Data Silos and Inconsistent Data Models: The client’s data was scattered across multiple systems like Ingenium (Policy Administration System), Dynamics CRM, Claims Management System, and Sales Force Management, each with different data models and formats. This fragmentation made it difficult to integrate data, affecting critical functions like Customer Persistency Prediction, Claims Forecasting, and Sales Governance.
Manual Reporting and Delays in Data Access: Over 600 reports were manually generated using Excel extracts, resulting in inefficiencies and slow access to actionable insights. Business areas like sales performance, claims data and persistency tracking relied on batch processes, which hindered the ability to track KPIs in real-time and slowed decision-making.
Data Quality Issues: Fragmented data sources and inconsistent data entry practices led to inaccuracies in decision-making and reporting. This inconsistency impacted the reliability of AI/ML models for Persistency Prediction and Claims forecasting, leading to inaccurate insights.
Limited Real-Time Analytics: The absence of a centralized real-time analytics platform meant that decision-making was slow, and up-to-date data was not readily accessible. Key areas like Sales Performance, Claims Status, and Policy Servicing lacked real-time visibility.
We proposed a cloud-based data lake solution using Microsoft Azure and Azure Synapse Analytics for centralizing data, along with Azure Data Factory and Azure Fabric for seamless data orchestration and governance. The solution was implemented in five stages:
Data Strategy:
Data Warehouse Creation/Data Integration:
Real-Time Centralized BI Reporting:
AI/ML Models:
Data Governance:
Automated Reporting: The automation of over 600 reports into real-time dashboards led to a 50% reduction in manual reporting time, enabling quicker access to insights and freeing up resources for more strategic tasks.
Real-Time Analytics: Real-time data processing allowed business teams to access up-to-date dashboards, improving operational decision-making and enabling more agile responses to emerging trends.
Improved Decision-Making and Data Accuracy: Real-time insights from Power BI and AI/ML models enabled faster, data-driven decisions. Automated data validation reduced errors by 30%, ensuring reliable reporting.
Increased Efficiency: Streamlining reporting into dynamic dashboards reduced manual work, allowing employees to focus on analysis and improving productivity while enabling self-service analytics across departments.
High User Adoption: The intuitive design of Power BI dashboards led to high adoption rates across departments, empowering business teams to perform self-service analytics and make informed decisions independently.
By implementing a comprehensive cloud-based data platform using Microsoft Azure, our client successfully centralized their data and transformed reporting and analytics. This solution addressed data silos, manual reporting, and limited real-time insights, improving decision-making and operational efficiency. The integration of AI/ML models and LLMs enhanced predictive capabilities, while data governance ensured data security and compliance.
Insurance
India
End to End Project Lifecycle Management
Briefly describe the challenges you’re facing, and we’ll offer relevant insights, resources, or a quote.
Business Development Head
Discussing Tailored Business Solutions
