Exploring data analytics in the insurance sector

On October 4th, I joined the fantastic Jonas Piela to record the next episode of the “Digital Insurance Podcast” series.

Jonas hosts discussions with digital experts to analyze the ongoing digital transformation trends in the insurance industry, offering valuable insights for industry professionals seeking to comprehend the technology landscape and learn from best practices.

In the episode releasing today (October 19), Max Kossatz, Solutions Architect at RedHat and myself, were interviewed side-by-side to provide some clarity about data analytics concepts and solutions.

Data analytics in the context of insurance companies

The conversation kicked off with a key question: What falls under the umbrella of data analytics, and what are its key components? Within the realm of data analytics, two major areas stand out: Business Intelligence (BI) and Data Science.

Business Intelligence (BI)

Business Intelligence, which has gained significant traction over the last two to three decades, is primarily focused on the art of data visualization, report generation, and interactive dashboards. In the context of insurance companies, BI plays a pivotal role in presenting comprehensive data-driven insights to top-level executives, enabling them to make informed decisions and fostering the transformation of the organization into a data-driven enterprise. This involves the collection, analysis, and presentation of data in a way that enhances business strategies and helps in identifying emerging trends, customer behaviors, and areas for operational improvement. Essentially, it forms a critical foundation for integrating data-driven practices throughout their operations.

Data Science

On the flip side, Data Science is a relatively recent development that focuses on statistics, machine learning, and artificial intelligence (AI). It digs deeper into the data, seeking patterns and insights that can revolutionize the insurance industry. One practical outcome is the integration of predictive models directly into insurance products for the benefit of end customers. For instance, consider the automation of claims management, where advanced algorithms can handle the entire process seamlessly, making the experience faster and more efficient for policyholders. In essence, Data Science is all about harnessing the power of data to create smarter, more automated, and customer-centric insurance solutions.

Common data architectures in BI & Data Science

What are the most effective strategies and structures for implementing Business Intelligence (BI) and Data Science? Three of the most notable approaches include Data Warehouses, Data Lakes, and the emerging concept of Data Mesh. 

Data Warehousing

Data Warehousing, a technology introduced approximately three decades ago, revolves around consolidating various data assets from across the company into a single unified repository, often referred to as the ‘single source of truth.’ This approach involves creating a comprehensive relational data model from this stored data, which in turn forms the cornerstone for all data consumers and their analytical needs.

Data Lakes

Data Lakes were introduced to address certain limitations in terms of scalability and flexibility that were encountered with traditional Data Warehouses. Data Lakes rely on relatively cost-effective object storage and incorporate a separated compute layer, allowing them to seamlessly handle both structured and unstructured data. This inherent flexibility caters to the diverse needs of Data Science applications. More recently, there’s been a notable trend toward convergence between Data Warehouses and Data Lakes, often termed as the ‘Lakehouse’ model.

Data Mesh

The notion of Data Mesh is a relatively new concept spearheaded by Zhamak Dehghani. It represents a decentralized approach to data management, returning the ownership of various data assets to their respective business domains. These domains, in turn, offer their data as products through a self-service platform, effectively democratizing data sharing and access.

In addition to the technical solutions that support these architectural changes, there is a need for certain organizational shifts when implementing an effective analytics platform. Decentralized architectures, in particular, embrace a new paradigm that requires cultural adjustments compared to traditional models.

The core idea is to return data ownership to the teams, making them fully responsible for the end-to-end management of the data products they contribute to the company. This concept shares similarities with the successful DevOps approach in Software Engineering, breaking down large monolithic structures into smaller, team-managed functions. This approach can serve as the foundation for a corporate data marketplace, where teams can collaborate and share the results of their work. Furthermore, it’s essential to have appropriate incentives in place to foster this collaboration, encompassing concepts related to both social and material motivation.

How Starburst & Red Hat fit as technology providers

Starburst provides a data analytics platform to enable fast and efficient analytics within a given architecture. We offer an innovative solution for companies looking to improve the performance and scale of their analytic projects because we support modern architectures such as Data Lakehouses (a Data Lake architecture with Data Warehouse functionalities). Thanks to our capacity to explore and analyze data wherever it lives, we are also capable of supporting analytics in decentralized data architectures, such as Data Mesh. 

Through our collaboration with Red Hat, data scientists and AI developers can enhance their workflow, simplify processes, and take their Data Science endeavors to a higher level. Starburst offers the versatility of deployment across any of the three major cloud providers or Red Hat Openshift, ensuring a scalable infrastructure. Starburst’s capabilities encompass a compute layer that sits atop multiple data sources, facilitating data retrieval, transformation, and consumption. This data can be meticulously curated into the form of Data Products, subsequently made accessible to end-users in the fields of Business Intelligence (BI) and Data Science.

The benefits of data analytics for the insurance industry

Employing a contemporary data stack with precise and performant data analytics technology solutions empowers the development of smarter and more automated insurance products. This extends from attracting new customers and fostering customer loyalty to delivering personalized content experiences, such as optimizing website or app navigation. Other common applications include fraud detection and automated claims management. Given that insurance companies offer abstract products inherently reliant on data, these methodologies have found widespread adoption.

Listen to the full podcast here (in German).