HomeRepairModernising data for future-ready insights
As a national repairer serving Australia’s leading insurers, HomeRepair required a more efficient and reliable data system to improve reporting, enhance decision-making, and prepare for future innovations like AI.
Challenges
HomeRepair faced key challenges including lack of data trust and governance, complex and inefficient reporting, complex data integration and orchestration tools, and limitations in scalability and future readiness for digital transformation.
Solutions
Fujitsu implemented AWS Databricks Lakehouse Accelerator, a best-practice approach to modern data architecture, recognizing the need for a scalable and future-proof data solution.
Outcomes
- Streamlined data processes
- Improved reporting accuracy
- Scalable platform for AI-driven insights
“Fujitsu’s work transformed our data and enhanced our data analytics capability, giving us the confidence and clarity we needed to unlock its true value and drive better operational and financial performance and insights.”
Shane McNamara, Head of Finance, HomeRepair
8
Weeks for solution delivery
- Industry:Insurance and home restoration
- Location:Australia
- Customer's website
About the customer
HomeRepair is a national repairer of homes for customers of Australia’s leading general insurers. They sought to improve reporting, enhance decision-making, and prepare for future innovations like AI through a more efficient and reliable data system.
Overcoming Data Complexity and Inconsistencies
HomeRepair, as a national repairer for Australia’s leading insurers, recognised an urgent need for a more efficient and reliable data system. This was not merely about improving current reporting and enhancing decision making, but also about strategically preparing for future innovations, including the integration of AI.
The company faced a confluence of significant challenges that impeded its progress.
A fundamental issue was a pervasive lack of data trust and governance, rooted in outdated legacy platform data models, unsupported features, and fragmented reporting. This directly resulted in inconsistent analytics and limited adoption of essential reporting tools. Furthermore, reporting itself was complex and inefficient; the absence of critical CRM source data attributes in Redshift necessitated cumbersome workarounds and custom SQL queries, severely complicating Business Intelligence reporting.
Unwieldy data integration and orchestration tools, whose processes were difficult to support and incapable of managing upstream source system schema changes were also a topic for HomeRepair’s internal processes. Ultimately, these limitations in scalability and future readiness stifled HomeRepair’s’ aspirations for digital transformation and data-driven decisions, preventing it from fully leveraging its valuable data assets.
