Building Data-Driven Teams That Drive Results

When data is often seen as the heartbeat of successful businesses, it’s not enough just to have the right tools or a large volume of information. If you don’t have a data-driven team that can turn all that raw data into meaningful insights, you risk losing the competitive edge. Data isn’t just for analysis—it’s a strategic asset that can help us innovate, make better decisions, and improve outcomes across the board.

Looking back on my time leading data science and analytics teams for over 15 years, I’ve seen firsthand what works and what doesn’t. I’ve worked on platforms that increased data accessibility by 75%, made workflows more efficient, and boosted team productivity by over 50%. But through all of that, one thing I’ve learned is that building a high-performing analytics team takes more than just hiring people who can crunch numbers. It takes leadership, a solid strategy, and, perhaps most importantly, a culture of collaboration.

Today, I want to share the core principles I’ve learned from my experiences, along with some real lessons I picked up from working with teams at both big enterprises and fast-moving startups.

Start with Business Goals—Not Just the Data

One of the biggest mistakes I see is teams that dive into the data without considering what they’re trying to achieve with it. It’s easy to get lost in the weeds of analytics for analytics' sake. But, the real power of data is in how it helps drive specific business goals.

At Tradeshift, one of our most impactful shifts was to clarify why we were doing analytics in the first place. We got away from seeing the analytics team as a back-end function and instead positioned it as a strategic partner for teams like Product, Support, and Engineering.

Here’s what we did:

• We sat down for listening sessions with stakeholders to make sure we really understood their challenges.
• Built real-time dashboards that spoke directly to their team objectives.
• Made sure analytics delivered measurable results—things like improved response times or higher product adoption rates.

The results? Data was now something everyone could easily access and act upon—and ultimately, we saw a 75% improvement in data accessibility, which helped the business move much faster.

The takeaway here: Never start with the data itself. Always ask: What decisions do we need to make, and how will we measure success? Aligning data efforts with business outcomes turns analytics into something that drives real, impactful results.

Foster Collaboration Over Silos

Another crucial point: Collaboration is key. I’ve seen over and over how data teams can only truly succeed when they break down silos and work closely with teams like engineering, product, and even customer support. Getting everyone on the same page involved in the process from the start has huge benefits.

When I was working at PwC, we saw massive improvements in platform scalability and reporting efficiency just by focusing on stronger collaboration between business units and technical teams. Instead of traditional handoffs between teams, we created a new rhythm of collaboration that fostered faster feedback and iteration.

Here’s the process that worked for us:

• We put data roles directly within business teams so that there was a direct line of communication between those collecting the data and the people using it.
• We had weekly sync meetings where we could share progress across teams.
• And we adopted an agile workflow, ensuring that no one team had to wait long before seeing their needs being addressed.

By embedding data throughout the business, we saw a 50% increase in team efficiency. Platforms were delivered faster, operations ran smoother, and teams could make decisions more proactively instead of waiting for reports.

Here’s my big lesson: Don’t keep the data team isolated in a silo. Collaboration creates momentum, and quicker feedback loops mean faster results.

Hire Problem Solvers, Not Just Technical Experts

Now, on the hiring front—I can’t stress this enough. It’s so tempting to only focus on finding people with deep technical skills in data science or engineering. While those skills are crucial, the best people aren’t just those who can code—they’re the ones who can think critically, tackle business problems, and connect the dots between insight and action.

In my experience, I made sure to hire people who:

• Have curiosity—those who are driven to ask questions like, Why is this happening? What is the root cause?
• Can think critically about the business and the data itself, filtering out noise to focus on what matters.
• Can tell a compelling story with data, converting complex findings into easy-to-understand recommendations for the business.

For example, in one of our projects, we had to deploy predictive models. When we hired a few extra data scientists with a knack for business understanding, we were able to increase model accuracy by 30% just because the teams were aligned better from the start.

What I’d suggest for hiring:

• Don’t just focus on “technical tests”—give candidates real-world business problems to solve.
• Find people who can communicate clearly about their thought process. In data, it’s not just what you can do—it’s what you can make people understand.

Final lesson: Data teams need problem solvers who understand the broader business. If you hire the right people, they’ll be able to change the game.

Scalable Processes and Platforms Are Critical

Here’s the thing: As you scale your data initiatives, efficiency becomes paramount. If you haven’t built your processes and platforms to be scalable, you’ll hit a wall eventually.

When I worked with both PwC and Tradeshift, we saw huge improvements by focusing on scalable platforms, from cloud solutions to process automation. Cloud migration drastically improved data availability and eliminated slowdowns, while automation of reporting reduced manual errors and saved significant time.

The impact? We improved report latency by 40%, making our insights available quicker for decision-making.

So, here’s my advice:

• Plan your systems for future growth. Data isn’t going to stop growing anytime soon.
• Think about automation early on—automated dashboards, data pipelines, and processes are your friends.

Scalability is not optional; it’s essential. Building with growth in mind will pay off huge in the long run.

Develop Talent Over Time

Lastly, investing in your team is one of the best decisions you can make. Building a high-performing data team isn’t just about hiring rock stars—it’s about helping them grow and giving them the tools they need to succeed.

In my own teams, I focused on:

• Mentorship programs where juniors could learn from senior leaders.
• Access to learning platforms so everyone could grow in the tools and techniques that were most relevant to them.
• Giving people the chance to work on high-visibility projects—expanding their skill sets and confidence.

When you support your team’s growth, they’ll support your vision for data excellence. The result? A team that’s motivated, skilled, and loyal.

The real secret: People who feel valued and empowered will perform at the highest level. Keep investing in them, and you’ll see continuous results.

Let’s Chat

So, what challenges are you facing in scaling analytics within your teams? How have you aligned data with business goals? I’d love to hear your experiences—feel free to drop a comment or share what’s worked (or not worked) for you.

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