Case Study

Implementation of Power Query Reduces Consolidation Time

Before DLC 

  • Disconnected systems and offline data 
  • Lengthy consolidation and actualization process 
  • Lack of structure and reliability for historical data 
  • Inaccuracies within data structures

After DLC 

  • Implementation of a Cohesive, scalable and reliable platform 
  • Reduced actualization process from 4 days to 4 hours 
  • More structure and visibility into historical data 
  • More robust and structured consolidation process



A consumer products company with millions of royalty contracts around the world engaged DLC to solve for issues stemming from lengthy, disconnected consolidation processes that included unstructured data with inherent inaccuracies and inefficient process. 

DLC was chosen by the client due to our deep knowledge in finance and technology best practices and systems troubleshooting alongside our proven ability to utilize technical skills to work with large data structures and create more organized and streamlined workflows. 

Business Challenge

The client was using three disparate systems and data housed offline in Excel spreadsheets for their consolidation process. Furthermore, historical data contained inaccuracy spanning over 10 years with more than 9 million transactions.

The volume of historical data combined with an overall lack of structure contributed to a 4-day actualization process, making quick and reliable progress difficult for the client. 


After aligning on the client’s vision for the final product and the issues they were facing for ordering data for reporting purposes, DLC consultants proposed a new data infrastructure and process workflow through restructure of the current Power Query model.

To reduce consolidation and actualization time, the team of consultants developed a highly flexible model, structured more flexible business rules, and used M language for mapping. 

The DLC team documented the process to ensure a seamless transfer of knowledge, trained all client FP&A teams involved, cleaned and normalized historical data, and tested and validated the new model and process to ensure effective functionality.  


After reworking the client’s Power Query configuration for maximum efficiency, the client was able to reduce the time it took to make changes to historical data from 1 week to 1 hour, reduce the actualization process from 4 days to 4 hours, and provide leadership with the real-time visibility they needed to make accurate and reliable decisions for growth.