The primary obstacle to becoming a data-driven business is that data is not readily available, leaving valuable insights unused in data silos. To overcome this hurdle, today’s companies are creating a new role: Chief Data Officers (CDO). Responsible for unlocking insights hidden in data silos, the CDO is tasked with
Since the idea of an “IoT analytical lifecycle,” may be understood in many different ways, let’s start with a definition. Performing analytics at the data center and the cloud is well established practice, and is still quite relevant. With growing numbers of connected devices and availability of computing capabilities at
The analytical lifecycle is iterative and interactive in nature. The process is not a one and done exercise, insurance companies need to continuously evaluate and manage its growing model portfolio. In the last of four articles on the analytical lifecycle, this blog will cover the model management process. Model management
Advances in technology, evolution of the distribution channels, demographic shift, economic conditions and regulations changes. How does an insurer prioritize all these seemingly competing goals and create sustainable competitive advantage. One answer is analytics. Many insurance companies are just beginning to take steps toward becoming an “analytic insurer” – one
