Can data explain the creativity of developers?

Written by Christina Forney, VP Product at Uplevel

As someone who’s been an engineer, managed engineering teams, guided them as a product manager, and advocated for them as VP of Product for several dev tools, I can confidently say that the most innovative and impactful developers are a lot like artists.

Why does this matter? Because businesses aiming to differentiate on software need to understand what drives their most innovative teams.

In my experience, it’s not the analytical, coding aspect of development that business stakeholders struggle to understand. It’s the creativity that excellent software engineering requires. Creativity isn’t easily captured in conventional metrics that measure things like ‘productivity’ or ‘efficiency.’ Although crucial, the creative aspect of development and its impact on business outcomes can’t be measured or quantified… or can it?

Software development: Science and art

The best software developers are deeply creative, and they leverage tactics like iteration that are often found in creative fields like art and music (many software developers actually are musicians, and vice versa).

Why? Because the hard problems developers face require creative solutions. Every challenge presents an opportunity for imaginative brainstorming, involving collaboration with various teams across engineering, product, and design. This ‘non-coding’ time, often overlooked, is actually crucial, as it lays the groundwork for productivity.

However, coding is contextual, and large systems become highly complex with constraints and dependencies. Finding an intermittently failing bug or detangling a complex mess of legacy spaghetti code requires exceptional problem-solving and out-of-the-box thinking.

What creative development requires

Productivity and efficiency metrics tend to be lagging indicators — they show how much code is pushed at the end of the day, and this feeds into the misconception on the business side that developers should be doing nothing but coding.

The reality, however, is that the actual writing of code is minimal relative to the problem understanding, solution brainstorming, dependency mapping and other tasks that engineers simply must do to have their code succeed. Thinking, learning, researching, and experimenting are critical — and they require nothing as much as time to focus.

In my own research, I looked at some of history’s other prolific creatives: writers and artists. How were they able to be so productive? The trend became clear rather quickly:

  • Andy Warhol created approximately 21,000 pieces of art. He maximised his productivity by experimenting and innovating new ways to automate art making.
  • Stephen King has written over 60 novels and 200 short stories. He emphasises the importance of focused work time without ‘going to Huffington Post to see what Kim Kardashian is up to.
  • Ancient Greek orator Demosthenes wrote 258 speeches during his political career. He would shave half his head to resist the temptation of distractions outside his home.
  • Pablo Picasso created over 147,000 pieces of art. He would often paint late into the night to minimise disturbances or distractions.

Writing code requires similar conditions. For engineers, figuring out hard problems means getting into flow — iterating, failing fast, failing often, and trying again. This flow state, or ‘Deep Work‘, as Cal Newport defines it, is how real engineering work gets done — and it contributes to more engaged, more productive engineers.

The stark reality

Sadly, this idyllic portrait of an artist’s environment is not the reality most of us face.

Software developers are rarely given the space or time to do deep work. For example:

  • We have eight JIRA tickets to close out today and are context-switching as we jump between them.
  • We get pulled into a status meeting for one project, start to get back into the groove, and then it’s time for another sync or standup.
  • We’ve pulled off an important value demand epic to do incident response and urgent KTLO work.
  • We’re pinged on Slack every five minutes.
  • We can’t get what we need to close out a ticket because it’s blocked by another dependency in another department, and the acceptance criteria for this task change every five minutes.

What if Picasso had five people knocking on his door every hour? Would he have been as prolific as he was if every commission involved the customer changing their mind halfway through?

How tech leaders can enable art through data science

Every engineering leader I’ve worked with can intuit what is slowing things down in their organisations. But it’s incredibly difficult to create change and promote more generative, creative cultures when you don’t know for sure.

Data might feel like a decidedly non-artistic tool, but it turns out that even the most creative people throughout history were experts when it came to smart time allocation. Data enables creativity and innovation at scale by providing insight to leaders of large engineering organisations.

Demosthenes knew he was a chatterbox, so he shaved his own head (when n=1, it’s easy), but it’s much harder to protect and promote deep work when the demands on your time and focus are external and when you don’t know where the inefficiencies and blockers lie.

For instance, using machine learning models to analyse meeting calendars and chat metadata anonymously can unveil work patterns such as how time is spent responding to interruptions at the team level or how many teams are at risk of burnout due to being ‘always on.’

Knowing this can kickstart the necessary discussions and initiatives to affect change. Streamlining scrum meetings, establishing ‘quiet’ hours, or setting chat response expectations during focused work time can enhance productivity and foster deep work. Data itself won’t solve the problem, but having visibility is the first step.

Creativity under fire

Engineering is under more scrutiny and pressure to deliver business results than ever before. Between ramping up productivity and efficiency to do more with less and striving to deliver value-demand work, it’s not an easy time for tech leaders. But maintaining productivity isn’t just ‘engineers in, code out.’ It’s important that leaders are able to make data-driven arguments about the root cause of what’s holding their teams up — and the value that creativity brings to the engineering org and the business.


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