Since 2020, our team at SnA have worked with multiple clients that wanted to set up an open source analytics platform. In most cases, this meant replacing their existing commercial analytics environments..
A common motivation shared by our clients was managing their costs. But they were also facing rigidity, slow innovation cycles, and vendor-driven roadmaps. Open source was seen as a way to gain control of their analytics environments.
Based on our experience, successful transitions require a balance between technical issues and also changes in operating model, skills, and governance.
In almost all of our migration projects, we followed the same steps. We helped our clients start with a selected use case. Then we demonstrated the value from this use case. Later on, we built a step-by-step transition roadmap and completed the migration.
Open source analytics platforms have clear benefits:
- Lower total cost of ownership
- Faster experimentation and model development
- Better integration across data engineering, analytics, and AI
- Reduced vendor lock-in and roadmap dependency
- Stronger internal data and engineering capabilities
Over time, these platforms became more than analytics environments. They turned into foundations for AI, automation, and decisioning.
The core insight is straightforward. Open source analytics is about building capability. Once that capability is in place, speed and impact follow naturally.