Why Data Teams Need More Than Just Good Code

Here's something that might surprise you: being great at coding and data visualization isn't enough anymore. At least, that's what Hugo Lu , founder and CEO of Orchestra believes – and he's got the experience to back it up.

A Surprising Start in Data

Hugo's journey into the data world started with a simple request. As a strategy team member, he asked his company's data team for help with reporting. Instead of just doing the work for him, they offered something better: "They said 'no way, but we'll teach you,'" Hugo recalls. That teaching moment sparked a journey that would eventually lead him to create Orchestra, a unified control plane for data operations.

The Hidden Problem in Data Engineering

After years in the field, Hugo noticed something interesting: data engineers everywhere were spending tons of time on the same repetitive tasks. "Every single data person I speak to is doing similar work; it's boilerplate platform engineering," he explains. Instead of focusing on innovative solutions or strategic analysis, skilled professionals were getting bogged down in routine platform engineering work.

Think of Data Like a Restaurant Kitchen

To understand why this matters, Hugo suggests thinking about data like a restaurant's supply chain. Just as a great meal depends on quality ingredients moving properly from farm to kitchen, good data analysis relies on data flowing smoothly through your systems. "If you don't have a resilient system... you risk an enormous amount of waste," he points out. One broken link in the chain can spoil the whole operation.

More Than Just Knowing SQL

"It doesn't suffice to just write good SQL and do DBT well," Hugo emphasizes. Many analytics engineers focus solely on their specialization – whether that's SQL, DBT, or another tool – without understanding how everything fits together. It's like knowing how to perfectly grill a steak but not understanding how the rest of the kitchen works. You need to see the bigger picture to really excel.

Enter the Control Plane

This is where Hugo's solution, Orchestra, comes in. Think of it as a central command center for all your data operations. Instead of juggling three or four different tools to monitor and manage your data pipelines, everything's in one place. "A data control plane is essentially a unified view of everything going on in your data pipelines," he explains.

The AI Reality Check

Here's a hot take from Hugo about AI: "AI is like putting the herbs on a dish at the end." In other words, if you're rushing to implement AI without solid data infrastructure, you're missing the point. You need quality ingredients (data) and a well-run kitchen (infrastructure) before you can start thinking about the fancy garnishes (AI).

Ethics: Going Beyond the Rules

When it comes to handling data responsibly, Hugo makes an important distinction: following regulations doesn't automatically make something ethical. Compliance is just the starting point. Real ethical data practices require thinking beyond the rulebook and considering the broader implications of how we use data.

Building a Better Data Culture

But here's the thing – even with all the right tools and understanding, success still comes down to people. Hugo emphasizes the importance of good communication and a balanced work culture. It's about "keeping a good balance between all types of work and keeping good lines of communication between everybody," as he puts it.

Looking Ahead

The data world isn't getting any simpler. But Hugo's insights offer a practical path forward: focus on understanding the whole system, not just individual tools. Build strong foundations before chasing the latest trends. And most importantly, remember that great data work is about more than just technical skills – it's about seeing the bigger picture and working together effectively.

The Bottom Line

Whether you're just starting in data or you're a seasoned professional, Hugo's message is clear: success in modern data operations requires more than just technical expertise. It needs people who understand how all the pieces fit together, who think about the ethical implications of their work, and who can collaborate effectively with their teams.

By focusing on these fundamentals – architecture, ethics, and team culture – data teams can build more effective and resilient systems. And in today's data-driven world, that's more important than ever.