[:en]Today Insurers have the chance to positively reinvent themselves.
In recent years the opportunities offered by the availability of the data, whose amount has risen from 2.6 to 276 billion gigabytes in the last 20 years, as well as the new IT technologies developments to use them, have incredibly increased. In such a huge volume of insightful information, managers have to carefully pick the useful ones that allow faster decisions with fewer risks, avoiding at the same time the unnecessary ones that might mislead choices and planning.
Big Data industry should be a top priority investment for Insurers that want to use data as a clear competitive advantage. In a scenario of constant increase of insurance service providers and growth of competitiveness, exploiting the most of data related technologies, can be the way to stay profitable in the long term.
How can an Insurer take advantage of these new kind of data? Just think about the wearable technology market: the company has the opportunity to track the behavior of customers about their health habits. Or think about the car telematics insurance that provides accurate information about breaking speed limits or unsafe driving. With a 360-degree customer profile, insurers now have the chance to refine their approach to sales, marketing and existing customer services by creating customized products through a meticulous Big Data analysis.
Investing in these technologies also means investing in people who are able to make the best out of such insightful information and with a solid knowledge of both data and science, in order to create a real competitive advantage from their analysis.
The person who seems to better match these features is the Data Scientist. Big Data can be used to gain more insights and solve many issues, but only if the people deputed to work on them are properly trained with the right skills to ask the right questions. Data scientist has a deep understanding of human behavior, finance, economics, technology and analytics; he also owns strong communication skills and is willing to support the business decision-making process, in order to achieve the market goals and positively influence the company strategy faster than the less well-prepared companies. This means that the good data scientist also needs to have an aptitude to dig under the surface of the data to discover the most useful ones, set up hypotheses to be tested and the ability to make these data “talk”. He turns information into actions. The feature useful to identify the best data scientist is of course his capability to work with a huge amount of data, combined with a predisposition to work in team to better mix different abilities.
The issue here is to realize that, at the moment, there are not enough data scientists on the market, given the high need and growing demand.
A McKinsey report estimates that there will be 140.000 to 190.000 unfilled positions of U.S. data analytics experts by 2018 and the demand for them is predicted to continue rising sharply, while already ranking at the top of the best jobs list for 2016. In response to this situation, universities are implementing their offering of degree programs with the aim to train people on data science.
[pullquote position=”right”]Today insurers should spend time and effort to develop a great data scientists pool and to decide how to fit them in the organization, as these are the keys to remain competitive[/pullquote]. In order to do so, companies have different possibilities, such as acquiring, an expensive but fairly quick option, renting -through crowdsourcing, freelancers or consulting firms- or running competitions to spot the best talents.
The best solution to keep a company ahead for a longer period would probably be a “built in” office, specifically created within the organization and filled with individuals who have at least some of the mentioned skills and can be trained in the other areas. Last but not least, considering the different kind of approaches, companies should keep in mind their strategy, goals and capabilities.
Data scientist is becoming the prerequisite for big data projects and we have just begun to codify the role.[:]