What is a Fractional Data Team & When Does it Make Sense?

The Modern Data Journey

Most growing companies follow a familiar data path. It often starts with a technically-minded founder or engineer cobbling together some basic analytics. You set up tracking, build a few dashboards, maybe write some SQL queries. For a while, it works.

Then things start to change. Your team grows. More people need access to data. The requests pile up. What used to be a manageable side task becomes increasingly complex. As the CTO of Tiney, a 50-person startup, describes it:

"We reached a point where our setup had been growing and become unwieldy. The backlog of requests and volume from the team started to increase. Lots more people became interested in data, which is really good... but we needed a solution."

The Hiring Dilemma

At this stage, many companies find themselves asking: "Is it the right time to hire someone for data?"

It's a tricky question. Your team needs data capabilities, but a full-time hire feels like a big commitment. As Tiney's CTO explains:

"We had a plan to hire somebody to become our data person. But when you're at our stage, it's quite tricky to work out exactly what kind of role you need. We need senior expertise to set things up right, but we don't necessarily have enough complex data science work to keep a senior person challenged. At the same time, bringing in someone more junior without the right support structure could create more problems than it solves."

Matt from Plain echoes this sentiment:

"We really reached for a fractional data team because we had a lot of data needs that we didn't quite have the bandwidth or experience in the team for. Our options were either to hire someone full-time or to go for the fractional. We ultimately went for the fractional setup because we felt that we weren't quite knowledgeable enough yet about how much work there was to do or what the profile was or what kind of experience we wanted in a hire."

This timing challenge is compounded by several factors:

• Data needs that are significant but perhaps not yet full-time

• Uncertainty about the exact role requirements

• Risk of making an expensive hiring mistake

• Desire to build foundations the right way

Enter the Fractional Data Team…

This is where a different approach comes in: the fractional data team. Think of it as having senior data expertise on tap - typically 1-2 days per week. Unlike traditional consultancies that focus on one-off projects, fractional teams integrate with your existing team, providing ongoing strategic guidance while actually implementing solutions.

Key characteristics:

• Ongoing partnership vs one-off projects

• Senior expertise without full-time cost

• Integration with your existing team

• Flexibility to scale up or down

• Focus on knowledge transfer

For Matt at Plain, the quality of expertise was a deciding factor:

"When we met Dan initially, we were really drawn by the depth of his past experience. If you consider the flip side, we would have had to hire someone very, very senior in a team with virtually nothing in the way of a data setup or data team. That can sometimes be a bit challenging because a lot of the initial setup work in early stage companies can be a bit rote and perhaps not as interesting for someone as experienced."

When It Makes Sense & The Cost Equation

The fractional model works particularly well for companies that have built some basic data infrastructure but need expertise to scale it properly….

Let's break down the numbers:

Full-time senior data hire:

• Base salary: £80-120k

• Benefits & taxes: ~20-30% additional

• Recruitment costs: 15-25% of salary

• Management overhead

• Total: £120-200k annually

Fractional team example:

• Monthly cost: £5k-£7k

• Flexible scaling

• No recruitment or management overhead

• Access to broader expertise

• Total: £54k annually

"We're getting two days a week of highly efficient, experienced time. It's a lot less than we would pay all in for a senior data person."

Real Value Delivery

The value goes beyond cost savings. A fractional team brings:

Tool & Architecture Expertise 

As Tiney's CTO explains: "Having experience of the breadth of different tools and setups... when you're a founder or small team trying to make procurement decisions, it's really hard to know how suitable something will be until you're six months into it."

At Lovespace, this meant moving from expensive production database queries to a modern data stack with Snowflake and dbt. For Plain, it meant successfully transitioning from Mixpanel to a robust data warehouse that could support both internal analytics and customer-facing reporting.

Operational Efficiency 

At Founders Forum, this meant transforming their weekly reporting process from a task that consumed multiple team members' entire Monday to an automated system. At Ditto, we helped implement proper activation tracking across their user journey, consolidating data from multiple tools (Mixpanel, Intercom, Stripe) into unified dashboards that drive product development decisions.

Knowledge Transfer 

At Lovespace, we're actively mentoring a junior analyst in modern data practices. For Plain, knowledge transfer happens through weekly office hours and a shared Slack channel, where the team gets help with everything from debugging customer questions to financial modeling.

Strategic Growth Support 

For companies like Plain and Ditto, having fractional data expertise has been crucial for fundraising preparation. Having robust data hygiene and reporting capabilities became increasingly important for investor conversations.

When It Might Not Be Right

That said, fractional teams aren't right for every situation. If you need constant, daily hands-on support, already have strong internal data leadership, or have highly specialised domain requirements, a full-time hire might make more sense. 

As Matt from Plain emphasises, the fractional approach works best when you're uncertain about your exact data needs or don't yet have the volume to justify a full-time hire: "Going for a fractional team would allow us to quickly make progress on all of our data needs without really having to answer those questions."

Ellie, Product Marketing Lead at Ditto, shares a similar perspective: "I think we're small enough now that honestly, the fractional model is working really well. It has opened all of our eyes to how important a data person will be in Ditto's future, but the fractional team has folded into the team nicely - they're in our Slack, we meet with them twice a week, and it's really just been easy to get them integrated into how we work."

Taking The Next Step

Understanding when and how to scale your data capabilities is a crucial decision for any growing company. If you're wrestling with this challenge, we'd love to help you think it through. Book a 30-minute consultation with us to review your current setup, discuss whether a fractional team could be right for you, and learn how other companies have navigated this transition.