Unlike at companies in many other industries, insurance business models have remained relatively unchanged for many years. Even the impact of digital disruption on customer behavior was slow to catch up in the industry.
The inherently risk-averse culture of insurance companies has made them slow to evolve, but industry leaders began introducing the topic of transformation several years ago. Defining an approach to institutional data was among the first topics addressed for most companies.
Led primarily by traditional CIOs, many companies have created centralized data management, big data and information governance strategies and capabilities. To lead these functions, insurance companies have sought talent from adjacent industries that have led the way in data analytics.
This typically includes retail, financial services, information services and data companies. In some instances, Insurance companies have looked to executives coming out of healthcare organizations in order to leapfrog their own data capabilities.
Though the Insurance industry has made significant strides in terms of data capabilities, it continues to grapple with the challenges of organizational access, and structuring to maximize the impact of analytics.
For the past four years, analytics has been predominantly used in marketing and customer analytics for insurance. In an attempt to expand these capabilities, some organizations have tried to centralize all analytics functions at the enterprise level.
For most companies, this has proven to be a mistake. Instead, an analytics center of excellence approach to analytics is currently recognized as the best model. Within this structure, a centralized data repository owned by the CoE provides each business line and function access and ownership of analytics.
For example, this provides HR the opportunity to leverage data for people analytics, and react and respond real-time rather than waiting for a corporate strategy to message findings. Similarly, personal lines provide their own analytics that allow it to create predictive models which result in more targeted plans and policy owner engagement strategies. This model is often referred to as the ‘hub & spoke’ model for data and analytics.
Within the last year, there has been an increased focus on finding analytics leaders who can improve claims and operational efficiencies across the enterprise. As was true with the hunt for leading data executives, best-in-class analytics leaders are once again being recruited from retail, financial services, information services and data companies.
Though the target companies for these searches are the same, the competencies and experience that insurance organizations are now seeking are very different. Most are looking for leaders with the reputation for leading full-scale digital transformations and who have taken a ROI approach to data analytics.
Beyond deep-seated experience working with data, these leaders must bring strategic vision and judgement, problem solving and analytics skill sets, change management backgrounds, commercial acumen, the ability to build high performing teams, results driven mentalities, and most importantly, ability to influence and manage a diverse set of stakeholders.
(About the authors: Justin Cerilli and Giles Orringe are members of the financial services technology practice at executive search and leadership advisory firm Russell Reynolds Associates).