The Unsung Hero of Data Science: The Stakeholder

In the dynamic world of data science, much attention is often given to the intricate algorithms, the sophisticated models, and the technical prowess of data scientists and engineers. While these elements are undoubtedly crucial, there is an equally, if not more, vital player in the data science lifecycle: the stakeholder. Often overlooked, the stakeholder is the driving force behind successful data science implementation and the key to transforming concepts into tangible, impactful solutions, particularly in the life insurance technology space.

The Traditional Data Science Cycle

The traditional data science development and implementation cycle typically involves several key stages, each critical to ensuring the effective and responsible creation of data-driven life insurance solutions:

Problem Definition: This is the foundational step where stakeholders, including data scientists, business leaders, and domain experts, collaborate to clearly articulate the business problem or opportunity. In the context of life insurance, this might involve moving towards more data-driven underwriting, facilitating actionable insurance lifecycle analytics, developing the ability to personalize policy offerings, or even to help combat fraud. It’s essential to align the problem with broader strategic goals and to frame it in a way that lends itself to data-driven analysis.

Data Collection and Preparation: Once the problem is well-defined, the next step involves sourcing relevant data. This stage often requires significant effort to clean or transform raw data into a usable format. Addressing issues like missing values, inconsistencies, data silos, and privacy compliance helps ensure that the resulting dataset is both high-quality and ethically sound — both of which are crucial when establishing a life insurance data infrastructure.

Model Development: With a prepared dataset, data scientists can begin building the model. The methodology of the build will depend on the nature of the problem and the type of data available. This stage often involves iterative experimentation, engineering, and fine tuning to create a model that captures the complex patterns in the data.

Model Evaluation: Before deploying the model, it must be rigorously tested to ensure it performs as expected. Evaluation typically involves splitting the data into training and testing sets, using metrics such as accuracy, precision, recall, and a number of other variables depending on the task. Special care must be taken to avoid overfitting and to ensure the model generalizes well to new, unseen data. When it comes to life insurance technology, regulation and governance are paramount, so factors like fairness and bias assessment must be considered.

Deployment and Monitoring: Once validated, the model is integrated into production systems where it begins to inform real-world decisions, often in real-time. However, deployment is not the end of the cycle. Continuous monitoring is essential and regularly scheduled retraining is necessary to maintain performance, accuracy, and compliance, particularly as data, business needs, and regulations evolve.

The Stakeholder's Critical Role

The stakeholder, the individual or group who will ultimately benefit from the data science solution, brings a unique and indispensable perspective to the table. In our case, stakeholders are the leaders and experts inside carrier partner organizations. Here's why they are the most important individuals in the data science process:

Driving Momentum and Priority: Stakeholders are the ones who can champion the data science initiative within their organization. Their support and advocacy are essential for securing resources, gaining buy-in from other departments, and creating the long-term roadmap that ensures the effort remains a priority.

Defining the Problem and Success Metrics: Stakeholders possess deep domain knowledge and a clear understanding of the business problem. They can accurately define the problem, identify the key performance indicators (KPIs), and set realistic expectations. Absent their expertise and input, any effort will merely be a data science solution in search of a problem. That’s the thought behind Bestow’s collaborative partner approach, which seeks to truly understand stakeholder needs, rather than assume that a prefab solution will solve everything. 

Ensuring Long-Term Sustainability: When it comes to life insurance, data analytics is a long-term investment, stakeholder engagement is vital for sustained success. Their ongoing support and commitment are necessary to monitor the model's performance, make necessary adjustments, and ensure that it continues to deliver value over time.

Strong Partnership Makes The Difference

At Bestow, our approach to developing data-driven insurtech solutions is collaborative. Carriers come in all shapes and sizes, so rather than a one-size-fits-all approach, we center stakeholder needs to identify challenges and find opportunities to offer the right solutions. 

We’re more than just an insurance data platform, we’re a long-term partner. Rather than disappearing once the ink is dry, we’re invested in carrier success over time. As an example, we recently leveraged our data science expertise to help a carrier already in market further reduce their underwriting costs by 22%.

If you’re interested in learning more about our data science processes and products, and how Bestow can help your organization modernize and grow, email [email protected].