Question 1

Phase 1: Laying the Foundation

Accepted Answer

The initial phase focused on increasing data granularity within Aviva’s core database shifting from agency-level data to a view which contained policy and claims level insights. This required significant changes, including:

• Expanding the dataset from 100 million to 1.5 billion rows, covering five years of historical data

• Replacing the legacy Altair data transfer system with an Oracle-based solution to handle the increased data volume

• Re-engineering the database structure to remove redundant fields and introduce a standardised naming convention

Overcoming Challenges
 • Data Transfer Complexity: Moving data from the data asset to Aviva’s Data Insight team proved to be the most difficult aspect, with transfer times exceeding expectations and multiple failures occurring. To resolve this, three teams - Finance SMEs, Database Developers, and Data Engineers worked in unison, holding daily stand-ups to systematically identify and eliminate bottlenecks.

• Testing & Deployment Risks: With limited test environments and the need to deploy changes between month-end reporting cycles, the project team implemented rigorous pre-deployment checks to minimise operational disruption.

• Stakeholder Trust: The team proactively engaged stakeholders through visual management techniques, applying the 1, 3, 10 rule enabling leaders to grasp key updates in one second (status), three seconds (why), and ten seconds (action plan).

Question 2

Phase 2 & 3: Refining Financial Cost Allocations

Accepted Answer

Once the foundational data structure was established, the next phases focused on ensuring accurate financial cost allocations. These processes had previously led to mistrust in data, as teams sought secondary datasets to validate figures. Key improvements included:

• Developing data loaders to automate the correct application of reinsurance and large loss allocations

• Introducing robust reconciliation processes across Database, Data Engineering, and Data Insight teams to ensure accuracy at every stage.

• Creating a single source of truth, eliminating the need for multiple datasets and improving alignment across business teams

Question 3

Building a Strong Team & Driving Engagement

Accepted Answer

Cross-location collaboration was critical to the project’s success. With teams split across three locations (onshore and offshore), a dedicated Teams chat allowed real-time issue resolution. Task prioritisation during morning stand-ups, combined with follow-up meetings for complex issues, ensured a structured approach to problem-solving.

Question 4

Promoting Team Well-Being & Recognition

Accepted Answer

Given the long hours and high-pressure environment, team engagement was prioritised:

• Daily well-being check-ins helped ensure workload balance and provided space for team members to voice concerns.

• Aviva’s Recognition Shop allowed project leaders to reward team members with small tokens of appreciation - boosting morale and fostering a culture of collaboration.

Developing a Single Data Asset for Broker Business at Aviva

Key Opportunities

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To drive these improvements, Aviva launched a strategic project to develop a single, trusted data asset—enabling real-time insights, faster decision-making, and enhanced data governance.

From Data Transformation to Team Excellence

Results & Impact

Lessons Learned & Future Improvements

Conclusion

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