1. VOLUME – The term ‘big data’ comprising both structured and unstructured can be vague and immeasurable since insurers differ in their storage and analytics capabilities. Thus, what may seem huge for a small local insurer, maybe a drop in the ocean for a multinational corporation. The volume of big data is decided by insurers when reviewing their data storage systems. For example, on-premise or cloud and the various kinds of data utilized. It can impact many things, such as the carriers processing power and security.
2. VELOCITY – Large amounts of data need to be generated, collected, and processed quickly; this is where the speed is critical. Forbes suggests the production of 2.5 quintillion bytes of data a day, and insurers must have systems to aggregate such data volumes to ensure data analytics perform at the optimum.
3. VARIETY – More than the quantity of big data, the type of information available for insurers to derive insights from is critical. While structured data like texts and numbers are easy to derive, with NLP-based AI engines such as Charlee.ai, there are more profound insights and patterns that predictive analytics can unlock. As a result, insurers can use unconventional information to design policies, detect fraud, and enhance the overall customer experience. What insurers thus get is a complete picture of every customer, their past behaviors, claims cycles, the behavior of individual claims, and so much more, all derived from continuous access to unstructured data.