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The Four Vs of Data: Volume, Variety, Velocity, Veracity

Bill Jenkins | February 28, 2017

Traditional policy administration systems have been architected and designed to handle large volumes of operational data, in specified formats and to aggregate data to save storage space. Large volumes of non-operational data have traditionally not been captured and processed for various reasons, mostly because the costs to do so was viewed as far greater than the value of the insights the company could derive  from the use of this data.

As data has now come to be viewed as a means of competing in the insurance industry, carriers have focused on how data—all types of data—can be used to provide them a competitive advantage. Even though policy administrative systems and other insurance processing systems have enhanced their technical capabilities to become more agile, flexible, and robust, these systems continue to be hampered in their abilities to handle the characteristics of big data:

  • Volume: the amount of data being created and available is significant compared to previous  times
  • Variety: data now comes from multiple sources in multiple formats  and from multiple devices
  • Velocity: data is being streamed extremely fast, 24 by 7
  • Veracity: these multiple sources of data and volume and speed of this data poses quality challenges.

Few if any insurance processing systems can accommodate these characteristics as most systems have constraints in their ability to handle unstructured data, and to process and store large volumes of data at high speeds. These systems possess relational data bases and generally batch oriented in processing.

Systems based on older technology often include batch oriented processing. Newer systems tend to be built upon relational databases, which allow for new tables to be added easily and also include the ability to integrate with outside data sources. This provides a means for big data to be managed (interpreted, cleansed, analyzed, aggregated) and then matched and linked to policy administration databases.

To address the demand to use big data in order to gain better insight into company operations and enhance their predictive analytics capabilities, companies have begun to leverage the new software and infrastructure technologies being offered in the cloud. Use of cloud solutions like Hadoop for distributed storage and parallel processing of large data sets to store and process unstructured/big data at high speeds are now being leveraged by carriers, thereby making it easier for them to analyze and obtain value from this data.

Many organizations are looking to these cloud solutions to augment their core systems with pre-built storage, analytic solutions that also enable the company to have the scalability needed for big data. The availability of cloud processing does open up the ability to store and process “unlimited” amounts of data but it comes with its own set of concerns such as security, privacy, and what resources the company might need to manage these cloud solutions. Furthermore, processes need to be built to connect, integrate, interpret and use this information.

Technology is, however, not the only consideration a company must address. A company needs to take a holistic view and approach as it undertakes a big data initiative if it is to be successful. The components of people, process, technology, and data need to be addressed as these fit into the overall data initiative. This also means incorporating the developed information into the business routine and aligning its use to the business strategy so it adds value to the organization.

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