How Datacor built self-service rental analytics with Amazon Quick Sight
Learn how Datacor built a self-service rental analytics experience for gas and welding distributors by embedding Amazon Quick Sight dashboards and natural language querying into its TrackAbout platform, powered by an automated cross-cloud data pipeline and multi-tenant row-level security.
Datacor, a leading provider of software and expertise in process manufacturing, chemical distribution, and engineering, has developed a self-service rental analytics solution using Amazon Quick Sight. Gas and welding distributors rely heavily on rental billing, but data was often locked in disconnected systems, making it difficult to gain insights into fleet utilization, rate performance, and revenue recovery.
To address this issue, Datacor integrated Amazon Quick Sight into its TrackAbout solution, enabling interactive dashboards and natural language search capabilities.
The solution provides a self-service analytics experience for business users to explore rental performance data, ask natural language questions, and make data-driven decisions without relying on IT support or custom reports. By automating the cross-cloud data pipeline, Datacor enables users to access up-to-date rental data directly within the TrackAbout application. This allows for real-time decision-making based on accurate, contextually relevant insights.
The architecture of the solution includes a multi-tenant rental analytics platform that integrates with Amazon Quick Sight, providing a generative BI capability powered by generative AI. Business users can ask plain-language questions about their rental data, such as "Show me rental revenue by product type last quarter" or "What is the average rental rate by asset class across my top 20 accounts?"
The solution also offers pre-built dashboards displaying key metrics like fleet utilization rates, billing exceptions, revenue recovery trends, and customer-level asset aging. These dashboards are designed with a visual format tailored for business users, ensuring easy interpretation and analysis.
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