Scaling a Data Team
Published:
Moving from putting out fires to proactive growth
In the early days, I spent a lot of time running around, solving immediate data needs and putting out fires across different departments. Those quick wins demonstrated the value of data, but as my influence grew, so did the workload. Scaling a data team involves moving beyond reactive problem-solving toward proactive strategy. Here’s what I’ve learned about growing a data team, building a solid strategy, and creating a data function that supports the entire company.
Make a rough strategy and refine as you go
When you’re just getting started, building a polished strategy can feel impossible. But having a rough plan is better than having none. One of my first Google searches was literally “Make a strategy for a data team.” My early strategies had gaps, but those first plans gave me a foundation to build on as I gained experience.
Two things quickly stood out: I needed to build my leadership skills, and we’d eventually need more people on the team. Even when I was a solo data person, I was still the data leader for the company, and leadership skills were essential. I needed to understand the company’s goals and how data could help achieve them, but I also had to communicate that effectively to get others on board.
Advice:
- Don’t wait for the perfect strategy; start with a rough plan and refine it as you go. Focus on developing leadership skills and keep an eye on potential team growth.
Scale your tech stack to meet growing needs
As the demand for data grew, I realised my basic tools and workflows weren’t cutting it anymore. I was helping every department – from marketing to sales, customer success, and product. I was building KPI trackers, customer success dashboards, product performance reports… you name it. But with all these responsibilities, manually updating reports wasn’t sustainable.
This is where having “data cheerleaders” really paid off. They helped prove the value of data across departments, which made it easier to justify investing in better tools. We upgraded to a proper IDE for SQL, moved to a laptop with a stronger processor, and eventually shifted from Data Studio to a more advanced data analysis tool. These changes made our workflow faster and more efficient, allowing us to start thinking strategically.
Advice:
- Evolve your tech stack as your team’s responsibilities grow. The right tools make your team’s work more efficient and free up time to focus on strategy.
Build influence across the organisation
Building strong relationships across the company has been just as important as building our tech stack. As our influence grew, it became clear that regular check-ins with other teams were essential to keep us aligned with company goals. Each month, I set up a time with every team to talk about their data needs, share ideas, and address any complaints about data. These meetings keep us agile and make sure we have a finger on the company’s pulse.
But it’s not just about formal meetings. Building a data-driven culture also means making it easy for people to reach out to the data team. At Signable, we use a request form to keep track of data projects and automatically put them in our backlog for the next sprint. And then there’s my personal favourite: coffee chats. I make it a point to have a coffee chat with every new employee. It’s a chance to get to know them and make them feel welcome, but it’s also an opportunity for them to learn who to go to with data questions or ideas.
Advice:
- Schedule regular check-ins with departments, make it easy to request data support, and build relationships. The more people feel comfortable coming to you, the easier it is to create a data-driven culture.
Prioritise transparency to build trust
In data, trust is everything. The moment you publish a dashboard with mistakes, people lose confidence in your team, and they’ll start going elsewhere for information. That can lead to inconsistent data across the company, with different definitions for the same metrics. And once trust is lost, it’s tough to get back.
To prevent this, establish strong QA processes as your team grows. At Signable, we make sure every report goes through a review process for accuracy, leave comments in our code, and use a version-controlled repository. These simple practices ensure that our data is reliable, and everyone across the company can count on it.
Advice:
- Implement QA processes early to keep your data clean and accurate. Transparency and accuracy are essential to build and maintain trust across the business.
Positioning Your Data Team for Long-Term Success
Scaling a data team isn’t easy, but with the right approach, you can create a lasting impact. Start with a rough strategy, invest in the right tools, build influence by connecting with others in the company, and prioritise transparency. These essentials helped our data team expand its reach and support company goals, and they’ll help you position your team for long-term success too.
I wrote these two pieces to share what I’ve learned in hopes it can help others navigate similar challenges. If you’re building or scaling a data team and have questions – or want to share your own experiences – feel free to connect with me on LinkedIn. I’m always open to discussing all things data, strategy, and scaling with like-minded professionals!
Thanks – MJ
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Marijn (MJ) Quartel
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