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Focus: Three Keys to Effectively Handling Technological Advancements
Norbert Monfort, Vice President, IT Transformation and Innovation, Assurant Global Technology
Broad understanding means that we stay abreast of the evolution of new technologies, especially through the use cases being experimented or implemented. To this end, an insurance or financial services organization should not only use public information and research firm data but should also invest in InsureTechs and FinTechs related to the markets they serve if they have the venture capital resources. All of this helps keep a pulse on technologies to enable quicker decision making in future stages. As an example, an organization may be tracking and monitoring the evolution of Blockchain closely, but not make it a priority or area of focus until there is a compelling use case that brings value to the company and/or its customers.
An organization’s machine learning investment should remain within areas where exclusive data is available to solve specific business use cases and/or issues
Containerization in some organizations has been deemed to not be a good fit because its success is predicated on a large-scale commitment by both infrastructure teams as well as application development teams, and not all teams were willing to support it or fund it. Some companies skipped containerization and focused instead on utilizing cloud technologies such as FaaS (Functions as a Service) and PaaS (Platformas a Service) as these utilize containerization at scale butare supported by cloud providers that have already made the necessary large-scale commitment. In terms of Machine Learning, an organization must ensure that the right data is available for the use case being pursued. Understanding this from inception allows experimentation in areas where the data does exist, thereby resulting in successful rollouts of this technology.
Focused scaling is the final principle. When an organization is confident that a technology will bring value, then the focus must shift to scaling the solution. As an example, once it has been determined that machine learning will bring value to the company, an investment should be made in the platform that allows data scientists to develop models more quickly. Similarly, with RPA, once value is established, an organization should invest in a platform and a partner that brings scalability to future deployments. This scalability must also bring high utilization. Any areas where an organization does not have unique data that brings competitive advantage (i.e., in data security, HR, Finance, etc.) will be better supported via third party products or services. An organization’s machine learning investment should remain within areas where exclusive data is available to solve specific business use cases and/or issues.
While these principles may seem like common sense, developing the discipline to stick to them amidst competing internal forces pushing for a favorite new technology is a challenge that most organizations face. Still, these principles must be a starting place in order to bring discussions and investments back to a common foundation for decision-making. Only then can an organization effectively adjust to the ever-changing tech industry landscape.