Complex System Migration Loading 15,000 Contracts with Minimal Errors

1. About the Client  

We worked with a large Canadian tour operator, an established business with a lot of complex data and a huge amount of contract data behind the scenes that kept everything running.  

2. The Challenge  

They were in the middle of moving to a new reservation centre, and part of that transition meant loading all their accommodation, tour and transfer data. The problem was that the data lived in multiple places, some in the old reservation system, some in spreadsheets, and none of it matched the structure of the new platform. If they had tried to do this manually, it would have taken dozens of people years, and it would have drained the business at a moment when they really couldn’t afford that kind of disruption. 

3. How We Helped 

We built a system that loaded old contracts automatically. Because the new platform expected the data in a completely different format, we had to gather everything together, merge it, clean it, and reshape it. As the older system was gradually retired, new batches of contracts were processed and released onto the new platform, keeping everything moving without overwhelming the team. 

4. What We Achieved Together 

In the end, we loaded a huge amount of complex contract data with a level of accuracy that would have been well-nigh impossible with manual entry. It took three of our team several months to work through the analysis and build the automated loading, but our effort kept the operational pressure away from the business and allowed them to keep trading as normal.  

The quality of the data was consistently high, and that matters more than people often realise. On a booking system, even small loading mistakes, repeated across hundreds of bookings, can cause real financial damage. Automating the process kept that risk low and protected the business from costly errors. 

6. What This Means Going Forward 

Large, intricate data-loading projects like this simply aren’t suited to teams of people working by hand. It’s exhausting, it’s slow, and it’s incredibly hard to maintain accuracy. Machines do this kind of work well; humans are much better at guiding the process, setting direction, and making sure everything lines up. Achieving the right balance between human and machine made the whole transition possible.