Level Up OpsLevel

We sat down with Eli Daniel, Head of Engineering at Jellyfish, to discuss how important measurement is to engineering effectiveness—and culture. By combining quantitative and qualitative data points, engineering leadership can better articulate the entire team’s impact on the organization. 

Topics discussed in this episode:Productivity metrics provide objective data to measure team performance and inform decision-making.The right metrics depend on the goals and context of the organization.Start by collecting the metrics you have and focus on understanding where time is being spent.As organizations scale, metrics should focus on alignment and resource allocation.DORA metrics are a good starting point, but they don't tell the whole story.Balancing quantitative and qualitative metrics provides a more complete picture of team performance.
Jellyfish is a platform that helps engineering leaders translate and maximize the business impact of their teams. To learn more about Jellyfish, visit https://jellyfish.co/.

Why your data isn't showing the changes needed for success…

We sat down with Laura Tacho, CTO of DX, to break down where measuring developer productivity falls short. Laura shares how organizations should aim to balance making decisions based on both quantitative and qualitative data. Humans are not machines and numbers only go so far. She goes further and encourages companies to collect accurate qualitative data, avoid misalignment with leadership, and improve company culture.

Join us as we discuss: Understanding the ‘Why’ behind your metricsA framework for balancing quantitative and qualitative dataUsing metrics that best fit your current status (probably not DORA)

When measuring software developer productivity turns from enlightening to damaging…

We sat down with one of our own, Fernando Villalba, to discuss a controversial article on measuring software developer productivity. Fernando walks us through the different errors of this article when it comes to understanding the purpose behind different metrics and regarding low-value tasks as unimportant for a developer. He breaks down the complexities of a developer, and how a successful developer can find balance through communication, productivity, and finding the right flow.

Join us as we discuss:
Criticizing the lack of detail on developer’s metrics and the implications of different kinds of metricsThe limitations that coming with measuring productivity and interpreting different metricsSeeing the value in optimizing for Flow state

Christian Oestreich, Vice President Of Architecture at WEX, joins us to discuss how to prioritize your developer enablement programs, getting comfortable with soft and hard metrics, and best practices for using automation to improve developer enablement and keep your team in the flow.

Join us as we discuss:
Calculating the ROI of developer enablementHow to measure the impact of developer enablementFocusing on getting tasks that take minutes down to milliseconds

Bestselling author and technical leader and Head of Community at Amazon Web Services, Emily Freeman shares her insights around career, community, and business success. She touches upon the importance of the developer experience and its influence on productivity, measuring developer productivity, and different questions around using AI tools.

Join us as Ken and Emily discuss:
The critical link between the developer experience and business outcomes. How streamlining decisions and reducing unnecessary work to enhance efficiency, developer satisfaction, and ultimately increase profit.Investing in incremental improvements in the developer experience that focus on reliability, security, and performance.The role of AI in developer tools and the automation of repetitive tasks, which can free up time for more creative work.

AI and machine learning are becoming indispensable in maximizing system performance- for both developers and end users. But teasing out the right tools and methods can be challenging.

Philip Carter, Principal Product Manager at Honeycomb, sits down with us to discuss the developer experience and observability tools in the context of open-source telemetry projects- specifically with OpenTelemetry. He shares the positive impact these projects have had on the software community, especially the improvement in observability through open telemetry standards. He discusses the benefits of open telemetry for both vendors and customers, shedding light on the integration of machine learning into product development. Philip also delves into the role of observability tools in enhancing language models and data querying.

Join us as we discuss:
How open telemetry and observability drive Innovation for the software communityThe balance between the challenges and opportunities with AI augmentationBy putting developers at the center of software development efforts, companies will provide better products and higher customer satisfaction