Why Do Fractional Data Teams Work?

Beyond the theory of fractional data teams lies the practical reality: how do they actually work in practice? Having worked with companies from early-stage startups to scale-ups, we've seen distinct patterns emerge in how teams successfully leverage this model.

Meeting Companies Where They Are

Every company's data journey is different. For Tiney, a 50-person childcare platform, it started with strong foundations. As their CTO explains:

"I built the data capability right from the beginning. We adopted BigQuery early and made sure every bit of data we could track was being hoovered in. Not with a particular strategy at first, but knowing that at some point in the future, we'd probably want to look at it."

This forward-thinking approach served them well initially. But as the company grew, so did the complexity of their data needs:

"Over the first few years, we built enough capability that we could create dashboards and basic analytics ourselves. But we reached a point where that setup had become unwieldy. The backlog of requests kept growing as more people became interested in data."

For Plain, the starting point was different. As Matt, their CTO, describes it:

"Outlier literally took us from 0 to 1. When they joined, we had no ETL or BI tooling of any kind. We hadn't even picked a BI tool. So really, they saw us through from the very beginning to where we are today."

Real Projects, Real Impact

The real test of any data team is in the work they deliver. Here are some examples of how fractional teams have driven meaningful change across different types of organisations:

Customer Service Analytics

For Tiney, this meant combining data from multiple tools into meaningful insights: "They've helped us ingest all our customer service data into our warehouse and build really interesting, bespoke analytics that looks at data across multiple tools. This has given our head of customer service insights to drive improvements in our SLAs and quality."

Growth Analytics & Funnel Optimization

At Ditto, the focus was on user activation tracking. The team implemented a modern data stack to define and track core activation metrics, enabling real-time monitoring of user engagement and identification of bottlenecks in the process. These insights directly influenced their product roadmap and even drove a major redesign. Ellie, their Product Marketing lead explains:
"We noticed that one of the five core actions had very low adoption, and that was really skewing our activation data in a bad way. Part of the redesign is making it stupid simple for users to be able to do that."

Customer Facing Analytics

For Plain, the fractional data team's impact extended beyond internal analytics to become a core part of their product offering. As Matt from Plain describes:

"Over time, one of the most interesting things for us was that it grew from being mostly about internal BI and data to helping out on our actual reporting features in-product."

The fractional team supported Plain's project to build customer-facing analytics directly inside their SaaS product, working closely with Plain's engineering team to implement real-time data APIs. This collaboration allowed Plain to launch a dedicated reporting for support teams with near-real-time data, satisfying one of their most popular customer feature requests.

Working With Your Team

One common concern about fractional teams is integration with your existing team. In practice, we've found flexible engagement models work best. As Tiney's CTO describes:

"Sometimes a project is very well scoped and documented, and we just need someone to deliver it. Other times, like with our customer service analytics, it's more open-ended and needs a true partnership approach. Both setups work pretty well."

The key is fitting into your existing workflows:

• Weekly standups to coordinate priorities

• Direct collaboration with relevant team members

• Shared communication channels

• Regular strategy sessions

Matt from Plain describes how the fractional team seamlessly integrates with their workflow

"I was initially concerned that it would be a bit of an agency-client kind of relationship and vibe, but I found it really great how it does just feel like it's a team member in the team. Perhaps they don't join stand-up every day, but…I can Slack them, I can ask them random questions, I can spontaneously huddle—all those things that you normally see and do with anyone else in your team."

Measuring Success

While ROI can be measured in multiple ways, our clients typically see impact in three areas:

1. Time Savings

• Automated reporting processes

• Reduced ad-hoc query requests

• More efficient decision-making

2. Strategic Value

Fractional data teams enable better strategic decision-making across the organisation. At Ditto, insights from activation data now directly drive their product roadmap, ensuring development efforts focus on features that truly drive user adoption.

As Ellie from Ditto explains:

"I think it's strictly the fact that we actually can make decisions with data now. I think startups naturally might be okay with making some gut-driven decisions for the first chunk of time, but eventually, once you start growing up, you can't be making gut decisions anymore. It's really helped build out the roadmap for the rest of the year of what our priorities are because now I know where our biggest gaps are instead of just guessing."

For Plain, improved data capabilities helped position them for fundraising, providing clear visibility into key metrics such as ARR, account activation, and user engagement rates. The additional data expertise also enabled them to expand their product offerings, including launching customer-facing analytics - one of their most requested features.

3. Team Development

Fractional teams can help upskill your existing team while preparing for future hires. At Lovespace, this meant not just implementing new infrastructure, but actively coaching their junior analyst in modern data practices. Their CEO, Will Edwards, explains:

"Outlier transformed our data capabilities, implementing a modern stack that will improve our analytics speed and reliability. They also took the time to coach our analyst team, helping them to level up their skills and putting us on a sustainable path."

Similarly at Plain, the fractional team provided ongoing support and mentorship, helping them build toward self-sufficiency through regular office hours and collaborative problem-solving sessions.

Evolution Over Time

Perhaps the most interesting aspect of fractional data teams is how the relationship evolves. As Tiney's CTO notes:

"The plan at the moment is to continue like this for a while. We're getting such good output from it. Hiring somebody would be more effort, more risk, and potentially not as productive in the short term."

However, the goal isn't perpetual dependency. A good fractional team should help you build towards self-sufficiency, whether that means eventually hiring in-house or maintaining a hybrid model.

As Matt from Plain explains the value proposition:

"I think the value of a fractional team specifically is that you can kind of shortcut a lot of the normal things you'd have to get right with hiring a full-time person and building a new team. From the get-go have a warm team of people who know what they're doing, who know how to work with each other and you can just get cracking. Importantly, however, it also doesn't have to be immediately full-time, and you can kind of scale the time commitment you need based on your own requirements."

Ready to Learn More?

If you're interested in understanding how a fractional data team could work for your company, we'd love to chat.