The predictive models of the future
We are moving into a new era in connectivity that will be characterized by the proliferation of billions of connected devices all over the world. Most of this growth comes from machine-to-machine: linked sensors, cameras, and complex peripherals. The number of these devices is rocketing around us, together with new and unusual ways to blend these streams of information into a cohesive intelligence layer we can interact with in our daily lives. GSMA estimates they will be able to connect 24 billion devices by 2020, while Cisco and Ericsson think we will hit 50 billion and this will affect the real-time availability of information, the well- known "big data" phenomenon.
But most companies cannot deal with the speed at which these big data are emerging. Adding to the challenge is the nature of these data: only 20% of it is structured. This means 80% of our data is unstructured, and not stored in the friendly and manageable confines of a database.
For this reason, Business analysts use algorithmic analysis of big data to create data aggregations from which it is possible to extract information about market trends as well as about individual consumers. We may need a further extended data logical model to manage the complexity of this phenomenon and, in particular, to manage the different data.
The implementation of an extended data model should be hybrid as only a part of the data information can be gathered from the inside of the company, while the majority of the data will remain external. Indeed, the data should be accessed by the company from both the internal and the external sources in an "agnostic" way: in this way the possible changes of the underlying data sources would not impact the Company systems.
Thus, processing these data will imply the possibility of creating countless opportunities not only to understand the current context but above all to anticipate the market evolution and the consumer needs.
The involvement and benefits for the Insurance Business will be:
- business agility, with the development of innovative products and services and increase in the technical quality
- sales model evolution and sales process simplification
- claims efficiency
Indeed, all insurance companies are now aggressively adopting big data analytics for use in a variety of areas, particularly marketing and claims.
In the coming years, big data analytics is going to play an even more prominent role for all types of insurers. However, human interpretation will still be needed to interpret analytics and apply the results to real world situations that are far too.