Let me start with a question.
Is the job really done when the code reaches production, or is it just the beginning of a whole new phase?
While many teams celebrate the speed with which they make their code slide through commits to production, only a few take care of what happens after the code goes live.
This post-deployment phase, where real-world testing, user feedback, and critical bug fixes happen, is often overlooked.
Though incident metrics like Mean Time to Acknowledge (MTTA) and Mean Time to Restore (MTTR) are metrics generally dealing with incidents after deployments, there is no other concrete way to measure how quickly and effectively deployed code stabilizes, performs, and delivers value to end users.
In many cases, after the initial excitement of deployment fades, teams assume the job is done, and they look back to it only when some critical issues occur. But the real work isn’t purely about making code live, it is also about making it value-driven.
In between these, three concepts collide.
- Lead Time for Changes - The time it takes from code commit to successful deployment in production.
- Deployment-to-Value Time - Duration between a deployment's completion and when it's fully operational and available to users.
Speed-to-Value - The time it takes from code commit to successful deployment and beyond till it delivers value to users.
The reason I am today putting Lead Time for Changes, Deployment-to-Value Time, and Speed-to-Value in the same bucket is - if we focus solely on deployment speed by grinding our commit to production process, we might miss the critical aspect of long-term system health, performance, adoption, and value.
The true measure of any deployment is how quickly the system gets ready to face the user front, pull values out of them, and feed it back into business.
Simply put, post-deployment performance is as important as pre-deployment speed, both technically and commercially.
And that’s why, though these terms (Deployment-to-Value Time and Speed-to-Value) aren’t widely popular and adopted, I strongly believe in their relevance and use cases when we are discussing Lead Time for Changes.
Understanding Deployment-to-Value Time and Its Key Components
The deployment phase is where code must prove its value. Once code is live, it still needs time and effort to stabilize, become fully operational, and start delivering real value to users. This is where Deployment-to-Value Time comes into play. It measures exactly the same - time from deployment to value.
Though this is not a part of DORA or any other popular metric system, it makes a lot of sense when priority of every deployment is not just about pushing code to production successfully but deriving business values out of it.
Beyond business values, Deployment-to-Value Time gives a deeper understanding of the engineering brilliance the team carries. Because poor Deployment-to-Value Time shows issues with code quality, CI/CD pipeline efficiency, configuration management, release coordination, observability practices, developer experience, and feedback loops.
The following are its key components.
- Post-Deployment Testing and Validation: The deployed code must function as expected in the real world under real usage conditions.
- Monitoring and Performance Tracking: After deployment, it is crucial to actively monitor how the system performs in production.
- Bug Fixes and Incident Management: Once the code is deployed, hidden bugs often surface. MTTA (Mean Time to Acknowledge) and MTTR (Mean Time to Recovery) are crucial metrics to track how quickly teams are able to respond to and fix production incidents.
- User Feedback and Adoption: After deployment, the team should actively collect feedback from users along with the adoption rate of released features and its measurable impact to make strategic business decisions.
Resource Optimization and Cost Control: It is important to actively review how cloud resources are used to balance out unused or underused resources without degrading performance.
Realizing good Deployment-to-Value Time is not only about mastering all the above components or achieving the highest technical stability, it is also about putting all these things with regards to one single goal - the real business value.
How Poor Lead Time for Changes Impacts Deployment-to-Value Time
We often see Lead Time for Changes as a pre-deployment metric and purely focused on speed. However, inefficiencies in this stage can easily ripple into the post-deployment phase.
The following are the top reasons causing slow Lead Time for Changes - with its corresponding impact on Deployment-to-Value time.
- Lengthy approval and review cycles, which slow down Lead Time for Changes, also delay the feedback loop and slow down real-world validation post-deployment.
- Excessive rework due to unclear requirements, which slows down Lead Time for Changes, also results in unstable features needing immediate post-deployment fixes. And this delays value realization.
- Poor test automation or missing test coverage, which slows down Lead Time for Changes, also increases bugs in production and extends the time window for post-deployment stabilization.
- Context switching and multitasking, which slow down Lead Time for Changes, also lead to lower code quality, demanding more post-release patching.
- Siloed teams and poor cross-functional sync, which slow down Lead Time for Changes, also cause misaligned releases and dependency failures.
Heavy reliance on manual deployment processes, which slows down Lead Time for Changes, also increases the risk of deployment errors, leading to a higher Change Failure Rate and extended Deployment-to-Value Time.
The Business Impact of Poor Lead Time for Changes and Poor Deployment-to-Value Time
Since now I have established that poor Lead Time for Changes ripples into Poor Deployment-to-Value time, the business impact of both is more interconnected than we often admit.
To make it easier for you, let’s understand with an example of a tech company offering a cloud-based project management tool decides to release a new feature: an AI-powered task prioritization system.
- Increased Costs & Extended Release Cycles
- Lead Time for Changes: A slow approval process, miscommunication, and the lack of automated testing make the development team face frequent delays. As a result, the AI feature which was planned to release in one month is now delayed by 2 months.
- Deployment-to-Value Time: Once the feature was finally deployed, it took several weeks to stabilize the system. The team was fixing post-deployment bugs. During this time, the feature failed to provide any value to the users. It only led to increased operational costs associated with bug fixes, monitoring systems, and customer support.
- Delayed User Satisfaction & Missed Market Opportunities
- Lead Time for Changes: Users were excited about this new AI feature. Many decided to upgrade the plan for this new upcoming feature. But due to slow development, the release was significantly delayed, and users began to turn to competitors.
- Deployment-to-Value Time: When the feature finally became available, it was full of bugs, and it took an additional few weeks to make the feature fully operational. By the time the feature became fully operational, users had either switched to other platforms or lost interest in this new feature.
- Customer Experience & Trust
- Lead Time for Changes: Since the company has a historically bad reputation of releasing poor quality of late features, users started questioning long-term sustainability. It made them lose trust in the product.
- Deployment-to-Value Time: After the feature was finally released, it failed to work seamlessly. This led to negative feedback and dissatisfaction among users. Value was nowhere near the horizon.
- Developer Burnout & Morale Drop
- Lead Time for Changes: Due to rushed development and pressure to meet deadlines, engineers lived on the verge of burnout. They became prone to errors, and their anxiety piled up before every release as they were well aware of the corners they had cut to meet the timeline.
Deployment-to-Value Time: Post-deployment, the same team was pulled back into the same situation - lengthy review process, manual efforts, context switching, unrealistic timelines, and more burnout. That was a never-ending loop of rushed development > error-prone deployment > urgent post-deployment fixes > more rushed development to catch up > growing tech debt > rising pressure > declining quality > a point of no return!
How Can You Incorporate Lead Time for Changes with Deployment-to-Value Time?
The short answer - Well, it’s a mindset shift.
The long answer -
- Redefine Done: Instead of stopping at code deployment, consider ‘done’ when the user experiences the intended value.
- Connect Developer Metrics with User Impact: Link developer productivity (speed, efficiency) with post-deployment outcomes like feature adoption rate, time-to-stability, number of required hotfixes, and user-reported issues. This will give you a holistic picture of human-tech factors affecting delivery and value realization speed.
- Feedback Loops that Don’t End at Prod: Create feedback systems that continue beyond deployment with real-time monitoring, feature flag analytics, A/B test results, and user behavior heatmaps.
- Collaborate Across Functions: The culture of ‘this is not my job’ is very dangerous. So, build a collaborative culture and involve product, QA, SRE, and customer support in the post-deployment phase.
- Integrate MTTA, MTTR, and Change Failure Rate with Lead Time Metric: Use MTTA and MTTR to gauge how quickly your team notices and responds to post-deployment issues and how long your team takes to fix the issue in production. Moreover, add Change Failure Rate to understand the percentage of code deployments that cause failures in production. This combined metric system will help you track not just how fast you ship but how reliably that code delivers value to users.
How to Track Lead Time for Changes with Hivel?
Hivel is an AI-powered software engineering intelligence platform. It makes it effortless to measure and track Lead Time for Changes of different teams and projects through a single intuitive dashboard.
By deeply integrating with your CI/CD and SDLC tools, Hivel captures the full journey of a new code or code change. After mapping code-to-commit-to-deployment time, Hivel automatically calculates both speed and quality metrics.
This end-to-end visibility helps you measure and track not only Lead Time for Changes, but also Deployment Frequency, Coding Time, Review Time, Merge Time, Pickup Time (duration between when a PR was opened to when the first comment was done on the PR), Change Failure Rate, Maintenance, Rework, MTTR, PRs Merged without Reviews, Flashy Reviews, Burnout Rate, MTTA, MTTR, and PRs with more than 400 LoC.
By applying AI to this comprehensively collected data, Hivel delivers in-depth, context-rich insights into speed or quality metrics, including Lead Time for Changes you are aiming to track and improve.
So ultimately, Hivel empowers project managers and tech leaders to replace guesswork with data-backed strategic decisions that streamline speed-to-value cycle - from code commit to value delivered!
Get Started with Hivel for Free to Accelerate Your Journey from Commit to Value
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