Amway manufactures over 450 different nutrition, beauty, personal care and home products, each of which needs to be carefully categorized and organized in its product hierarchy. Updates to the hierarchy need to be made daily as categories expand, business lines evolve, and new products develop. However, Amway’s original desktop-based model was complex and required numerous manual updates, ultimately proving itself slow and unsustainable. Amway smoothed out some of these kinks after switching to a virtualization solution, but the solution’s SQL-based transformations required too much hand-holding from the engineering team. Analysts and engineers had to communicate back and forth about data requirements until, days later, the outcome produced was as expected. Neither solution allowed for flexibility nor agile changes to the product hierarchy.
Amway migrated its data to Google Cloud BigQuery and centered its product hierarchy around Dataprep. The transition to a cloud-based, self-service data engineering technology has dramatically reduced time-to-insight—whereas before Amway would have to wait two to three days before a change could be realized, now it’s a matter of minutes. Much of this acceleration is credited to Dataprep’s ease of use. Analysts can easily obtain product hierarchy data and make direct changes without communicating back-and-forth with engineers. Plus, Dataprep has improved visibility into the product hierarchy with clear audit trails and data lineage. The result is a more streamlined and agile product hierarchy that can quickly respond to new changes each day.
With Google Cloud Dataprep, Amway was able to reduce time spent making changes to its product hierarchy from 2-3 days down to 5-7 minutes.
Amway’s engineering team has been freed up to focus on more complex deliverables instead of responding to data requirements.
Amway analysts can now obtain data at different hierarchy levels and, using their unique business knowledge, remodel the hierarchy for the most profitable option.
