How can we use data to make software teams more efficient, products better, and engineers happier?
This talk explores what we have learned while applying data analytics and business intelligence to large-scale software production — from open source communities to the automotive industry.

Agustín Benito Bethencourt works on Delivery Performance Analytics (DPA) in collaboration with Bitergia, a Spanish company known worldwide for its expertise in open source software metrics and analytics. Together, they study how to measure and improve the delivery performance of complex software systems and the organizations that create them.

Luis Cañas Díaz is a software engineer and data specialist, and co-founder of Bitergia. He has extensive experience in software development analytics, contributing to the development of tools and methodologies for analyzing open
source ecosystems, including GrimoireLab within the CHAOSS initiative. His work focuses on deriving actionable insights from software development data,
supporting organizations in assessing community health, engineering productivity, and delivery performance through rigorous, metrics-driven
approaches.

The talk will explain how analytics frameworks and metrics born in open source — such as project health indicators, contribution activity, code review data, and delivery process metrics — can be applied to automotive software environments, even though they are often more closed and structured. Agustin and Luis will discuss what works, what doesn’t, and why.

Through practical examples, graphs, and real use cases, they will show how data-driven insights from open source projects can help automotive companies understand their own development processes better. At the same time, he will share the main limitations faced when transferring open source analytics methods to industrial contexts — such as restricted data access, different collaboration models, and organizational silos — and how to overcome some of them.

The session will also explore the reverse direction: how methods and practices from the automotive industry can enrich analytics in open source projects, especially in areas such as quality assurance, delivery predictability, and performance tracking.

Participants will take away:
A clear picture of which open source metrics and frameworks can be reused in closed environments.
Common pitfalls when adapting them to industrial projects.
Practical advice for developers, managers, and data engineers who want to use analytics to improve software delivery.
Examples of how DPA combines Bitergia’s experience with Agustín’s work in software-defined automotive environments to turn data into actionable insights.

In short, this talk is about bridging two worlds: the openness, transparency, and data richness of community-driven development, and the rigor, scale, and constraints of the automotive industry. Both can learn from each other. By sharing lessons from our data analytics journey, we hope to inspire others to make their own software production smarter, faster, and more satisfying.