Organizational Implications for Insurers

Big Data has become an ubiquitous term attracting widespread interest in many sectors, and the insurance industry is certainly no exception. Already at the 2015 Insurance Government Leadership Network conference in London the magnitude of the disruption Big Data causes was pointed out by Henri de Castries, previous Chairman and CEO of AXA insurance, when he stated that Big Data “changes everything, it’s the equivalent to oil and electricity a century ago and printing five or six centuries ago”. But what is the hype all about? The insurance industry has always used data for business intelligence. Historical data has formed the basis for strategy, claims, underwriting, pricing, and product development. So, what is changing with Big Data? It’s the evolution from descriptive analytics to prescriptive analytics, putting organizations in the position to make decisions based on real-time data instead of historic data, experience, and gut feeling. Big Data is the foundation for machine learning and AI. It is the tool for insurers to reach a higher personalization and allow services to go beyond insuring risks by offering active risk management on a forward looking basis.

Challenges around the right IT infrastructure and access to credible and large enough data sets are widely discussed but topics that are often overlooked are internal, organizational requirements. An academic research I conducted in 2017, found that there is a need for organizational changes in structure, culture, skills, and leadership.

Structure

Centralized, cross-functional center of excellence

Changes to structures and processes are often necessary in order to fully adopt Big Data. Structures and processes tend to be very traditional in the (SEA) insurance industry and need to become less hierarchical in order for the adoption of Big Data to be successful because being hierarchical means that the time to respond to market changes is slow.

Apart from designing a leaner, more agile organization, Big Data needs to be somehow represented in an organization. Some managers have the perception that Big Data is the responsibility of the IT or marketing department but a centralized approach has proven to be more effective.

Big Data responsibilities can be organized in a center of excellence, a centralized team that works cross-functional. The advantage is to get a holistic view on Big Data and to deliver applications and insight to other departments in the organization. Responsibilities of those centralized teams include building the right infrastructure, developing Big Data analytic capabilities, and providing data for decision making to different areas of the insurance organization.

Skills

Migrate existing talent into Big Data talent

Skills is another important area when it comes to the adoption of Big Data. Essentially the current human capital is unable to support Big Data demands. Those centralized Big Data teams should consist to a certain degree of people with special skillsets in data science and data engineering. A problem often reported is that insurers are struggling to attract talents such as data scientists, especially in emerging Southeast Asian countries but also in Singapore and Hong Kong. Data Scientist was rated sexiest job of the 21st century and insurers are not only competing with their competitors but also with other industries that are often more attractive for those professionals.

In order to attract those talents around Big Data, insurers need to provide an environment where they can thrive. If talents do not have the right infrastructure and are not given responsibilities and the power to make changes, they will leave. Furthermore, the insurance industry needs to acknowledge the importance of those professions and treat and pay them accordingly.

Apart from the centralized teams, staff members across the organization need to develop new skills. Functions along the value chain will be impacted and processes will change due to real-time data-based decision making. People need to become more analytical and learn how to make sense of data.

Culture and Mindset

Corporate culture that embraces change

The other big challenge, or maybe even the biggest one, is culture and mindset. Insurers can invest in the latest technology and acquire the best talents available and still fail to successfully adopt Big Data because behavior is not changing. Behavior is tight-knit with culture and mindset and consists of assumptions, values, and beliefs. The insurance industry can be considered conservative which is often reflected by the people who work for insurance companies. They are not known for being creative and to embrace change. Innovations and new concepts, such as Big Data, cannot thrive in such an environment.

Insurers need to change their structure, change their culture not only to adopt Big Data but in general to be open for innovation and change. This is crucial for an industry that was rated number one sector ripe for disruption. However, changing a deep-seated culture is difficult and can cause resistance. Change management is therefore an important part when it comes to the adoption of Big Data.

Leadership

Strong leaders with a Big Data vision

Perhaps the most important factor of all is leadership. Change needs strong leaders, especially in Southeast Asian cultures with a collective mindset and high-power distance (Hofstede CREATE LINK TO https://en.wikipedia.org/wiki/Hofstede%27s_cultural_dimensions_theory). In order to maximize the benefits of adopting Big Data, leaders need to develop a strategic vision to successfully change structure, mindset and culture. A problem, however, is getting the management team aligned. Different parts in an organization view Big Data and its possible benefits differently. For example, the IT department often sees Big Data as a burden and as counterproductive to their cost-saving targets.

The other challenge is often a lack of understanding of Big Data. If the leaders do not understand the concept and implication of Big Data they are not able to design an effective strategy and Big Data implementation is not likely to be successful.