This chapter is from the book
This chapter is from the book
This chapter is from the book
Challenges
Yet as much as analytics are needed, analytic adoption often falls short because insurance stakeholders face a myriad of challenges using analytics.
First and foremost, insurance stakeholders often lack data access to complete, trusted, and understandable data. Insurers should strive not just for quality, but for trusted data that is well documented, that can be used for its intended purpose, and where any “nuances” in the data are understood. Data can also be augmented; for example, structured data can be augmented with unstructured data such as text data from customer surveys or customer sentiment data from social media. A solid data governance process can ensure the documentation of existing data and define new data needed. Often the best “home” for data governance in an insurance company is in actuarial or finance departments because both are responsible for internal and external reporting and have a high interest in data quality and integrity. Further, these areas often use data from across multiple business domains and are familiar with the challenges in integrating data.
A second hurdle is a lack of or limitation in analytic skills, which inhibits effective use of analytics. One cause is cultural, often due to a lack of awareness or perhaps lethargy. All too often we rely on gut instinct for decisions. We mistake reporting for analysis. Data exploration and data visualization, enabled by user-friendly tools, are helping enterprise employees improve their analysis and their analytic insights. In addition, organizations are hiring new employees with analytic skills and developing the skills of existing employees. Those organizations who invest in ongoing analytic skill development will see higher analytic adoption and increased employee satisfaction and retention.
Yet a third barrier is the analytic tools themselves. It’s important to select and use the right tool for the right analysis and the right user. At one extreme, executives want to use highly visual, summarized analytics through dashboards and mobile devices. At the other end, analysts are looking for robust tools that allow them to manipulate and merge or augment data, create new calculations, and perform complex analysis. “Data scientists,” who include marketing analysts, finance analysts, and actuarial analysts, want even more robust predictive and statistical capabilities. Many companies have formed BI Competency Centers or Centers of Excellence to provide training, encourage end user analytic tool adoption, and engage business users in the evaluation and selection of new tools as well as to define and execute an overall BI strategy and to provide overall governance.
Technologies continue to evolve. Enterprise information management tools allow companies to better access and integrate data; to scrub it, to understand its source, and to understand the impact on existing analytics if it is added to or changed. In memory, databases provide increased performance and speed for cranking through ever more granular data for actuarial pricing and reserving analysis. Geographic Positioning Systems (GPS) and geomapping technologies enable organizations to augment risk and loss data to better understand underwriting risk and loss patterns. Visualization tools enable users to make more sense of market and customer data. Organizations have access to machine-generated data through devices in automobile insurance; similarly, they can integrate data from medical devices providing biofeedback for health insurance and wellness monitoring and intervention. User interfaces go beyond the desktop or laptop, to mobile not just for field employees like agents, loss inspection, and claims professionals but for all employees—data anywhere, anytime, on any device making data security critical as data and analytics access become ubiquitous.