The Mini-Data Organization
How to scale data capabilities in the age of Generative AI without fully decentralizing your data team
When your marketing team needs a quick analysis of a campaign performance, how long does it take to get answers? A day? A week? A month? If you’re working with a centralized data team, the answer is probably: too long. Yet, this simple example illustrates one of the most common scaling challenges in growing organizations.
As the demand for data across the business increases, a single centralized team simply cannot keep pace. Analysts become bottlenecks, priorities conflict, and leaders lose confidence in the data function’s ability to deliver.
That’s why I believe we need a sustainable way to scale data organizations that are able to deliver at speed and with the right business context. Also, in the era of Generative AI (GenAI), I think it should be done without placing a dedicated data analyst, scientist and engineer in every single team.
Let’s explore a better way in this article.
Why Central Data Teams Struggle to Scale
Most companies start data organizations with the classic centralized structure, where all data professionals report into one team. Business units like Marketing or Product, channel their different needs into one shared queue for everything from routine reporting to advanced modeling.
While this structure works well for small organizations, it quickly breaks down under the pressure of growth. The central team becomes a significant bottleneck. Analysts are forced to constantly switch domains and start specializing in a specific area, at the cost of having to hire one analyst for each specific domain.
Why Full Decentralization Doesn’t Always Work Either
Fully embedding a dedicated data professional directly inside each business unit ensures deep integration and business context, but it introduces new risks that often outweigh the benefits.
On one hand, individual experts become isolated and mentorship and career growth becomes more difficult to manage. On the other, without central coordination, individuals begin to define things differently, leading to inconsistencies and confusion.
Finally, organizations run into an inherent capacity problem. It becomes almost impossible to align resources precisely with demand; some experts are over-stretched while others are underutilized.
A More Flexible Alternative: The Mini-Data Organization
So my proposal lies not at the extremes, but in a functional middle ground.
Instead of embedding a single person everywhere, you create small, integrated data teams that support clusters of teams, for example, a department (3-4 teams) dedicated to ‘Customer Growth’ or ‘Core Experience’ that can also leverage a GenAI skillset. These mini-data organizations, typically 3–5 people, function as a cohesive unit and include:
One or two domain-focused analysts
A specialist focused on data engineering support
One or two optional data scientists if the domain requires it
These teams support the department operationally but they functionally report to the same person, a manager within the mini-data team. This manager then reports to the central data organization. This ensures every individual has a clear career path and peer support. They operate using shared tools and definitions, coordinating with a central platform team that guarantees standardization across the enterprise.
Why This Model Works
Ultimately, this model succeeds because it addresses the core, competing demands of the other structures. It provides the focused business context needed for high-impact results while offering the professional framework essential for talent retention and growth.
The structure is inherently flexible, which is a key benefit. By serving several business units, the mini-teams can adaptively shift focus to the most important priorities, effectively addressing resource under- or over-utilization. Additionally, functioning as a true team ensures consistent execution and knowledge sharing.
On top of that, a critical emerging factor that makes the mini-data organization model even stronger is the rise of GenAI.
GenAI tools can significantly automate many basic data tasks, such as generating routine reports, assisting with initial data checks, and converting simple requests into data queries. This allows the dedicated people in the mini-teams to no longer spend time on these low-level activities. Instead, they can focus on more complex and critical business problems that require their deep expertise. This synergy enables smaller data teams to accomplish the work of much larger teams.
What It Takes to Get Started
If this strategic alignment resonates, how do you begin to implement it?
You don’t need to reorganize your entire data team overnight. Start by picking one department that’s struggling or constantly competing for attention from your central team. Form a small squad of 2-3 data people to support just that area for a few months. See what works, what doesn’t, and adjust before expanding the model elsewhere.
On the GenAI side, start with the tasks your analysts and Product Managers complain about the most: usually the weekly repetitive work or simple data extractions. Choose one or two AI tools or features (from tools you already use) that can handle these tasks and let your team experiment with them on real projects.
When PMs understand what GenAI can handle quickly, they’ll stop routing every small question through the data team and save analyst time for questions that genuinely need deeper thinking.
When adopting this model, your core data governance and definitions must remain very stable; otherwise, this semi-decentralization will only amplify existing issues (though not as severely as with full decentralization). Without strong shared standards, you risk inadvertently recreating the same inconsistencies and data silos you were trying to eliminate.
Final Thoughts
The mini-data organization model is the result of data organizations having to adapt to the evolution of businesses and the rise of GenAI.
As businesses evolve, the demands on your data function will fluctuate, and this hybrid approach gives you the flexibility to respond without constant reorganization. Building effective mini-data organizations require investment in the shared platforms, the governance frameworks, the communication channels, and the cultural norms that keep these teams aligned while allowing them to operate with autonomy.
When done well, this model positions your data organization to evolve alongside your business.
Enjoyed this post? You might like my book, Data as a Product Driver 🚚.