In the data preparation space, very frequently the focus lies in BI as the ultimate destination of data. But we see, more and more often, how data enrichment can loop straight back into the primary systems and processes.
Take recommendation. Just the basic type (“customers who bought this also bought…”). That, in its simplest form, is an outcome of basket analysis.
We recently had a customer who asked for a basic recommendation as a part of proof of concept, whether Keboola Connection is the right fit for them. The dataset we got to work with came from a CRM system, and contained a few thousand anonymized rows (4600-ish, actually) of won opportunities which effectively represented product-customer relations (what customers had purchased which products). So, pretty much ready-to-go data for the Basket Analysis app, which has been waiting for just this opportunity in the Keboola App Store. Sounded like good challenge - how can we turn this into a basic recommendation engine?