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Transportation & Logistics

Cloud Migration Optimizes ERC Data Performance

The Challenge

Given the scale of its activities on a critical infrastructure tunnel project, Elizabeth River Crossings (ERC) possesses vast amounts of data across business functions and stakeholders. Exponential growth in project work meant it needed better visibility over its data.

ERC’s incumbent business intelligence platform, SAP BusinessObjects, is good for daily, monthly and annual reporting but presents challenges when leveraging advanced analytics and scaling up performance. Having migrated a significant portion of its on-premises infrastructure to AWS, ERC decided to take its BI platform to the cloud and turned to Cognizant for help implementing SAP Analytics Cloud on AWS.

Our Approach

ERC engaged Cognizant for our expertise in cloud migration. The need for insights based on predictive analytics was greater than ever, given the expanding scope of the company’s projects. Specifically, ERC needed increased insight into toll road usage, customer type, revenue, costs and internal operations.

The project involved migrating SAP BusinessObjects and its components to the cloud, as well as implementing and configuring SAP Analytics Cloud on AWS. The migration enabled ERC to utilize SAP Analytics Cloud’s ability to create specific single-view dashboards that offer insights into customer payment patterns and behavior, day-to-day traffic patterns, traffic volume predictions, customer care call center volumes, operational performance monitoring, and transactions and the revenue expected from them.

Better Data Management and Improved Business Insight

ERC’s migration to SAP BusinessObjects and implementation of SAP Analytics Cloud on AWS delivered a host of tangible benefits, including a 360-degree view of the business, overall system performance and availability improvements, on-demand capacity and improved SAP Data Services job performance, which increased by over 90%.


90% improvement

in SAP Data Services jobs


overall system performance and reliability


data retrieval compared to on-premises set up


data storage