The world of insurance is slowly but surely embracing the significance and urgency of data science solutions to manage and leverage the data that’s available within the organization, as it is counter-intuitive for an insurance company not to be data-driven. However, most organizations do not have a concrete plan to become truly rooted in data science.
As data science strengthens its place on the executive table, it is necessary to develop a sound data science practice so that all available data – internal and external (public domain or permission-based) – is processed efficiently. The analytics derived from data can deliver predictions and recommendations that can affect the bottom line and market share of the organization.
There are a few nuances to setting up a data science practice, which when mastered, can deliver unprecedented benefits to the insurance organization. Let’s look at the core ingredients required for instilling a practice of data science in an insurance organization:
1. Follow a data-driven approach: Understand and evaluate the available data and identify use cases that can be effectively solved by using AI/ML on available data. This identification is important as not every use case can be solved using AI/ML; these technologies only work if there is a sufficient amount of useful data on which a model can be trained. Recommend significant data points, which when collected by an organization, can lead to massive improvements in predictions.
2. Build a repository of analytical tool-sets: A data science practice should have multiple techniques/tools available for data understanding and solution development. For example, for a use case where the amount of relevant data is not much, you may need to develop a predictive data model, whereas, for large data sets, a deep learning based model would be more effective.
You will need to possess an array of technical stack to deal with various data challenges so you can use the right tool to solve the right problem.
3. Focus on impact and ROI: Currently, AI/ML solutions are expensive to build (mainly because of resourcing and hardware/cloud cost); hence these solutions must deliver a tangible and meaningful impact. We need to devise techniques to measure the impact and build it into the process. It is possible to apply the AI/ML solution to an array of uses cases, though not all of them will deliver sufficient impact.
For example, if an AI data analytics solution delivers a prediction or recommendation by processing real-time information within minutes instead of a day, you know that your solution has successfully delivered tangible ROI and a meaningful business outcome.
4. Take measures to increase adoption by business users: The wide-spread adoption of AI/ML data solutions has one major challenge – the black box issue. Since the predictions are often based on algorithms that cannot be explained, business users have a hard time coming to terms with the way the outcome was derived. The best way to deal with this challenge is to start by implementing use cases that are easily understood and can be readily adopted by business users.
5. Create a data science workflow: Create a workflow that is complementary to your needs and makes the optimum use of your available resources.
While choosing an AI solution, make sure that it has the capability to access all your data silos.
6. Execute the plan: To ensure the success of the data practice set up, you need flawless execution. One of the best ways for creating such a plan is to break it down into sprints and assign timelines and stakeholders to all moving parts.
In today’s competitive landscape, just managing your insurance data efficiently may not be enough to stay relevant and competitive. Without a data practice, it is possible to miss out on insights that may be critical for business development, increasing market share, and revenue growth.