Your data team has to produce solid data. The pipelines have to run, the logic in your transformations has to be sound, and the report has to show accurate revenue. Those fundamentals are hard to argue with. But if that’s all you’re doing, your team is probably bored and your organization definitely isn’t getting as much value as it could out of its data.

Open-ended creative work is a huge part of the appeal of working in this field - identifying opportunities to improve processes, appeal to new customers, or build better products adds value for the organization, but it is also just incredibly personally satisfying. One of the fundamental challenges of managing a data team is balancing the need for rigor and reliability with the team’s desire to spend most of their time creating new knowledge. How do we manage those sometimes conflicting priorities?

I recently read The Culture Code by Daniel Coyle, and among the many gems about building great organizational cultures, one idea stood out to me as a helpful framework for thinking about this challenge: managing for proficiency versus managing for creativity. Proficiency-focused organizations must consistently and reliably perform clearly defined tasks, while creativity-focused organizations require consistent innovation and fresh ideas.

Coyle argues that great organizations know whether they need to optimize for proficiency or creativity, and take a single-minded approach to building the culture and practices that support that goal. You can look at companies like Pixar, an incredible creativity factory, and Ford, an incredible, well, factory factory, and the benefits of focusing entirely on one of these priorities is clear. But successful data teams require both proficiency and creativity - so how do we lead teams that balance the two?


Proficiency is critical for data teams: unreliable systems or inaccurate data destroy the trust placed in us. Without proficiency in data collection, transformation, and reporting, the value of anything built on top can become highly questionable.

Coyle describes that leading high-proficiency groups requires emphasizing clear, simple practices that align behaviors with consistent, reliable outcomes. A lot of engineering best practices increasingly adopted by data teams are proficiency-focused. Examples of these practices include:

Creating a team culture that values these behaviors (ensuring they don’t become under-appreciated glue work) increases the reliability of results and protects the institutional capital of the Analytics team. We’ve covered a lot of tactics here on Locally Optimistic to build the data quality flywheel and improve the focus on proficiency within the data team (with help from the rest of the company).

One other tactic I’ve found incredibly useful is to define user trust as a key metric for the data team. It’s one thing to know that occasionally, things break - it isn’t ideal but you’re working to improve it. It’s another thing to know that because something occasionally breaks, your colleagues have trouble trusting the output of the data team more generally. To get that kind of action-inspiring feedback, we survey the company about every 4 months. Questions cover whether folks can access the data they need, understand the data they have, and most importantly to me, whether they trust the data resources they use. Keeping that number moving in the right direction helps motivate the team to ensure proficiency. Fair warning, though - the results from the first survey can be very humbling, so make sure you’re ready to embrace the feedback as constructive!

But as much as I value a robust data warehouse and accurate reporting, that isn’t what has kept me working with data over the years.


The creativity of analytics is why I show up to work each morning, and I think that’s true for many of us. We want to turn data into new knowledge and products - to learn from the past in order to shape the future. We want to explore and uncover new insights that suggest a path forward for the business. We want to predict who is going to churn instead of reporting on who churned last week. We want to create beautiful and signal-rich visualizations that allow our colleagues to immediately grasp relevant trends and patterns. And though I argued that the outputs of analytics engineering encourage proficiency just a few paragraphs ago, I think the emergence of analytics engineering as a discipline is also an act of creativity. Very smart folks are thinking outside the box to create tools and processes that apply engineering best practices and solutions to the problems analysts face.

Leading for creativity means fostering systems that consistently churn out fresh ideas, even if many of those ideas don’t turn out to be successful. Your team has to understand that failure is not just a possibility, it’s a necessary output of both research and creative processes. Every failure is an opportunity to change your perspective, reframe either the problem or the solution, and over time home in on a more refined idea of what approaches are likely to be fruitful.

To generate creativity, empowerment is critical - providing support and tools to enable the team and letting them loose. Creativity also requires a real focus on the team’s dynamic and sense of psychological safety. Teams should celebrate individuals who take initiative and generate new ideas, but not all those ideas will ultimately be good ones. Folks need to feel safe suggesting something they aren’t quite sure is fully baked, as well as giving authentic feedback on someone else’s idea in an honest but respectful way.

Tactically, enabling creativity requires time to work on less structured projects and a clear path to pursue ideas. Folks need time for explorations that may not always result in clear outcomes. Sometimes you spend half a day investigating something you think will be meaningful, and it’s just not - or weeks building a model only to find out that you just can’t predict the outcome with any useful level of accuracy. That time wasn’t wasted if you learn something from it.

The Balance

The magic of a data team is the combination of rigor and open endedness - it isn’t a pyramid, where we need one to enable the other as an ultimate goal. It’s more complementary and interconnected - the yin of proficiency and the yang of creativity, if you will. Focus too much on proficiency, and you’ll have accurate reports created by people who are probably bored every day and stakeholders who don’t really understand the value that the data team can add. Enable too much creativity, and you’ve got folks making decisions based on questionable data or a team that works on projects that are interesting but not relevant to the business.

Maintaining this balance relies on building a team culture that celebrates both rigor and exploration, but also requires a concerted effort just to make time for creative projects. It’s easy to get pulled into an endless queue of ad-hoc tasks and data quality investigations, but you can consciously make space for creativity.

Here are some of the ways I try protect the balance and encourage teams to have a healthy respect for both proficiency and creativity:

How does your team balance the need for accuracy, rigor and reliability with the need for creative freedom? I would love to explore this more. Send me an email, come have a chat in our Slack channel, or join us June 23 at 3pm EDT for a live conversation!