Gymlib
Leading corporate wellness platform slashes data processing time from 15 hours to 25 minutes, automating core financial processes and enabling seamless growth.
Business goals
Gymlib, a fast-growing French corporate wellness marketplace serving 800+ companies and 450,000 users, needed to modernize its data infrastructure to support surging demand during and after the pandemic. The company aimed to scale efficiently by:
- Supporting rapid user and data growth
- Ensuring accurate, timely billing and invoicing
- Reducing manual, error-prone data prep
- Enabling its lean data team to focus on insights
- Building a reliable, scalable foundation for analytics and marketing automation
Integration Challenges
Success always presents unexpected challenges. As a fast-growing startup, Gymlib discovered that its legacy data stack, built on basic tools, could not handle the company’s expanding volume and complexity of data. As a result:
- Data preparation time had ballooned to 15 hours per cycle
- Data quality issues disrupted accounting and operations
- Manual corrections created inefficiencies and errors
- Infrastructure was not built for scale or automation
- Data from third-party apps and internal systems lacked consistent integration
How Boomi Helped
Gymlib implemented Boomi Data Integration to unify, automate, and scale its data operations. With seamless integration to Amazon Redshift and other key platforms, Gymlib was able to:
- Automate data ingestion from seven key sources, including SaaS apps and application databases
- Centralize analytics and reporting through Amazon Redshift
- Enable reverse ETL for marketing automation by syncing enriched data to HubSpot
- Build custom connectors for APIs to ensure full data coverage
- Leverage Boomi’s support to reduce technical lift and accelerate delivery
Results
By modernizing its data infrastructure with Boomi Data Integration, Gymlib reaped numerous benefits. The value of getting its data in shape included:
- Data processing time dropped from 15 hours to 25 minutes
- Financial operations were automated, saving four FTE days per month
- One FTE now oversees accounting data, just one day per month
- The company scaled data operations by 50X without increasing team size
- Business users can easily onboard and focus on insights and not maintenance
