How engineering performance is understood - DORA and delivery metrics, OKRs, team health signals, reporting cadences, data quality, and the continuous improvement loops that convert measurement into action.
Six topics
Measurement without action is just surveillance. Each topic covers a specific dimension of how engineering organisations understand their own performance - and what to do with what they find.
DORA and Delivery Metrics
The four metrics that predict software delivery performance - and how to use them without gaming them.
DORA's four key metrics - Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recover - are the best validated measures of software delivery performance we have. This covers what they measure, how to instrument them, and how to improve your position without teaching your teams to cheat.
Read more →OKRs for Engineering
Objectives and Key Results done well create alignment and autonomy. Done badly they create bureaucracy and gaming.
OKRs are one of the most widely adopted and most poorly implemented goal-setting systems in engineering. This covers the original intent, why engineering OKRs are harder than product OKRs, how to write key results that actually measure outcomes, and how to make the quarterly OKR cycle useful rather than theatrical.
Read more →Team Health Metrics
Measuring the conditions that predict performance before the performance data tells you something has gone wrong.
Delivery metrics tell you how a team is performing now. Team health metrics tell you whether that performance is sustainable. Engagement, psychological safety, clarity, and cognitive load are lagging indicators of future performance. This covers how to measure them honestly and act on what you find.
Read more →Engineering Performance Reporting
Building the dashboards, cadences, and conversations that keep your engineering organisation honest.
Engineering performance reporting is not about surveillance. It is about creating shared visibility into how the system is performing so that the right conversations happen at the right level. This covers what to report, to whom, at what cadence, and how to make the data lead to action rather than just acknowledgement.
Read more →Data Quality and Metric Integrity
Metrics are only as useful as the data behind them. Most engineering metrics have data quality problems nobody talks about.
Before you can trust your engineering metrics, you need to understand where they come from, how they are calculated, and where they break. Data quality is not glamorous but it is the foundation on which every improvement conversation depends. This covers how to build metric integrity into your measurement system.
Read more →Continuous Improvement Loops
Measurement without action is just surveillance. This is how you close the loop.
The point of measurement is not to know things - it is to improve things. Continuous improvement is the practice of building systematic feedback loops that convert data into action, action into learning, and learning into better performance. This covers how to build those loops at team, domain, and organisational level.
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