What makes an effective data science team structure?
Data science team structures vary in strength, and their success depends on how roles and leadership align with business goals to meet analytics needs.
Organizations are forming data science teams to take advantage of growing data volumes and advances in AI and analytics. But they can only realize the data's value if the teams have the skills and experience to make sense of it.
To deliver meaningful results, data science teams need to integrate with broader business operations and respond to the organization's larger goals. Building that alignment starts with defining the team's structure, how roles are filled and what steps to take to build the best data science team.
Most organizations choose between one of three approaches: centralized, decentralized or a hybrid structure. Understanding these models helps leaders design teams that translate data science efforts into real business value.
Structuring a data science team
Before forming a data science team, senior leadership, in collaboration with the chief data officer, should define a structure that helps team members work efficiently and respond quickly to business needs:
A centralized model organizes all data science professionals into one team serving every department or a dedicated data science department with multiple teams. In both cases, the defining feature is a single structure dedicated to the organization's data science initiatives.
Centralized teams encourage collaboration, mentoring and skill development by grouping data science professionals. A single team can better monitor and manage overall resource and tooling use while reducing duplicate efforts and simplifying coordination efforts. This approach also makes it easier to standardize processes, implement best practices and enforce data governance.
Despite these advantages, the centralized model has challenges. For example, it can be difficult for a centralized team to gain the deeper domain knowledge necessary to understand how individual business units function. Although a centralized team collaborates among team members, they might struggle to communicate effectively with other departments. A centralized team can become overloaded with projects and be slow to respond to inquiries, while lacking the needed management insight to prioritize these projects.
The decentralized team model
In a decentralized model, data science professionals are assigned to individual business units instead of being grouped into one team. They report to that unit's leadership, work closely with colleagues in that unit, learn the nuances of their business operations and focus exclusively on their projects. This setup gives the data teams more familiarity with the unit's operations so they can address the specific needs of that unit.
In some organizations, the decentralized model is organically established. Individual business units recognize the value of data science for their initiatives and hire data scientists to help carry them out.
One of the main advantages of the decentralized model is that the data science team can be more responsive to the needs of a specific department. Stakeholders do not encounter the same issues with a centralized model. The data science team prioritizes its efforts based on the department's goals and objectives, adjusting those priorities as requirements change. The result is greater flexibility, quicker turnaround times and more targeted solutions.
However, spreading teams across units can create silos that complicate collaboration, knowledge-sharing and mentoring. It also becomes more difficult to standardize processes, enforce organization-level data governance and control resource and tooling utilization. Decentralization can lead to duplicate efforts as different teams try to solve the same problems.
The hybrid team model
The hybrid model balances the centralized and decentralized models and builds on their strengths. In the hybrid model, data science professionals remain part of a single organizational structure, but some or all are embedded directly within individual business units. This arrangement gives them direct domain knowledge of these units and keeps them focused on their projects while allowing them to share resources and information with data science professionals across the organization.
Organizations implement the hybrid model in different ways depending on priorities and scale. One option is maintaining a centralized core team that sets standards, manages governance and handles cross-functional projects. On the other hand, an organization might forego the central team and instead embed all data science professionals, while maintaining centralized management and governance.
The hybrid model enables data science professionals to gain the domain knowledge they need to deliver solutions quickly while facilitating collaboration and knowledge-sharing among those professionals. This approach also makes standardization and governance easier to enforce than fully decentralized models, offering more flexibility and scalability than centralized ones.
The main challenge in a hybrid model is coordination. Balancing the priorities of the individual business units against organization-wide goals adds to management complexity, and success depends on communication between embedded teams and centralized leadership.
Choosing the right team model
Choosing the right team model depends on an organization's size, data maturity and strategic priorities. Leadership plays a role in this decision, weighing factors such as governance needs, speed of delivery and the level of domain expertise required.
Despite the availability of other models, the centralized, decentralized and hybrid models remain the most widely adopted because they directly address universal tensions in data work. Centralized structures often suit organizations looking for consistency, while decentralized teams fit those prioritizing flexibility. Sometimes, an organization has already adopted one of these approaches, so leadership should focus on whether it's worth switching to another team structure as their needs change.
Once an organization decides on a team structure, the next challenge is ensuring the structure is filled with the right expertise. Clear roles define how work is divided, how teams collaborate and how data initiatives support business priorities.
Roles in a data science team
As data teams evolve, specific roles emerge to effectively manage, process and analyze data. An effective data science team combines professionals with specialized skills and experience to carry out complex data science projects. Modern data science leadership must ensure team members are qualified to achieve long-term success.
An effective data science team combines professionals with specialized skills and experience.
Team composition varies depending on business requirements and goals. All team members should be highly skilled professionals with expertise specific to their assigned roles:
Data scientist. An individual with a wide range of skills, includingexploring and interpretingdata, preparing and cleansing data sets, building predictive models and deriving insights to support business decisions.
Data architect. An IT professional who designs the overarching architecture, defining models, policies and technologies for collecting, organizing and storing data.
Data engineer. An IT professional who builds and maintains data pipelines, integrates data from multiple sources and provides analytics teams with secure, reliable access.
Data analyst. An individual who collects, cleanses and analyzes data, applies business models and algorithms and communicates findings through reports and visualizations.
Machine learning (ML) engineer. A professional who develops and optimizes ML models and algorithms, manages large data sets and tests systems for accuracy and performance.
Data strategist/analytics translator. An individual who bridges technical teams and business stakeholders, aligning data projects with organizational objectives and maximizing value in analytics efforts.
Product owner. A professional representing stakeholders' interests, prioritizes features and manages the product backlog for data science initiatives.
Team manager. A lead who oversees team operations, including budgeting, scheduling, project assignment, resource management and coordinating efforts with external stakeholders, while facilitating collaboration and knowledge-sharing within the team.
Data governance lead. An individual who develops and enforces policies for data quality, security and compliance of an organization's data assets in line with its governance strategy.
These roles are not exhaustive but represent the more common positions on data science teams. Many teams also include specialists such as process experts, business analysts, data visualization engineers and DevOps engineers.
Data science roles are often described in black-and-white terms, but their definitions and responsibilities can vary by organization. A single professional might even cover multiple roles, such as acting as both data architect and data engineer.
This organizational chart shows a typical management structure for a data science team.
Data science leadership
Strong leadership is critical for data science teams to succeed. Leaders must foster communication, facilitate learning, encourage collaboration and understand the business objectives that drive the data science initiatives.
Modern leaders value soft skills alongside technical skills. Curiosity, data storytelling and clear communication help teams share insights and support ongoing learning. Leaders should also put efficient processes in place to support project development and make sure that everyone on the team understands why those processes are necessary, how they work and how they reinforce the team's objectives.
Stakeholder engagement is another core responsibility. Leadership should listen to stakeholders and keep them informed as the project progresses. They should also foster a data science culture that extends outside the team so other individuals in the organization can understand the value of the data and the goals of the data science team. Regular updates and clear documentation foster trust and keep expectations aligned.
Data science leaders must also provide team members with the tools to carry out their tasks efficiently and effectively. This can include access to appropriate programming, visualization and machine learning tools to support analytics work.
Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.
