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Case Studies

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Spot goal-divergent metric alerts and insights

Spot goal-divergent metric alerts and insights

Spot goal-divergent metric alerts and insights

How streamlined analytics and automation transformed business decision-making

How streamlined analytics and automation transformed business decision-making

Identifying business drivers

Centralized platform

Reduced cycle time

Accurate forecasting

The challenge

Streamlining decision-making with real-time insights

A major P&C insurer aimed to develop an early alert system to track deviations from its business portfolio goals and benchmarks. They wanted a guided analytics dashboard for tracking KPIs such as Items in Force, its metrics, and underlying drivers. Core factors included early identification of deviations, understanding the reasons behind those deviations, and enabling leadership for better business performance.

Key challenges

  • Need for insights into goal deviations

  • Need for user alerts about performance flaws

  • Multiple isolated reports delaying decision-making

  • Disparate data sources causing misalignment on direction

  • Delayed data availability and preparation for consumption

The solution

Smarter business insights through automation and centralized reporting

Centralized reporting and dashboards

Agile approach for insights

Real-time dashboard

Consolidated metrics

Automated and self-alerting mechanisms

Self-alerts for dips

Storyboards highlight issues

Seasonality-based forecasts

Implementation approach

1

Data layer development

  • Data slicing

  • Integrated sources

  • Unified data layer

2

Automation and data processing

  • Automated processing

  • Streamlined workflows

  • Established governance

3

Performance monitoring and alerts

  • Self-alerts for tracking

  • Adjusted benchmarks

  • Alerts for deviations

The impact

Driving business success

Early identification of drivers

  • Improved portfolio management

  • Enabled proactive action

  • Identified key drivers

Centralized platform for all levels

  • Streamlined decision-making

  • Reduced solution cycle time

  • Unified stakeholder access

Accurate forecasting and business planning

  • Data-driven decisions

  • Better forecasting

  • Stronger strategy

Looking ahead

Enhanced predictive capabilities

  • Use AI to predict future trends and performance deviations

Scalable infrastructure

  • Expand system capabilities to handle huge, complex data

Continuous improvement

  • Refine forecasting models for accurate and actionable insights