With increasing demand for more personalized insurance offerings and changes in the insurance data landscape, a leading global insurance organization that provides property casualty insurance, life insurance, retirement products, and other financial services to clients in more than 80 countries and jurisdictions needed a way to store, manage and analyze additional data types, including unstructured, geospatial and IoT data to uncover ways to provide more value for customers. Additionally, the organization was interested in creating a modern, scalable infrastructure to empower its various lines of business with streamlined access to data for business end users.
Before the data lake: The organization was struggling with data quality issues, a fragmented data architecture and outdated data management tools. More specifically, they had more than 8,000 separate databases; 2,600 applications; and multiple claims, policy and administration systems in multiple languages being used by lines of business in different locations. Also, the organization was unable to effectively institute and enforce enterprise-wide governance and data quality controls to support security, editing and regulatory reporting – as well as cost-effective data lifecycle management of the company’s aging portfolio with 50% of data older than 10 years and 33% older than 20 years.
With the data lake: Working with the organization's existing data lake, Zaloni layered on its integrated Bedrock data lake management and governance platform, its Mica self-service data preparation platform and a data catalog to create a centralized data bureau. This scalable, modern data architecture enabled automated data ingestion; metadata management; data governance such as lineage, data quality and privacy and security; and data lifecycle management.
Results: Zaloni’s data lake management, governance and data preparation platforms are enabling the organization to provide centralized data and data services to its various lines of business for advanced analytics. Business end users have broader, yet controlled access to data and are able to correlate data and drive shared data insights across the organization, bridging the gaps between different lines of business, financial systems and more. The combination of traditional analytics and insights from unstructured data in the data lake supports a number of key use cases, including Customer 360, automated risk detection and compliance controls. Furthermore, data in motion combined with data at rest is enabling insurers to take risk management as well as risk mitigation actions in near real-time.