As more companies invest heavily in data as they grow, the question of how to structure the data and analytics function becomes increasingly important. It is not just about reporting lines or team charters. Structure influences how effectively data is used, how talent is retained, and how aligned the team is to business priorities.
In my experience, I have seen a range of models in action: centralized teams, embedded analysts, and hub-and-spoke hybrids. I have also observed different leadership setups, sometimes a single leader overseeing everything from infrastructure to insights, and other times, responsibilities split across specialized leads.
This post shares how I think about team structure, when different models tend to work best, and what factors to weigh when choosing between them.
Centralized, Embedded, or Hybrid: What Works When
There is no universal best practice. The right structure depends heavily on context, including company size, stage of growth, data maturity, and business model complexity.
Centralized
In a centralized model, all data roles, including engineering, analytics, and science, sit within a single function that serves the entire company. This structure works best for early-stage companies or organizations where data maturity is still developing. It allows for strong control over tooling, quality, and governance, and makes it easier to create a cohesive roadmap across the data stack.
The tradeoff is responsiveness. Centralized teams can feel distant from domain-specific problems. If not well integrated into business conversations, they risk becoming ticket takers rather than strategic partners.
Embedded
In embedded models, analysts or data partners sit directly within business functions like product, marketing, or operations. This structure excels in fast-paced environments where proximity to the domain leads to faster decision-making and richer context. It can work well in mid-to-late-stage companies with relatively mature data cultures.
However, embedded teams often struggle with consistency. Tools, definitions, and workflows tend to fragment. Without strong governance or central support, knowledge sharing erodes, and duplication becomes common. Career paths can also become unclear, especially for more technical roles.
Hybrid (Hub-and-Spoke)
The hybrid model combines elements of both. A central team owns infrastructure, governance, and shared frameworks. Meanwhile, embedded analysts or data leads work closely with business teams, often with dual reporting lines or dotted-line alignment.
This model is often the most scalable and sustainable as a company grows. It allows for consistent platform and data standards while maintaining domain intimacy. But it requires more deliberate coordination. Dual reporting needs to be clear. Shared planning cycles, documentation practices, and communication rituals become essential.
Other Key Factors to Consider
Beyond size and maturity, there are a few additional factors that often shape what structure makes the most sense: the skillset of the internal team, the geographical distribution of people and stakeholders, and the company’s ownership model.
Skillset of the Internal Team
The structure should reflect the capabilities of the people in place, not just the ideal state.
If you have a team of strong generalists, a centralized or embedded setup can work well. Generalists are adaptable and can span a range of business needs. If the team is more specialized, for example with distinct data engineers, analytics engineers, and domain analysts, a hybrid model tends to offer more leverage. It allows for strategic deployment while still aligning functional peers.
If the team is early in its development or relatively junior, centralization can help with mentorship, standardization, and quality control.
Geographical Spread
If your company operates across multiple time zones or regions, structure needs to account for distance. A centralized team located in a single region can work for small companies or where async collaboration is well established. But as teams and stakeholders spread out, it often creates friction.
Embedding analysts regionally or structuring a hybrid model with coverage in multiple locations helps build trust and improve speed. It also enables continuity if supported by strong documentation, knowledge sharing, and handoff processes.
Ownership Model and Funding Pressures
The structure should also support the environment your team operates in. Different funding and ownership models bring different expectations.
Public companies often prioritize compliance, consistency, and auditability. In these environments, governance matters more and centralized or hybrid structures help ensure that financial metrics and reporting are tightly controlled.
Private equity-backed companies usually focus on efficiency and operational improvements. These teams are expected to move fast, surface clear ROI, and support performance visibility across the business. A hybrid model often works well here, centralizing infrastructure while embedding analytics close to decision-makers.
VC-funded startups tend to operate with lean teams, often staffed by generalists who support experimentation and growth. A centralized setup is typically the starting point, evolving over time as needs become more complex and the team scales.
Privately owned or founder-led businesses can vary widely. Sometimes they are highly sophisticated without formal data orgs, other times data maturity is still low. In these cases, relationship-building and business alignment matter just as much as structure. A hybrid or centralized model with strong communication and flexible resourcing often fits best.
One Leader or Multiple Leaders
A key design choice is whether to structure data and analytics under a single leader or distribute ownership across multiple functional leaders. I have seen both models work well, but they solve for different things.
A single leader model can create strong alignment across the entire data lifecycle, from infrastructure to insights. It is especially effective in earlier stages, where cohesion, simplicity, and unified prioritization are more important than deep specialization. One person can connect the dots between platform, analytics, governance, and enablement, which helps avoid silos and internal misalignment.
As teams grow, it becomes harder for one person to provide strong leadership across multiple disciplines. That is where a multi-leader model often becomes more effective. For example, a Head of Data Engineering may focus on platform, scalability, and architecture, while a Head of Analytics or Data Science leads insight generation, experimentation, and stakeholder engagement. Some organizations add additional leadership roles focused on data governance, machine learning, or data product management.
This setup brings depth, but also complexity. It only works if there is strong coordination across functions. Shared OKRs, regular planning cycles, and aligned prioritization are essential. Without that, it is easy for teams to drift apart, build redundant systems, or end up blocked by unclear ownership boundaries.
Ultimately, the number of leaders is less important than the clarity of their charters and the health of their collaboration. Whether centralized or distributed, what matters is that the team acts as one, supporting the business with consistent data, reliable systems, and useful insights.
Don’t Ignore the Politics
Even the best-designed structures can be undone by organizational politics. Competing priorities, power centers, or unclear ownership boundaries can lead to tension or misalignment. This is especially common when data teams serve multiple stakeholders across different functions or when they report into a neutral home like finance, product, or technology.
Politics show up in subtle ways, such as who controls headcount, whose roadmap takes precedence, or who owns the definition of a key metric. A structure that looks logical on paper might break down in practice if leaders are not aligned or if incentives are pulling teams in different directions.
The solution is not to avoid politics, but to navigate them. Data leaders need to be relationship builders, translators, and brokers of alignment. Structures that foster trust, transparency, and joint ownership tend to hold up better under pressure than those that assume everyone is already on the same page.
What Actually Matters
Structure is important, but it is not the goal. It is a means to enable business impact, trust in data, and team effectiveness.
What matters more than the org chart is whether:
- The team is helping the business make better decisions
- Stakeholders trust the data and the people behind it
- The data platform is reliable, secure, and scaling with demand
- People on the team are growing, challenged, and energized by their work
I have seen well-structured teams underperform because they lacked clarity, purpose, or cohesion. And I have seen messy org charts support high-impact teams because the people had strong relationships, clear priorities, and a shared sense of mission.
Closing Thoughts
Data and analytics teams are often asked to move fast, deliver insights, and build durable infrastructure, all at once. That is not easy. Structure can either help or get in the way.
The best structures grow with the company, evolve with the work, and reflect the strengths of the team. They create just enough alignment without stifling flexibility. And they support a culture where data is not just collected, but actually used to drive decisions.
No single structure is perfect. But the right one, for the right time, can be the difference between motion and momentum.
If you are a C-level leader, one of the most valuable things you can do is make structure intentional. Be clear about what the data function is responsible for, how it fits into the broader company strategy, and who is accountable for outcomes. Model alignment across executive teams, give data leaders a real seat at the table, and resist the urge to solve every org problem with a reorg. Structure should follow purpose, not politics.