Driving Real-Time Insights for a Leading Construction Equipment Dealer

A leading construction equipment and heavy machinery dealer in India wanted to harness the power of data to drive strategic decisions. With operations spanning sales, service, and machine telemetry, the client needed a unified platform to consolidate and visualize critical business data.

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Challenge

The client’s existing processes relied heavily on manual reporting, with data scattered across CRM, SAP, and equipment monitoring systems. This fragmented approach delayed insights, increased the risk of errors, and limited agility.

Fragmented Data and Reporting

Siloed data across multiple business systems and time-consuming manual reporting processes

Lack of Operational Visibility

Limited visibility into machine utilization, service efficiency, and customer engagement

Solution

Entrans implemented a modern data analytics platform powered by Azure Synapse Analytics and Power BI. This solution centralized data from various sources, streamlined reporting, and delivered actionable insights in real-time.

Key Initiatives:

  • Integrated CRM, ERP, and machine telemetry into a unified data lake
  • Transformed and modeled data using Azure Synapse
  • Built interactive dashboards and reports using Power BI
  • Enabled predictive analytics for service planning and revenue optimization

Strategic Advantage:

‍The client now operates with a data-first mindset. With a scalable and unified analytics foundation, they are better equipped to optimize operations, enhance customer satisfaction, and respond quickly to market shifts.

Client Feedback

“The data analytics solution provided by Entrans has transformed our business operations. We now have real-time insights that drive smarter decisions, ultimately improving efficiency and customer satisfaction.”
— [Client Representative], Leading Construction Equipment Dealer

Key Takeaways

  • Unified siloed data for real-time decision-making
  • Automated reporting, reducing errors and improving speed
  • Enabled predictive analytics for strategic growth
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Outcomes

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Real-time access to business-critical data

02

Reduced downtime through predictive maintenance

03

Improved revenue performance through better customer segmentation

Technology Stack and Architecture

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Methodology

Key step of the project are the following

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