Data Team Architecture: Centralized vs Hub and Spoke
Data teams are the backbone of any data driven organization, providing essential insights and analysis that inform decision making from the top down. As data teams grow to meet the needs of an organization, it’s important to consider how best to organize them for maximum efficiency and collaboration. Two basic designs for data team architecture are a centralized model and a hub-and-spoke model. Both of these two models for structuring a data team have distinct strengths and weaknesses that should be carefully examined while considering your use-case. In this article we will discuss the differences between these two approaches and provide guidance on which one might be right for your organization. We will also discuss some of the advantages and disadvantages associated with each approach so you can make an informed decision about how to structure your team.
Centralized
A centralized data team is one that is organized around a single, unified structure and workflow. This type of architecture typically involves a single, central team that serves as the primary source for all stakeholders and data needs. The centralized team will include many team members that all work together towards once common goal. A centralized team may include a manager, data engineer, analytics engineer, data analyst, etc. all working to ensure that data is collected, stored and processed in a consistent manner for the entire organization. Some advantages of this approach include improved efficiency due to streamlined processes and better collaboration by having everyone on the same team. There is less time spent communicating between teams and dealing with the company bureaucracy that comes with it. It also allows for closer management of the team and projects since the entirety of the team members are working under the same management.
However, there are also some drawbacks to consider as well. For example, if the central hub fails or experiences downtime, it could cause a major disruption to the entire company as opposed to just one single company domain. This can also be very problematic if each role is single threaded and the tasks do not overlap. If someone is on PTO or out sick and there is an outage, it can be much more difficult to get fixed. Also, because of its centralized nature with clearly defined roles it can be difficult to accommodate different skill sets besides the basic tasks that team members perform. Beyond these downsides, another issue is that centralized data teams are harder to scale as one team inevitably becomes un-manageable.
Hub and Spoke
A hub and spoke data team works by having a central “hub” system that is responsible for managing shared data assets and operations. Using the data from the hub, the specific modeling and analysis can be routed to one or more “spoke” systems. These are distributed members of that data team that work within a specifici company domain. Each “spoke” team specialize in a specific domains of the company and works with a specific group of stakeholders. This provides the “spoke” teams with a high degree of flexibility, allowing them to focus on specific stakeholder asks without having to worry about the health of the entire system. While the “hub” team focuses on the shared data assets and systems that serve the entire company, the “spoke” teams work with specific groups in the company for modeling and analysis.
Another main advantage of using a hub and spoke architecture for data teams is that it allows for scalability and flexibility. Teams can quickly add new functions or change existing ones without impacting other parts of the business. Spoke teams have the flexibility to build their own modeling and analysis that they can own, as opposed to trying to fit everything into one unified system. This makes it easier to respond to changing business requirements, as well as integrate new technologies into the environment. Additionally, the company can easily add on more “spoke” teams as the business grows without necessarily needing to scale up their ‘hub’ team every time.
However, there are also some drawbacks associated with this architecture. It requires careful planning when it comes to security and access control since data must be routed through the central hub before it can reach it’s destination. Additionally, having too many spoke teams may result in increased complexity and a disparate and confusing code base if not managed properly. Finally, if any changes are made to the hub then these changes need to be replicated across all of the connected systems in order to maintain consistency. For this reason, it can sometimes be difficult to maintain consistency and data integrity when the team is distributed across the organization and many times working under different management.
Which should you choose?
Ultimately, making the choice between a centralized or hub and spoke data team comes down to one thing; what best fits the organization’s needs. Companies must consider many things: how much control they want over access and security, how quickly changes need to be implemented across systems and how mission critical fixing broken content is. A hub and spoke system may provide more flexibility in terms of scalability but also requires careful management due its complexity. On the other hand, a centralized approach makes it easier for teams and data systems to stay consistent throughout all projects and changes, but also can be challenging when tasks are not shared and employees are un-availabile. It’s important for companies to evaluate their own unique situation and use case before deciding which option is right for them.
