Arguably, Report Hierarchy was the most important enhancement Travel Anaytics deployed in 2016. In a nutshell, Report Hierarchy allows administrators to download, amend and reload a new organisational structure (report hierarchy) for their data within a matter of minutes.
The functionality is a vast improvement on the traditional method of maintaining report hierarchy, which involves the Travel Manager updating individual traveller profiles within the mid office, reservation and online booking systems. A process that often takes days, if not weeks to complete.
While the reduction in time and effort are substantial, so to are the improvements in the accuracy of the data presented to the customer.
Often travellers will travel across multiple cost centres belonging to different parts of the organisation. Traditional travel management systems find it difficult to deal with this, as the traveller profile is linked to a static hierarchy and any variations cannot be matched to the correct structure.
Report Hierarchy turns this issue on its head, as the functionality automatically associates the correct organisational structure to the cost centre entered at the time of booking.
During development, we experimented with customer data with a little over 1500 cost centres, which over two-thirds were duplicates, obsolete, incorrectly entered or missing. When it gets to this stage, the data becomes unless, forcing customers to manually download and correct the data to make sense of it.
Using Report Hierarchy we were able to download the existing data, remove the duplicates, merge obsolete data, correct mistakes and format the data into the correct hierarchy using the cost centre as a reference point.
Therefore in matter of minutes, data is transformed from being almost unusable to highly structured reports, where little or no manual manipulation is required to get the information the customer needs.
However, cleaning up the data is the first part of the process. The second part, and more important, is programming the system to identify and clean up rogue data when we import it. We do this using aisles – you send me ‘HR Head Office’ and I am going to call it ‘Human Resources’. What’s great about aisles, once it’s corrected, any subsequent errors are updated automatically.