Four Steps to Insurance Data Mastery

In an increasingly dynamic and volatile insurance industry environment, the importance of data has only increased. And as traditional and non-traditional, real-time data volumes continue to grow exponentially, insurance organizations that have developed strong data and analytics capabilities continue to win market share and grow profitably.

While certainly many insurers are already channeling the insights obtained from proliferating data volumes to accelerate growth, the relative maturity of these efforts varies widely. Indeed, the research behind Capgemini Research Institute’s recently released report, “The data-powered insurer: Unlocking the data premium at speed and scale,” found that only 41% of insurance organizations ensure that their data executives align organizational data/analytics strategy with overall business strategy.

Further, only 18% of insurance organizations had the tools, technologies, people, processes, skills, and culture in place to support data-driven programs that derive full value from the growing volume of data. We call such organizations “Insurance Data Masters.”

How can insurance organizations achieve data mastery?

What are these insurance organizations doing differently, and how can data mastery be achieved? To become data-powered, insurers must align and invest in four key areas:

1. Build the infrastructure to allow rapid implementation of data-derived insight.

According to our survey, lack of agility in IT infrastructure and legacy systems and monolithic architecture are the top two technical challenges faced by insurers, compromising timely access to required data and a truly unified view of risk. Data Masters, in contrast, invest in superior technology platforms, such as building an industrialized cloud platform, a continuous integration and development environment, or API-based architecture. As part of building their modern technology platforms, they often:

·     Create a unified, 360-degree view of data through centralized systems: build a central repository of data-based solutions, accelerate cloud migration activities, and broaden the use of APIs to connect disparate systems.

·     Share and ingest relevant data from brokers/agents and reinsurers through appropriate platforms and APIs; partner with ecosystem players to standardize data-sharing across the industry.

2. Establish an appropriate operating model to scale data-driven insurance use cases.

Accelerating the development speed of use cases will not be enough; data teams must be aligned on business objectives. As our research shows, only one in ten organizations has been able to scale any particular use case fully. Insurers can take the following steps to optimize their scaling of data-driven use cases:

·     Create a hub-and-spoke model to democratize use cases: Set up a central team headed by a chief data/analytics officer, decentralize implementation of use cases at the business-unit level, and establish central facilitator teams, such as a data center of excellence.

·     Create dedicated roles for data stewards and owners; increasingly, there is a need for distinct roles to manage, process, and establish trust in all data. “Data owners” need to develop an understanding of which datasets feed into analytics solutions and how these link to business outcomes, improve data quality, and serve as “data evangelists” democratizing data and building meta-data libraries.

3. Foster a strong data culture across the organization.

Insurers have, for decades, been drawing on historical data in risk and actuarial organizations. A truly data-powered organization, however, infuses data into all other functions too, relying less on experience-based judgments, and integrates new sources of data to enrich insights. However, based on our research, fewer than half (48%) of insurance organizations invest in their data cultures by enabling employees with the skills and tools to generate and apply insights. Consider the following to improve the health of your data culture:

·     Adopt agile ways of working: in an agile culture, business teams can work alongside data experts to build and test new ideas.

·     Empower employees at all levels with tools and skills to apply data by deployment of self-service analytical tools and up-skilling/re-skilling existing business-unit employees to enable data fluency.

·     Ensure robust policies and processes for data ethics and security.

4. Orchestrate an open-data ecosystem.

Insurance organizations need to develop and tap into external ecosystems to attain a data advantage over their peers. What might this involve?

·     Participate in data ecosystems: form a team to define the value proposition, sharing formats, and monetization; identify datasets that can be shared and trusted partners; run small pilots to validate potential value estimates; and monitor and measure success and mutual value.

·     Collaborate with InsurTechs to help develop new digital offerings and accelerate time to market, and to increase customer reach and convenience.

·     Establish a data-driven approach to incorporating sustainability: establish a climate-action competence, aided by actionable data; increase awareness and understanding of climate risk in the organization; and leverage advanced analytics, AI, and machine learning to model and mitigate climate risk.

Move your organization up the data maturity curve.

Certainly, achieving each of these four recommendations requires significant time, focus, and resources. And while perhaps daunting for an organization just beginning its data-powered insurer journey, substantial rewards can be realized, even during the transformation process, to those who embrace it fully. Our research on data-powered organizations found that Insurance Data Masters earned 175% higher revenue per employee and were 63% more profitable than the average insurer during FY19-20 – these findings seem to point to a good potential return on your investment.