We helped hellorider take the next step in their IT architecture, allowing them to make data-driven decisions more easily.
Xomnia met us where we were, adapting to our technical capabilities and unknowns to support us choosing a path forward for our data.
Jeroen Wilmink, Data engineer
hellorider has demonstrated incredible growth built on a comprehensive data platform that supported decision-making. However, their data platform hadn’t kept up with the increasingly complex demands of their data analytics department. When it was time to refresh their data architecture and cloud capabilities, they turned to Xomnia for expert advice.
hellorider has become the leading omnichannel bicycle retailer in Benelux and Scandinavia. They have experienced 70%+ annual growth rates over the past few years, making it one of the fastest growing e-commerce companies in Europe.
Xomnia supported hellorider in choosing the right cloud platform and consulted on technical implementation of the company's database build. Xomnia hosted a two-day workshop on different cloud providers - building trial databases on Google, Amazon, and Microsoft platforms. We explained potential barriers and advantages to implementation.
hellorider was specifically interested in which platform would most easily accommodate their need for data to refresh daily. The workshop ended with hellorider deciding to run their database and BI on google cloud and Xomnia provided followup support as hellorider’s lead engineer designed and implemented the project.
hellorider built their new database in BigQuery and hosted it on Google Cloud Platform. Scripts to pull and export data were written in python, and executed via virtual machines on Google Compute Engine. Airflow was used for all scheduling, and Tableau was used for data visualization.
Rebuilding its data platform has allowed hellorider to run more detailed reports that role up to strategic decision making for both sales and operations. Updating their data management means hellorider was able to apply machine learning and develop predictive models for sales, informing inventory management and promotions.