How LinkedIn defines and tracks key developer productivity metrics | Grant Jenks (LinkedIn)
Dec 6, 2023
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Abi Noda chats with Grant Jenks from LinkedIn about their developer insights platform, iHub. They discuss qualitative versus quantitative metrics, choosing the right metrics, and using AI in developer productivity teams. Topics covered include winsorized means, composite scores, and the difficulties of answering simple questions.
LinkedIn's developer insights platform, iHub, collects build, CI, and deployment data to provide teams with valuable metrics and data for data-informed decisions.
Combining qualitative and quantitative metrics is crucial for obtaining actionable insights in the productivity space, as subjective human input and objective machine-derived metrics both offer valuable perspectives.
The Insights Hub platform at LinkedIn has evolved from a collection of dashboards to a suite of products that aim to provide actionable metrics, measure productivity, and facilitate operational reviews for teams at LinkedIn.
Deep dives
LinkedIn's developer insights platform iHub and its history
Grant Janks discusses LinkedIn's developer insights platform called iHub and its history. The platform collects build data, CI data, and deployment data using business intelligence tools like Power BI and Tableau. The goal is to provide a single destination for metrics and data, allowing teams to understand and manage their metrics effectively to make data-informed decisions.
Qualitative and quantitative aspects of insights
Grant Janks explains the qualitative and quantitative aspects of insights in the productivity space. The quantitative aspect involves telemetry from tools and services, while the qualitative aspect involves telemetry from people, such as surveys and satisfaction scores. By combining these aspects, actionable insights can be derived that are valuable for productivity work.
The challenge of qualitative and quantitative metrics
Grant Janks highlights the challenge of combining qualitative and quantitative metrics. Qualitative metrics derived from human input can be subjective, while quantitative metrics from machines are more objective. Finding a balance between subjective and objective metrics is essential, as both can provide valuable insights. Different lenses, such as relative versus absolute grading, can also be used to understand metrics better.
The insights hub platform at LinkedIn
Grant Janks discusses the Insights Hub platform at LinkedIn, which began as a collection of dashboards and business intelligence tools. The platform evolved to become a suite of products, including metrics rendering, persona experience, and operational insights. These products aim to provide actionable metrics, measure productivity, and facilitate operational reviews for teams at LinkedIn.
Challenges with using composite metrics
Grant Janks explores the benefits and challenges of using composite metrics. While leaders often prefer single numbers to represent performance, composite metrics can be complex to develop and interpret. They can increase noise and can be easily influenced by fluctuations. Comparisons over time may require recalibration. The trade-offs between simplicity and fidelity need to be carefully considered when using composite metrics.
In this episode, Abi chats with Grant Jenks, Senior Staff SWE, Engineering Insights @ LinkedIn. They dive into LinkedIn's developer insights platform, iHub, and its backstory. The conversation covers qualitative versus quantitative metrics, sharing concerns about these terms and exploring their correlation. The episode wraps up with technical topics like winsorized means, thoughts on composite scores, and ways AI can benefit developer productivity teams.
(1:10) Insights in the productivity space (7:13) LinkedIn's metrics platform, iHub (12:52) Making metrics actionable (15:35) Choosing the right and wrong metrics (19:39) The difficulty of answering simple questions (26:23) Top-down vs. bottom-up approach to metrics (32:12) Winsorized mean and selecting measurements (39:25) Using composite metrics (46:57) Using AI in developer productivity
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