“😩 My data team isn’t working!!!”
No matter how much we invest, no matter how much technical knowledge they have, projects get delayed, no one understands the business, and everything feels like a constant mess.
Does this sound familiar?
If you lead a startup or a data team, you’ve probably been there. And the worst part is that the problem isn’t technical capability. The reality is that there’s something deeper at play: diffuse leadership, lack of alignment, and a culture that doesn’t quite engage.
So how do you transform your data team from a headache into a high-performing team? Well, you have to find the right balance between demanding and empowering. Incorporating both the perspective of empathetic leadership and the more intensive vision of Elon Musk or Sergey Brin, and this can be summed up as:
- ✅ Execute with rigor (without killing the team in the attempt).
- 🚀 Give autonomy (but with clarity about the expected results).
- 🎯 Flexibility at work (but with well-defined expectations).
Now, let’s look at how we put all of this together with concrete actions to transform your data and AI team.
The breaking point between chaos and excellence
Good data leadership isn’t just about coordinating projects and ensuring they’re delivered on time. It has to inspire, challenge, and set the course. And above all, it has to understand that a data team isn’t just any team. It needs structure, but also creativity and fluidity.
How is this achieved?
- 🎯 Clear and aligned objectives: Each person on the team needs to understand how their work impacts the business. Using OKRs or KPIs helps focus on what really matters.
- 👨💻 Technically sound leaders: They don’t need to code every day, but they do need to be able to evaluate solutions and make informed decisions.
- ⚖️ Autonomy with accountability: If you give your team freedom without defining what you expect from them, you end up in chaos.
- 💬 Room to talk and improve: A team that can say “this isn’t working” without fear of being blamed is a team that grows quickly.
- ⚡ Balanced demand with well-being: If you want a high-level team, you need it to operate sustainably. Don’t burn them out or leave them aimless.
If you don’t retain talent, it will be stolen.
Data and AI talent is more in demand than ever. If your team doesn’t feel like they’re learning, making an impact, or having fun, they’ll be taken out of your hands.
Key actions to avoid it:
- 🚀 Culture of experimentation: Give them time to innovate and try new things. If we only tie them to operational tasks, they’ll get frustrated.
- 🛡️ Psychological safety: If no one dares to make mistakes, no one innovates. We must allow for mistakes and learn from them.
- 🏆 Purpose and recognition: Motivated teams know why their work matters. Make sure they see the real impact of what they do.
- 🌍 Diverse teams: Diversity of thought generates better solutions. If the same voices are always heard, ideas end up being limited.
- 🕒 Flexibility with accountability: Remote or hybrid work, yes. But with clear goals and concrete expectations.
Without soft skills, technique is not enough
You can have the best data scientist in the world, but if they don’t know how to communicate what they’re doing, they’re useless. The technical team has to learn how to work with the business.
Key skills to develop:
- 🗣️ Communicate well: Translate data into actionable decisions.
- 🤝 Teamwork: Integrate data with other areas so the business can truly use the information.
- 🧐 Critical thinking: It’s not just about “making models,” but about understanding what problem they’re solving.
- 🚀 Proactivity: Don’t wait for someone to tell you what to do, but suggest improvements.
- 🔄 Adaptability: Technology changes all the time. If you don’t keep up, you’ll fall behind.
You Have to Structure Innovation
It’s not enough to ask the team to “be innovative.” They need to be given the tools and methodologies to truly make that happen.
Some keys:
- ⚡ Agile methodologies: Scrum or Kanban help maintain focus and speed.
- 🎨 Design Thinking: Promotes user-centered solutions.
- 🏆 Internal hackathons: They allow you to test ideas and break away from routine.
- 🤖 MLOps/DataOps: Bring AI models into production quickly with quality and scale.
- 📢 Collaborative platforms: Slack, Notion, Jira… any tool that improves internal communication.
But above all, we have to start!!!
Now that you know where things are going, it’s time to move the pieces.
- Identify weaknesses: Are their objectives clear? Does leadership promote autonomy and accountability? Does the culture encourage innovation?
- Define a roadmap: Don’t try to change everything at once. Adjust gradually.
- Measure the impact: If you don’t measure, you won’t know if it’s working. Define clear KPIs and review them frequently.
Don’t expect change to happen overnight. But if you adjust something every week, in just a few months you can transform your data team into a true competitive advantage.
Now I ask you: what is the first thing you are going to change?
And if you need help implementing any Data/AI project, don’t hesitate to send us a message.