How Suunto Centralized Data From Millions of MAU with Mitzu and Databricks

Company

Suunto is a Finnish company known for its sports watches, dive computers, and outdoor gear. With a strong global presence and a loyal user community, Suunto supports its customers with integrated digital services for activity tracking and performance analysis. Now owned by the Chinese tech group Liesheng (since 2022), Suunto continues to push boundaries in outdoor and sports innovation developing cutting-edge GPS watches, bone conduction headphones, and rugged tools built for adventurers around the world. The company employs around 500 people and distributes its products in over 100 countries and supports millions of monthly active users (MAUs) across its digital platforms.

Challenge

As Suunto’s user base grew to millions of monthly active users, so did the volume and complexity of telemetry and app-generated data. The company rely on Databricks as its central data platform and used a third-party product analytics tool to support product and marketing analytics. However, with business-critical data spread across SAP, app telemetry, and other internal systems, integrating multiple sources became time-consuming and resource-intensive.

“Our data was fragmented, analytics were costly, and combining sources took too much time. We needed a simpler, flexible approach on top of Databricks.”

Suunto saw an opportunity to improve its analytics capabilities by consolidating data into a single platform that could support more complex, flexible analyses across teams. The goal was to streamline data pipelines, reduce manual effort, and enable both technical and business users to explore insights through self-service analytics. Scalability and cost efficiency were also key to supporting Suunto’s global operations and future growth.

Solution

Suunto turned to Mitzu to centralize all analytics directly on their Databricks platform. This helped them to keep their data in their own cloud while querying it efficiently without moving it around. Mitzu provided a familiar, self-service interface for business users, similar to what they were used to with their previous analytics tool, while giving engineers full control over custom data models and business logic.

By connecting directly to Databricks, Suunto ensured high data quality and more automated pipelines. Mitzu’s seat-based pricing replaced expensive event-based fees, making costs more predictable with unlimited events.

"With Mitzu, we can build our own data model in Databricks, combine multiple sources, and answer questions we couldn’t before, all without moving our data anywhere.”

Result

Since implementing Mitzu, Suunto has achieved significant improvements in both efficiency and insight. Analytics costs were dramatically reduced while maintaining or exceeding previous capabilities. Data from multiple sources including app telemetry, devices, and SAP can now be integrated into Databricks and Mitzu connects directly to Databricks.

Non-technical teams use the same intuitive interface as before, but with access to more advanced insights. Engineers have full control over custom data models, allowing predictive analytics and complex segmentation. The transition also accelerated decision-making, reducing time spent on manual data preparation and reporting. Suunto can now answer previously unattainable questions about user behavior and product performance. Overall, Mitzu has enabled a more scalable, flexible, and cost-effective analytics environment that supports the company’s global operations and future growth.

"The biggest change is the flexibility, our business teams see the same interface, but under the hood, everything is more powerful and easier to maintain.” - Paavo Keisanen, Data architect

Analytics costs dropped by over 80% using Mitzu’s seat-based pricing with unlimited events.

5+ major sources, including app telemetry, devices, and SAP, are now consolidated in one platform.

Suunto now maintains their own data model directly in Databricks, keeping all data in their cloud.
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How did you manage product analytics before using Mitzu?

Before Mitzu, we depended heavily on a single analytics platform for product and marketing insights. It helped us track user behavior and monitor app usage, but came with drawbacks. The solution was costly due to pricing based on data volume, its data model lacked flexibility, and it couldn’t support sophisticated analyses across multiple sources. Pulling in data from systems like SAP required manual effort and was slow, and creating custom business logic or advanced analytics was basically off the table.

How are you using Mitzu today?

Today, we use Mitzu with Databricks for product and marketing analytics, as well as broader business analysis across multiple data sources. Our business teams use the interface to explore data in ways similar to other product analytics tools, while we engineers maintain and build custom models in Databricks.

How has Mitzu impacted your data team?

Mitzu has given us much more flexibility and control for the data. Also the license-based price model is more sustainable.

Who would you recommend Mitzu to?

I would recommend Mitzu to companies with large volumes of event data and multiple data sources, especially tech-driven organizations where self-service analytics is important. Tools like Power BI or Tableau can handle reporting, but they aren’t as effective for flexible, self-service analysis. If you want cost control, flexibility, and the ability to maintain your own data models directly in your cloud platform, Mitzu is a great choice.

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