MAD24 208 Improving Delivery Process Performance for Software Defined Products Helped by Data Analyt

Most of what we know today about applying delivery metrics today comes from software production environments where the organization has significant or full control of the product deployment. How do we apply such metrics in environments where your control of the deployment environment is limited or non-existent? What if it is not a software-only product but a software-defined one (including HW)? What if there is a supply chain involved you have to collaborate with in order to create the product actively? What if there are regulations involved? Is our approach to delivery metrics similar in all those cases or do we need to adapt them? What adaptations do we need to apply? This session, tailored for software engineers and data scientists, will delve into some of the lessons learned when applying delivery metrics and other key metrics to such environments, as well as some of the challenges we have faced. It will also describe the lessons learned from open source we are applying when implementing data analytics into production environments that have worked well. This talk aims to elucidate how analytics-driven assessments empower software engineering teams with insights, aiding in process and practice refinement, identifying bottlenecks, silos, waste, etc., and fostering continuous improvement with a noticeable impact on both, the overall organizational performance and the workforce well-being.