getting very popular are starting to infest almost all industries, including the insurance sector. It is surprising to learn of the applications of ODS systems in the insurance industry, especially since it does seem to be a highly tech-centric sector.
Implementation of ODS systems in this sector
There are various applications of ODS systems in the insurance sector and they are aimed at improving internal and external aspects of the business. For example, some applications of ODS might be aimed at improving the performance of a companies registration website. If the insurance company has an app that serves as a dashboard for customers, having the most current insights can help improve their service.
In that way, ODS systems come into the picture since they solve these exact problems. By having operational data on hand, the company’s website can reduce its loading waiting period. At the same time, if the company has an app, those insights will also help improve the performance thereof since it won’t fetch data directly from the database. In essence, it bridges the latency gap that many apps suffer from.
Using OLAP in the insurance industry
Online Analytical Processing (OLAP) has been the pride and joy of ODS developers since it encompasses a wide variety of data management tricks. One of the tricks that are under this technology is data mining in Operational Data Stores. Data is very important in the insurance field for numerous reasons. Having mined data can help boost cybersecurity efforts by creating a user pattern that is normal.
This would help pick up on any anomalies and focus on patching up any weaknesses in the system. At the same time, data mining can help with marketing and sales efforts. By providing users with personalized offerings, insurance companies can boost sales and improve their revenue streams. There are many other applications of OLAP in the insurance sector that help boost operations and the overall bottom line.
Another benefit of using Operational Data Stores in this sector is the ability to do accurate predictive analysis. Sometimes these types of insights are crucial when making business decisions. Fortunately, ODS solutions facilitate the use of Business Intelligence tools that pose what-if scenarios. This helps the decision-makers see the holistic impact of that particular decision and whether or not it is worth doing.
These decisions could be regarding new product offers that can be assessed by comparing the current consumer preferences. All of this data is available in operational data stores and it helps the Business Intelligence tool have an accurate view of the company at that time. As a result, insurance companies can carefully plan a proportionate response to whatever problem or proposal they have on the table.
Simplifying real-time reports
One of the key applications of ODS in almost any industry, including the insurance sector, is the ability to generate real-time reports. ODS solutions are not like other data storage systems that archive historical data but instead, this system focuses on only operational insights. If data is not important to the current operations, it gets erased from this data store and stored in a warehousing solution.
This key feature allows companies that use this solution to have more accurate real-time reports. Real-time reports do not need large amounts of historical archived data. If there is historical data, the outcomes become inaccurate. However, since this system erases such data, the accuracy of the reports is improved exponentially. It also helps improve the time it takes the system you are using to generate the report since there isn’t a lot of data to sift through.
Benefits of implementing ODS technology
What are the overall benefits of implementing ODS technology in the insurance sector? The current benefits of implementing this technology include an overall improved data management system which created a positive domino effect to other aspects. For example, operational data stores impact more than just one aspect of insurance business dealings.
This technology has a direct bearing on internal real-time report accuracy. At the same time, it helps boost user experiences on company websites or mobile applications. It offers robust solutions to a wide range of different problems instead of just focusing solely on one set of solutions. Also, it is very easy to set up, especially if it is a cloud-based IaaS and SaaS solution. There is no need to worry about setting it up yourself because it comes as an off-shelf solution.
The bottom line
ODS technology can be used in the insurance sector and it has so many different applications that range from boosting cybersecurity to improving website and app performance. Its main benefit is reducing latency and boosting current performance while offering a generally improved data management solution. Insurance companies can even use predictive analysis to boost their decision-making when dealing with proposals or problems.