Tailor Your Processes to be as Data-Driven as Your Systems

Engineering leaders recognize the importance of building observability into their software systems. Through testing, monitoring, and detailed reporting, it’s possible to thoroughly understand what’s working and what isn’t. We always know when our code is working – why not our teams? Engineering leaders often have little insight into how well development processes, hiring practices, and team structure are working: Typically, the first sign of a problem is when important deadlines are missed or developers start to quit. Leaders are left reacting after it’s already too late to head off the damage.. This talk will explore how to use data derived from Jira, GitLab, Bitbucket and and more to better understand how your team is working. Developers don't even have to change how they work – the insights leaders need are in the tools their teams are already using. Using both internal case studies and customer examples, we’ll discuss how to use metrics like Cycle Time, Review Speed, and PR Throughput to spot risks to your engineering team. We’ll also share best practices for using data responsibly. Attendees will learn how to combine engineering intelligence with skilled leadership to spot pipeline bottlenecks, coach developers, and debug processes before issues get too big to fix.


James McGill

VP of Engineering @Code Climate

James (Jimmy) McGill is a technology leader, coach, team builder, and maker who was raised in Australia and is currently based in NYC. He brings over 10 years of experience building software tools to Code Climate. In his role as VP of Engineering, Jimmy is dedicated to helping engineering teams achieve their highest potential. Prior to Code Climate, Jimmy served as the VP of Engineering at Button and as an engineering leader at Google. As a creative, Jimmy combines his experience as a woodworker, engineer, and roboticist to build unique pieces of fine art.

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Wednesday Oct 26 / 11:50AM PDT ( 50 minutes )


Pacific LM


Agile DevOps Development Effective Team Engineering Data Analytics


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