Providing value to SMB by optimizing ETL

New point-of-sale (POS) machines help small businesses catalog transactions and inventory by warehousing customer, vendor, product, and sales data. This data, however, is usually warehoused in a data table that is not accessible to modern analytics and management software, such as Lightspeed. To help these businesses take advantage of their data, Enkidoo provides a service to export small business data by building an extract-transform-load (ETL) pipeline to Lightspeed. However, this process can be tedious, due to mismatches in column data and the template. The aim of this project is to provide an AI based solution for column prediction to automate the ETL process. In automating this process, this project provides value to Enkidoo, and the small business by reducing manual turnaround time and error in the ETL pipeline.

Paul Crouther
Superviseur universitaire: 
Yoshua Bengio
Partner University: