Insurance Business Analytics – Seven steps to reliable data models in the insurance industry

Deregulation of the German insurance market has led to new regulatory requirements. Keeping an overview is not always easy with the multitude of requirements. Therefore, it is even more important to understand the complexity of these requirements in order to specifically support customers in the selection of requirements and to ensure compliance with new rules and regulations.

mgm consulting partners relies on the combination of extensive expertise and many years of experience in the insurance industry. We specialize in in-depth analysis, design and implementation of data models tailored to the specific needs of insurance companies. This enables the development of robust and customized data models that optimize the efficiency and effectiveness of companies.

Data Analytics – Leaving nothing to chance with good advice

1. Use Business Intelligence Assessment
The use of Business Intelligence (BI) Assessment provides insight into the current status of the enterprise’s dispositive landscape. The assessment enables a comprehensive evaluation of the existing data infrastructure, identifies strengths and weaknesses, and provides recommendations for action.

2. Identify and Build Architecture
To build a stable and reliable data warehouse, a consistent layered architecture and the implementation of binding framework concepts are essential. These foundational elements ensure the integrity, scalability, and longevity of data warehouse infrastructure. When the current infrastructure is compared to the target architecture, potential inefficiencies or deficiencies are revealed. Proven templates and models ensure stability and sustainability.

3. Implementing Data Warehouse Projects
During implementation, it is important to have accompanying consulting throughout all project phases – from the technical requirements analysis to the specification of the data flows to the go-live. This guarantees that projects run smoothly and that the full potential of the data is exploited.

4. Model Data
Data models must be methodologically sound and stable – and applicable to all layers of the data warehouse. Experienced consultants have extensive knowledge in the use of common methods and tools to develop flexible data models. This ensures that the data warehouse can cope with future changes.

5. Analyze Patterns
From unstructured mass data to strategic decision-making: Modern processes and technologies can be used to find patterns and make unerring predictions that serve as the basis for strategic decisions – so that the full potential of data is exploited.

6. Implementing Governance
Now it is time to implement enterprise-tailored data governance as the organizational and operational framework for all data flows in the enterprise – even beyond the data warehouse. By establishing clear policies, roles and responsibilities, data integrity, consistency and compliance are ensured throughout the enterprise. This approach ensures alignment of data governance with business objectives and promotes a culture of responsible data management.

7. Ensure Data Protection
Security and trust are the basis for handling customer data and for complying with individual requirements. Sensitive information must be protected at all costs, so a comprehensive analysis of the specifications is essential. Processes and IT systems must be adapted accordingly.

We at mgm consulting partners are at your side with our expertise and experience to support you in this challenging process.