Project Title: Metratech – Implementation of Aggregate Metrics Processor at GXS and Bell Canada

Problem Statement:

Both GXS, a global leader in B2B integration and electronic data interchange (EDI) solutions, and Bell Canada, one of Canada’s largest telecommunications companies, faced significant challenges in managing and processing large volumes of aggregated metrics from their customer transactions. Their existing systems were inefficient and struggled to keep up with the increasing data volumes generated by their extensive operations. This led to delays in reporting, inaccurate metrics, and a lack of real-time visibility into business performance, negatively impacting decision-making and operational efficiency.

Ashwin’s role: Metratech’s Aggregate Metrics Processor (AMP) was introduced as a new product line at Metratech at that time and required a lot of learnings based on unique customer requirements. Not only I managed to gather the requirements but continued to work very closely with the product teams to manage the feedback loop once the full life cycle implementations were completed. Primary Scrum Master of the Aggregate Metrics Processor (AMP) team. Helped facilitate multiple sales calls by providing walkthrough sessions with customer groups on the AMP and sold it to over 10+ customers. MetraTech’s revenue for deals with AMP were 3X more than the usual.

How the Implementation Solved the Problem:

To address these issues, GXS and Bell Canada implemented MetraTech’s Aggregate Metric Processing System, designed to efficiently handle vast amounts of transactional data and provide scalable, real-time processing and analysis of metrics.

Key Improvements from the Implementation:

1. Real-Time Data Processing: I had enabled both GXS and Bell Canada to process and analyze data in real-time, offering immediate insights into business performance. This was a significant enhancement over their previous systems, which suffered from processing delays that hindered timely decision-making. The ability to process data in real-time not only improved operational efficiency but also allowed for quicker responses to market changes, providing these companies with a competitive edge.
2. Scalability: The system I implemented was built to scale with the growing data needs of both companies, ensuring that the system could manage increasing transaction volumes without performance degradation. This scalability was particularly critical as both GXS and Bell Canada expanded their global and national operations, respectively. The scalable nature of the solution ensured that as the companies grew, their data processing capabilities kept pace, avoiding costly system overhauls or performance bottlenecks.
3. Accuracy and Reliability: Through my implementation, it significantly improved the accuracy of the metrics generated, reducing errors and ensuring that the data used for decision-making was reliable. For Bell Canada, in particular, this accuracy was essential for financial reporting and customer service operations. The enhanced accuracy and reliability of the data provided both companies with a solid foundation for making strategic decisions, reducing the risk of errors that could lead to financial losses or customer dissatisfaction.
4. Enhanced Reporting Capabilities: The system provided advanced reporting tools that allowed GXS and Bell Canada to quickly and efficiently generate detailed reports. These reports provided valuable insights into customer behavior, transaction patterns, and overall business performance, which were instrumental in strategic planning. The ability to generate detailed and accurate reports in a timely manner allowed these companies to better understand their markets and customers, leading to more informed strategic decisions and improved business outcomes.

Savings and Revenue Generated:

The implementation of MetraTech’s Aggregate Metric Processing System resulted in substantial financial benefits for both GXS and Bell Canada:

• Real-time data processing: This capability allowed both organizations to reduce the average time for processing customer transaction data by over 15%,
• Scalability: Both organizations were able to process 2x the transaction volume without any degradation of performance
• Operational Efficiency: Both GXS and Bell Canada were able to generate detailed reports more efficiently by reducing their time to report during month end processing by 1-2 days on average which was significant improvement to their operations.
• Cost Savings: By automating the processing of large volumes of data and improving the efficiency of metric aggregation, both companies were able to reduce operational costs significantly. The reduction in manual processing led to estimated savings of between $1M to $2M annually for each company