# Turning on the Lights

This post is part of our historical archive. It represents the beliefs, actions, products, and services of Code Climate as of its publication date. Today, Code Climate focuses on providing enterprise leaders the software development data, context layer, and playbooks needed to build the AI-native software organization their enterprise needs.

Jul 12, 2018

2 min read

## An introduction to Data-Driven Engineering

Welcome to the first installment of Code Climate’s new “Data-Driven Engineering” series. Since 2011, we’ve been helping thousands of engineering organizations unlock their full potential. Recently, we’ve been distilling that work into one unified theme: _Data-Driven Engineering_.

**What’s Data-Driven Engineering?**

Data-Driven Engineering applies quantitative data to improve processes, teams, and code. Importantly, Data-Driven Engineering is not:

- Ignoring qualitative data you don’t agree with
- Replacing collaboration and conversations
- Stack ranking or micromanaging developers

**Why is this important?**

Data-Driven Engineering offers significant advantages compared to narrative-driven approaches. It allows you to get a full picture of your engineering process, receive actionable feedback in real-time, and identify opportunities for improvement through benchmarking. Most importantly, quantitative data helps illuminate cognitive biases, of which there are many.

**What can Data-Driven Engineering tell us?**

After analyzing our anonymized, aggregated data set including thousands of engineering organizations, the short answer is: _a lot_.

Over the coming weeks, we’ll explore unique and practical insights to help you transform your organization. We’ll share industry benchmarks for critical engineering velocity drivers to help our readers identify process improvement opportunities. Here’s an example:

Pull requests merged per week (PR throughput) per contributor1  
This plot shows that **an average engineer merges 3.6 pull requests per week, and a throughput above 5.2 PRs merged per week is in the upper quartile** of our industry benchmark.  
You might be thinking, “Why do some engineers merge almost 50% more than their peers?”… and that’s _exactly_ the type of questions Data-Driven Engineering can help answer.

1 We included contributors who average 3+ coding days per week from commit timestamps.

## Looking Ahead

To build a successful engineering insights strategy, you need to go beyond just installing a tool. An insights platform can only work if it’s supported by a clear purpose, the right people, a well-defined process, and a program that rolls it out effectively. The combination of these elements will ensure that your insights platform isn’t just a dashboard—it becomes a powerful driver of change and improvement in your organization.

Remember, a successful software engineering insights strategy isn’t just about the tool. It’s about building a culture of data-driven decision-making, fostering continuous improvement, and aligning all your teams toward achieving business outcomes. When you get that right, the value of engineering insights becomes clear.