Decreased time-to-decision by 99% while also reducing reporting systems by 86% in just 4 months.

Application and Data Migration


An enterprise client of ours was trying to increase the pace of decision making. Their current data marts were spread across multiple data stores and a simple forecasting query took hours to complete. BETSOL was asked to find a scalable solution and aid in its execution.


Key Challenges

  • Lifecycle tracking of over 821,000 entities had to be addressed across 26 data marts and 7 systems.
  • Query times were taking more than 8 hours and creating bottlenecks in quality decision making.
  • There were multiple stall points and certain data entries that were entirely missing.



  • Increase conversion rate through meaningful follow-ups with prospective clients.
  • Simplify data workflows.
  • Reduce data query time enabling speedy decision making.
  • Help improve their forecasting, budgeting, and trend analysis to instantaneously plan for unprecedented volume spikes.


  • We built an Extract Transform Load system for cleaning data using SQL Server Integration Services (SSIS) and T-SQL Stored Procedures.
  • Cubes were created for different business processes with Azure Analysis Services (Tabular Model) and DAX calculations. The visualization of data was powered using PowerBI dashboards. The remaining unstructured data sources were handled using Azure Data Lake and Bricks.
  • We converted 26 data sources, 7 separate systems, and manual processes of over 300 agents into a single self-serve dashboard in under 4 months.


Our client experienced a successful and seamless transition to Azure Data Mart.

  • Detailed lifecycle tracking of over 821,000 entities helped them in reducing costs, making better decisions quickly, forecasting, budgeting, improving conversion rates, and formulating better trend analysis instantaneously to plan for future volume spikes.
  • Lifecycle and pipeline analysis for IB/CSP networks (including attrition and supply chain pipelines) was greatly improved.
  • Service revenue analysis was able to process fine-grained data in a matter of minutes.
  • 99% reduction in the time taken to assemble and analyze data helped in instantaneous forecasting and decision making. This equipped the client in tracking:
    • Time taken at each process starting from registration to service.
    • Identify stall points and cut down on it.
    • Improve conversion rate through meaningful follow-ups with prospective clients.
  • There was a 96% reduction in data sources and 86% reduction in systems. This transported instant access to a single source point that was crucial for making critical business decisions.

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