How to Use Git Data to Make Better Engineering Decisions

In today’s fast-paced world of software engineering, relying on gut feeling just isn’t enough. As teams scale and products evolve, engineering leaders are under increasing pressure to make decisions that are not only fast, but also accurate and impactful. But how can you truly understand what’s happening across your dev teams? Where are the slowdowns? Are your processes as efficient as they could be?

Enter Git data.

Git is more than just a version control system—it’s a real-time reflection of your engineering team’s workflow. Every commit, every pull request, every merge tells a story. When you step back and analyze these stories over time, you begin to see patterns. And those patterns can lead to meaningful, strategic insights that shape the way your team works and grows.

This blog dives deep into how Git data can help you lead better, make smarter decisions, and create a more productive, balanced, and aligned development culture. Let’s explore how to turn Git logs into an engineering crystal ball.

Why Git Data Matters for Engineering Teams

At first glance, Git logs may just seem like dry technical records of code changes. But if you look closer, they contain a wealth of behavioral and workflow data that reflects your team’s performance and collaboration.

Think of Git as a heartbeat monitor for your engineering org. It shows:

• How work flows (or doesn’t) through your system

• Who is contributing and at what pace

• How fast changes get reviewed and deployed

• Where the same code is being rewritten again and again

Most importantly, Git data gives you objective visibility—free from bias or opinion—into what’s really going on. This kind of clarity can help you:

• Catch delivery issues before they escalate

• Support developers at risk of burnout

• Align code activity with product goals

• Make better sprint, roadmap, and hiring decisions

And it’s not just for leadership—teams themselves can use Git insights to improve retrospectives, set realistic goals, and celebrate progress more effectively.

Core Git Metrics That Drive Better Engineering Decisions

Git data is vast, and not all of it is useful out of the box. Let’s focus on the metrics that provide high leverage—those that help you uncover bottlenecks, spot trends, and drive positive change.

Commit Frequency

Commit frequency shows how often developers push code. While this metric should never be used to judge individuals, it’s incredibly useful for spotting trends at the team or repo level.

If one sprint shows a consistent stream of commits across the team, but the next sprint has a sudden dip or spike, it may signal a change in workload, scope creep, unclear requirements, or even team health issues.

You can also look at when commits happen. Frequent late-night or weekend commits? That could be a sign of overwork, poor time management, or looming deadlines creating unhealthy pressure.

Encourage your team to make smaller, more frequent commits. It improves traceability, reduces merge conflicts, and supports healthier working habits.

Pull Request (PR) Cycle Time

This metric measures the time it takes for a PR to go from creation to merge. PR cycle time is a direct reflection of team velocity and collaboration.

When PR cycle time is short and stable, it usually means that code reviews are happening efficiently, responsibilities are clear, and the team is working in sync. If this number creeps up, it's time to investigate—are reviewers overloaded? Is the code too large or complex? Are there delays due to unclear ownership?

Even small delays at the review stage can snowball into slower feature releases and missed deadlines. Tracking PR cycle time helps keep your workflow lean and moving forward.

Review-to-Merge Time

This is the time it takes to merge a PR after the review is complete. It may seem like a small detail, but it’s one of the most underappreciated bottlenecks in engineering teams.

Sometimes a PR gets approved… and then sits untouched for days. This creates confusion and frustration. If it’s approved, why hasn’t it shipped? Who’s responsible for merging?

You can solve this by setting clear expectations around ownership, or by automating merges once approvals are in place. Either way, tracking this metric helps ensure code doesn’t get stuck in limbo.

Code Churn

Code churn refers to how much recently added code is being rewritten or deleted shortly after it was written.

A little churn is normal—especially in early-stage features. But high churn over time often means that something is broken upstream: unclear requirements, changing specs, or technical debt getting in the way.

This metric is a great way to open discussions with product and design teams. If developers keep reworking the same code, maybe the problem isn’t the implementation—it’s the inputs.

Unreviewed or Stale PRs

PRs that sit unreviewed for days or weeks can be toxic to morale. They signal to the author that their work isn’t valued or prioritized. They also risk merge conflicts, outdated branches, and duplication of effort.

A good engineering culture promotes regular, timely code reviews. Use Git data to spot idle PRs early. You can set alerts for inactivity or highlight stale PRs in your team dashboards.

Some teams even assign a rotating “review shepherd” who’s responsible for keeping the pipeline clean each week.

PR Size (Lines Changed)

Bigger is not better when it comes to PRs. Large pull requests are harder to review, test, and merge safely. They often include multiple concerns and are more likely to introduce bugs.

Encourage developers to break work into smaller, atomic PRs. Set rough guidelines (like 300–400 lines max), and pair this with review time metrics. You’ll likely see that smaller PRs move faster and get better feedback.

How Engineering Leaders Can Use Git Data Strategically

Let’s connect the dots. Once you’ve gathered the right metrics, what should you do with them? Here are some ways leaders can use Git insights to make smarter decisions:

Use Git Data to Guide 1-on-1s

Data-backed conversations lead to better outcomes. Instead of guessing how someone is doing, you can bring up patterns gently:

“I noticed a few late-night commits this week. Everything okay? Do you feel overloaded?”

It’s not about policing people—it’s about supporting them with context and care.

Improve Sprint Retros and Planning

Sprint retros often rely on memory and feelings. Add Git data into the mix, and you get a much clearer picture of what actually happened:

• Did we finish what we committed to?

• Were reviews a bottleneck?

• Did we push too many big PRs too late?

This helps improve forecasting and creates accountability around process—not individuals.

Link Git Activity to Product Goals

Ask yourself: is the work in Git supporting the company’s strategy?

By tagging PRs or using integrations, you can match engineering effort to specific initiatives, features, or OKRs. This is incredibly helpful for reporting progress to stakeholders and prioritizing future work.

Detect Burnout and Bottlenecks Early

One of the most powerful uses of Git data is detecting trouble before it’s too late. A team member doing 80% of reviews? Someone always committing after midnight? A component with 5x more churn than the rest of the codebase?

These are signs to take action. It could be as simple as redistributing work—or having an open conversation to reset expectations.

Final Thoughts: From Git Logs to Engineering Clarity

At its core, Git is a reflection of how your team builds. Every decision to commit, review, or merge leaves a data trail. When you learn to read that trail, you unlock a superpower.

Git data doesn’t replace empathy, leadership, or communication. But it supports them with context, clarity, and confidence.

When used right, Git insights can:

• Help you lead with data, not assumptions

• Improve delivery timelines and team efficiency

• Protect developer wellbeing

• Strengthen alignment between engineering and the business

You already have the data. Now it’s time to put it to work.