Data Teams Shouldn't Be Firefighters

Data Teams Shouldn't Be Firefighters

Firefighter or Foundation-Builder?

Being on a data team feels like being in the eye of a storm.

At times, it feels like you’re spending all your time answering one-off questions and putting out fires. More fires than you could possibly handle.

We know you didn’t sign up for this just to run another quick query for the sales team or fix that broken product dashboard for the umpteenth time.

You’ve got bigger plans. Strategies to devise. Data architecture to perfect. You want to empower everyone else to answer their own questions so you can focus on the big picture.

Instead, you realize you're becoming the bottleneck. Serving everyone, but not really quenching the organization's thirst for data.

Becoming The Bottleneck

The truth is, when data teams become the bottleneck, this hurts the rest of the company, and it hurts the data team itself. Your grand plans for a robust, self-serve data environment get pushed to the back burner, and technical debt starts to pile up.

At the same time, those who can’t just open up a Jupyter notebook to get things done themselves... they’re just out of luck.

Minimizing Friction

Let's face it: not every question leads to a groundbreaking insight.

In fact, it’s a numbers game – for every 20 questions asked or hypotheses checked, perhaps one might lead to a significant impact. But here’s the kicker: you never know which one it’s going to be ahead of time.

If asking a question or testing a hypothesis is too cumbersome, it won't just be delayed; it's likely to get dropped completely, along with any potential insight.

When your data team is bogged down in firefighting mode, this friction is at its highest. Questions queue up, waiting for the data team's attention, and the momentum is lost.

By empowering the wider team to self-serve their data needs, you’re not just freeing up your data team's time, you’re actively boosting the company’s potential for impactful insights.

This isn’t about making everyone a data scientist. It's about creating an environment where curiosity is encouraged, and where people can make great decisions (yes – thanks to you, and thanks to data).

Building Bridges, Not Walls

So, how can you turn things around? How do you make sure that the next time sales have a question about quarterly performance or product wants to segment some cool new feature’s usage, they can find the answers (and more) themselves?

  1. Build Robust Data Infrastructure: Ensure your data stack is solid, reliable, and scalable. This means less time fixing things and more time on strategic initiatives, with the peace of mind that people are getting accurate insights.
  2. Implement a Self-Serve Data Platform: Choose a tool that integrates seamlessly with your modern data stack. Look for solutions that provide the ease of use necessary for non-data teams, combined with the robustness and reliability your data team requires. You’ll likely want everyone on the same platform!
  3. Maintain an Accurate Data Model: Tools like dbt make it easier than ever to transform and clean up your data. Modern data platforms like Supersimple add another layer of semantics to the data model, meaning people can accurately answer complex questions without starting from an empty slate.
  4. Be Intentional About Your Data Culture: Encourage people and teams to explore data on their own – to answer their own questions. With solid foundations in place, you won’t have to babysit every single little question! Not only will this free up the data team's time – it'll actually result in more findings, and more nuggets of gold.

One Step At A Time

We’ve seen data teams make this shift, turning their departments into powerhouses of efficiency and strategic insight. They’re not just answering questions; they’re driving the data culture forward and making sure everyone can be a part of it.

Chances are, you’re not building up a data function from zero here. Instead of solving every problem all at once, start small, focus on one department at a time, and watch as your role transforms from firefighter to strategic leader.

At the end of the day, the data team probably exists because of the fundamental belief that you can use data to make better decisions. Empowering everyone else is the only way to achieve this as a company… Even if you’d in fact like to hire millions of analysts instead.