Technology Category
- Analytics & Modeling - Big Data Analytics
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Cement
- Renewable Energy
Applicable Functions
- Product Research & Development
Use Cases
- Cybersecurity
- Smart Campus
Services
- Cybersecurity Services
- Data Science Services
About The Customer
Munich Re is a global reinsurance company that is part of a larger group, which also includes primary insurance entities. The company's core business involves managing large and complex risks, including natural catastrophes, satellite launches, and major building projects. Munich Re has been working with data for over a hundred years to understand risk. The company has a diverse range of teams working on different aspects of data analytics, including live data, weather data, and claims data. These teams are spread across various parts of the company and across the globe. The company also has a subsidiary in the U.S. that specializes in IoT and monitoring wind farms for other customers.
The Challenge
Munich Re, a global reinsurance company, was facing challenges in managing and utilizing its vast data resources. The company deals with complex risks, including natural catastrophes, satellite launches, and large building projects, which generate a significant amount of data. However, the company was struggling with data collaboration and transparency across its various departments and global locations. Different teams were working on similar data analytics projects without knowledge of each other's work. This lack of coordination and collaboration was hindering the company's ability to innovate and develop new products based on shared insights. Furthermore, the company was grappling with the challenge of efficiently managing its data lake, which contained thousands of data sources and tables. The difficulty lay in finding specific information and extracting it from the data lake.
The Solution
To address these challenges, Munich Re launched an innovative data platform in February 2017. This platform included a data lake and a data catalog, which served as the cornerstone for analytics. The platform enabled employees across the company to collaborate more efficiently, increasing transparency about ongoing projects and use cases. It also facilitated the combination of different data sources, leading to new insights and product ideas. The data catalog played a crucial role in managing the data lake. It provided a bird's-eye perspective on the raw data, understood the context from which the data originated, and identified the use cases executed with the data. This made it easier for users to navigate the content of the data lake. The platform also facilitated collaboration and knowledge sharing among the 2,000 employees using it. It allowed users to find not only the data but also the queries and insights that other users had discovered.
Operational Impact
Quantitative Benefit
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