Data-driven development – going beyond productivity metrics

Metrics are a minefield. Just figuring out what to measure is hard enough, never mind figuring out how. We take a deep dive into data-driven engineering to understand how it’s typically done, and how we think it should be done to ensure you don’t put productivity on the podium alone.

What is data-driven engineering? 

Data-driven engineering is the practice of collecting data from engineering tools such as Jira and empowering developers so they can optimize the way they work. It creates a shift from the murky world of development, to a transparent and empirical approach based on fact. It’s simple to do, complicated to do well. 

Benefits of data-driven engineering

According to Accenture, data-driven organizations are growing on average 30% annually. But what are the specific benefits of data driven engineering?

1. Removes ambiguity around what is working, taking decision making away from the loudest voice in the room and instead promoting a scientific approach.
2. Motivating for engineers to know how they are performing (see our article on this here)
3. Enables more empirical planning and analysis around future work.
4. Allows for comparisons and trend tracking, giving further statistical depth to softer discussions in retrospectives.

How is developer productivity typically measured?

This is an area of contention, as a lot of the metrics that first come to mind are flawed. In fact, GitHub denote five metrics which many companies tend to first track, calling them the ‘flawed five’:

1. Commits

2. Lines of code

3. Pull requests

4. Velocity points5. “Impact”

They go on to explain why each is flawed, with rationale largely being around them incentivising the wrong behavior. For example, good code is concise, whereas tracking ‘lines of code’ will encourage developers to protract their code in order to rank well. This isn’t to say that some of these metrics are write offs, they have value, but only when applied in the right way alongside appropriate counter metrics.

This is where many tools currently on the market, such as Haystack, Athenian and Faro have limitations. They do a good job of surfacing productivity metrics, but they miss two key pillars of metrics that Adadot brings to the party; wellbeing and collaboration.

The cult of productivity

There is a lot of talk at the moment around the ‘cult of productivity’ with Harvard Business Review claiming that productivity metrics are diminishing creativity, and The Guardian talking about how the age of Slack and email is fostering unhealthy working practices, where we take work home far more often in search of maintaining our productivity. If our productivity metrics are encouraging unhealthy working practices, the question begs, are we incentivising the wrong things?

How can we measure wellbeing?

Productivity is important, but having appropriate counter metrics that ensure productivity practices are sustainable, seeking efficiencies rather than pillaging wellbeing, is a key tenet of what Adadot does. One wellbeing metric Adadot tracks is ‘out of hours comms’, which looks to address the typically unhealthy habit of checking emails and Slack late at night in a bid to bolster productivity. What we have found is quite the opposite, with medium to long-term productivity actually increasing when people afford themselves the time to switch off and decrease out of hours comms.

Adadot have worked closely with Evelina Dzimanaviciute, a Wellbeing Consultant who uses neuroscience to bridge the gap between organizational performance and individual wellbeing, to bring rigor to the wellbeing metrics we use. We settled on six metrics, bucketed under two groups:

Adadot integrates seamlessly with various tools to gather this data and do the hard work, it’s then down to the users to try to influence them, encouraging them to find ways to be more productive whilst not affecting their wellbeing. You can find out more about each metric, and see how you track, by signing up to Adadot free here.

How can we measure collaboration?

Having a team of productive and healthy developers is great, but if they’re not collaborating effectively they will never perform to their best.

Adadot is again unique in that it includes collaboration metrics alongside productivity and wellbeing to ensure developer metrics are also fostering positive collaboration. This focuses on two key buckets of metrics, ‘network’ and ‘time’.

Your dev team needs YOU

You are what you measure. If you only look at productivity metrics you are creating a false economy. Worse still, you are most likely over optimizing the wrong things. Are your metrics creating a cult of productivity (and burnout), or promoting healthy and sustainable working practices that will build the high performing teams of today and tomorrow?

Take a look at your metrics and the behaviors they are promoting, and look at widening the pool to wellbeing and collaboration metrics by using Adadot, the fitness tracker for work.