For companies to fully capitalize on the capabilities that data and analytics can provide them, there are new skills, talent profiles, and positions that need to be available. In a recent Willis Towers Watson survey (see the full survey here), they cite a lack of expertise and staff as a major hindrance to carrier’s successfully implementing advanced analytics. The roles are already present in other industries that are analytics rich – a key to success is marrying the technical roles outlined below with insurance industry knowledge.
Data Engineer – Industry experience is that 80% of all analytical initiatives are consumed with data preparation and cleansing. This may seem astounding at face value, but, with so many legacy systems, the internal data that many companies have is not useable right out of the gate for analytical purposes. Data engineers are sophisticated analysts that transform messy data through techniques like ETL (extract, transform, and load) to make it useable. As an example, one client had 11 different definitions of “gender” – male, female, married, single, divorced, – as they pulled data from different systems. Data engineers need to understand the business context that they are supporting and how the data flows from source systems and business processes to wrangle the data in preparation for analytical purposes.
Data Scientist – The horsepower behind building predictive models that bring great insight from the existing data are data scientists. These professionals are trained on different statistical modeling techniques (e.g., generalized linear models, gradient boosting) and programming languages like R or python. Data scientists are not unique to the insurance industry – Facebook is actually credited with hiring the first official data scientists. The most effective data scientists for carriers are those that have the technical modeling capabilities, but also have industry domain expertise. In an industry as regulated as insurance, just because a variable may be predictive of loss results does not mean it will be permitted to be used.
Business Intelligence Analyst – Seeing is believing! Instead of reviewing information in rows and columns, business intelligence experts can bring data to life by showing the insight visually. Business intelligence analysts transform data into actionable insight to help companies improve their ability (and speed) in making data-driven decisions. Using powerful software like Tableau or Power BI, these professionals can create interactive dashboards that bring together the critical business questions with the underlying available data. (see an example here)
Product Manager – Product managers lead the efforts to optimize the profitable growth of a business by managing the portfolio of risks. Blending command of the underlying analytics with an understanding of the competitive marketplace, the product manager partners with cross-functional teams (both new positions as well as existing expertise in underwriting and actuarial) to consistently improve the top and bottom-line performance of the business. The product manager identifies segments within a given market where underwriting margins are the most attractive and positions the portfolio accordingly.
The collaboration between product managers and underwriters is critical to a company’s success. If the underwriter selects individual stocks, the product manager is the overall portfolio manager. In those segments where the amount of data is huge and the homogeneity of risks is high, like Personal Lines, the product manager is best positioned to manage the portfolio. In large accounts with so much risk variation and customization of offering, the underwriter will manage the portfolio. The balance between the two then varies as the size and complexity falls between these two ends. The critical component is collaboration as the skills and expertise are highly complementary.
The talent drain that the industry is going to feel on traditional roles likes actuaries and underwriters is well documented and many companies are already aggressively addressing these needs. For the industry to truly thrive, we are going to need to replace all of these talented professionals as well as attract a whole new type of talent like data scientists, data engineers, and product managers to drive the industry forward.