Do you hire a bunch of data scientists and lock them in a room full of supercomputers? Or do you sprinkle data analysts throughout your organization like fairy dust? There’s no one-size-fits-all answer.
It’s more like a choose-your-own-adventure book, but with spreadsheets instead of dragons.
The right structure for your data and insights team depends on your business goals, culture, and level of data maturity. Consider this your “Let’s Go” guide to the land of data teams. We’ll explore the main approaches to structuring your data and insights team: centralized, decentralized, hub-and-spoke, hybrid, and federated. Each has its own strengths and weaknesses.
Centralized Teams: A Gourmet Restaurant
Picture your data team as a five-star restaurant. All your chefs (data scientists) and staff (analysts) work in one place, and your customers (other departments) come to you. You concentrate your expertise and ensure you have the best tools and ingredients. You can serve up unbiased reports and analysis because the staff is insulated from the politics in the various divisions of the business. It’s like a data oasis in the desert of corporate chaos.
Netflix’s CTO, Elizabeth Stone, explained their approach in a 2024 interview: “At the scale of company that Netflix now is, very often data-oriented teams are embedded in other parts of the business. So it could either be embedded in a business line like ads or games, or they are organized more functionally separating data engineers from data scientists from analytics engineers from consumer researchers. We’ve resisted that and kept a centralized team that is both functionally diverse and works on nearly every area of the business from within the team.”
But centralization is not without challenges. The team must work hard to maintain strong partnerships with other departments and also balance centralized governance with agility.
While decision-making may be slower due to centralized control, decisions typically align well with overall organizational strategy. Cross-functional collaboration is facilitated, but the team may struggle to deeply understand specific business unit needs. It’s a powerful model, but one that requires careful management to reap its full benefits. Think of it as herding cats, if the cats were all geniuses with PhDs in statistics.
Decentralized Teams: Food Trucks
Picture data analysts embedded in every department, speaking the local lingo and solving problems on the ground. That’s the decentralized approach. It’s like having a fleet of food trucks instead of one big restaurant.
Your team spreads out across town to meet the customers where they are, and they have the ability to customize their recipes to local preferences. They can move with their customers, adapting to changing needs faster than you can say “pivot to video.”
But beware – this approach can lead to inconsistent methods, duplication of effort, and missed opportunities for cross-functional insights. It’s like having a bunch of chefs all inventing their own version of tomato soup – you might end up with some creative flavors, but good luck getting them to agree on a menu.
Hub-and-Spoke Model: Restaurant Franchises
Imagine a wheel with a strong center and sturdy spokes radiating outward. That’s the hub-and-spoke model in a nutshell. It combines elements of both centralized and decentralized approaches, aiming to balance consistency with agility. It’s like having a chain of restaurants with a central kitchen and local franchises.
In this model, a central “hub” team oversees overall strategy, standards, and governance, while “spoke” teams embedded in different business units handle day-to-day analytics and insights. It’s like having centralized sourcing for ingredients and equipment, as well as expert guidance on setting up restaurants, but the local franchise owner still has a lot of control and responsibility.
This approach maintains centralized standards while allowing for quick, localized decision-making. Data professionals have opportunities to specialize in business domains while staying connected to a broader data community, fostering skill development. This model effectively addresses both company-wide and department-specific data needs, balancing alignment across the organization.
However, it does come with challenges. Coordination between hub and spoke teams can be complex, requiring strong communication. Resource allocation can be tricky, as decisions must be made on how to distribute talent and resources between the hub and spokes. And of course, only companies with a sizable data team can have both centralized and decentralized staff.
Other Data Team Structure Options
Federated models are hub-and-spoke models in Bizarro world. The data teams in the spokes are the drivers of the business, but there is a centralized team that exists to provide services (such as databases and other tools) across the decentralized teams in order to benefit from economies of scale. It’s like if the local franchises ran the show, but there was a central office that just handled the boring stuff like napkin ordering and payroll.
Hybrid structures are just a DIY of whatever seems to get you through the day. For example, part of the company may rely on hub-and-spoke, but the largest business units may have fully decentralized teams embedded, while the smallest business units rely entirely on centralized support.
“Data Mesh” is the latest trend, or fad if you prefer, that holds a core philosophy of treating data as a “product.” Not only is each decentralized team responsible for their data, but they approach the data and its use as product managers would approach any other product. But data governance, such as quality and security, still operates more like a federated model. It’s like if every restaurant in town had to follow the same health code, but could serve whatever cuisine they wanted.
Of course, the best data team structure is the one that works for your organization’s unique needs and capabilities. Don’t get caught up in the hype – focus on what works for you! It’s like choosing between a fork, a spoon, or chopsticks – the best utensil is the one that gets the food into your mouth without making a mess.
The Evolution of Data Team Structures
As businesses grow and scale, their data team structures must evolve to meet changing needs. It’s like watching a caterpillar turn into a butterfly, if the caterpillar was really into spreadsheets.
The journey begins with the lone wolf phase in early-stage startups, where a single data analyst handles all tasks. This lean and agile approach works well for small companies with limited data needs but becomes unsustainable as data volumes and complexity increase. It’s like trying to bail out the Titanic with a teacup – heroic, but ultimately futile.
In the early growth phase, growing startups and SMBs form a centralized data team to serve the entire organization. This allows for consistent practices and efficient resource allocation. However, as the demand for insights grows, the centralized team can become a bottleneck. It’s like having one barista trying to serve the entire morning rush at Starbucks – someone’s going to end up with decaf when they ordered espresso.
During a rapid growth phase, fast-growing SMBs and mid-market companies often adopt a hub-and-spoke or hybrid model. A central data team (hub) works alongside embedded analysts in key business units (spokes), balancing centralized governance with localized agility. As organizational complexity increases, even greater specialization and autonomy may be needed. It’s like a game of data Tetris – you’re constantly rearranging pieces to fit the changing shape of your business.
At the enterprise scale, large companies often implement decentralized or federated models, giving individual business units their own data teams while maintaining some central coordination. This approach allows for deep domain expertise and tailored solutions but may call for some recentralization to maintain consistency and leverage economies of scale.
Factors driving these structural changes include data volume and complexity, business growth and diversification, regulatory requirements, competitive pressures, technological advancements, and shifts in business strategy.
Regularly Reviewing and Adjusting Team Structures
Be prepared to adapt your data team structure as needed. Set regular check-ins to assess team performance, be open to feedback, and don’t be afraid to shake things up if something isn’t working. Rearranging the deck chairs on the Titanic wouldn’t have helped, but rearranging the icebergs would have a been cool idea.
Your data team structure should be a reflection of your overall organizational goals and strategy. A centralized structure might align well with a company focused on standardization and efficiency, while a decentralized approach could better serve an organization prioritizing innovation and agility in individual business units. Always ask yourself: How does this structure support our broader business objectives?
Factors to Consider When Choosing Your Data Team Structure
As you navigate the journey of building and evolving your data team, several key factors will influence your decisions. Consider these elements carefully to ensure your chosen structure aligns with your organization’s needs and capabilities:
Organizational maturity • Core business model • Competitive landscape • Anticipated business growth • Short-term vs. long-term goals • Executive buy-in • Data literacy • Cultural readiness • Centralized vs. decentralized decision-making • Collaboration across departments • Appetite for innovation and risk • Existing data infrastructure and processes • Current tech stack • Volume and variety of data sources • Data quality and governance • Data security and privacy requirements • Regulatory requirements and compliance needs • Resources and budget • Existing skill sets • Career growth opportunities • Ability to attract and retain talent • External partnerships or vendor relationships • Balance between agility and consistency • Need for quick, localized insights vs. comprehensive, organization-wide analysis
That’s a lot, I know. By considering these factors, you’ll be better equipped to choose a data team structure that not only fits your current situation but also positions you for future success. After all, the goal isn’t just to build a data team – it’s to create a data-driven organization that can thrive in an increasingly complex business landscape.
Staffing Implications of Data Team Structures
The structure of your data team doesn’t just affect technology and processes—it has profound implications for the people who make up your team. It’s like choosing between assembling a rock band or a symphony orchestra—both can make beautiful music, but they require very different skills and management styles.
Staff for the needs of the business. For centralized teams, roles tend to be more specialized. But data analysts on decentralized teams often wear multiple hats. And for combo approaches, there will be a mix of needs for specialists and generalists that will vary greatly depending on the details. It’s like trying to cast for a movie that’s part action thriller, part romantic comedy, and part documentary.
As you build these teams, career progression will be highly dependent on the choice of structure.
Centralized teams have clear career progression, but more of a silo-feel that the analyst is focused on a data career more than a career in your business’ particular industry. Decentralization offers a wider set of experiences and skill development, but can limit access to the wider data professional community. The combo structures can offer the opportunity to rotate across different areas, but some efficiencies would be lost as analysts frequently are having to adapt to new roles.
The most effective data teams are those where people feel valued, challenged, and see clear paths for growth. As you evolve your team structure, always consider the impact on your team members’ experiences and career aspirations. Despite our claim that data is the new oil, it is your people that are your most valuable asset!
Implementing the Right Structure for Your Organization
Whatever structure you choose, don’t be afraid to adapt. Even doing so frequently if the business needs it. Maybe you’re moving from a centralized model to a hub-and-spoke, or perhaps your federated team is consolidating. Whatever the shift, change is hard, but with the right approach, it doesn’t have to be chaos.
Follow a few simple rules:
- Communicate, communicate, communicate:Don’t spring the change on your team like a pop quiz. Share the vision, rationale, and expected benefits. Be transparent about challenges too.
- Phase it in:Rome wasn’t built in a day, and your new team structure won’t be either. Consider a gradual transition, perhaps starting with a pilot in one business unit. This allows you to iron out kinks before rolling out company-wide.
- Provide training and support:Your team might need new skills or tools to thrive in the new structure. Invest in training and be patient as people climb the learning curve.
- Expect and plan for resistance:Change can be scary. Some team members might be concerned about their roles or responsibilities. Address concerns head-on and involve the team in problem-solving.
- Reassess and adjust:Set checkpoints to evaluate how the new structure is working. Be prepared to make tweaks, or even bigger changes if needed.
- Maintain continuity of service:Ensure that ongoing projects and day-to-day operations don’t suffer during the transition. You’re renovating the house while still living in it!
- Update processes and documentation:Your new structure will likely require updates to workflows, responsibilities, and governance policies. Don’t let this be an afterthought.
- Celebrate wins and learn from setbacks: Recognize early successes to build momentum. When things don’t go as planned, treat it as a learning opportunity.
The goal isn’t just to change your org chart – it’s to create a more effective, efficient, and adaptable data organization. Keep your eye on the prize, and don’t forget to enjoy the journey.
Your Data Journey Awaits
Building the right data and insights team is a journey, not a destination. Whether you choose a centralized powerhouse, a decentralized network, or a hybrid approach, the key is to align your team structure with your business goals and culture. There’s no “perfect” solution – only the one that works best for your organization.
Are you ready to embark on this adventure? Maybe one day, you’ll look back on this journey and realize that building your data team was the most fun you’ve ever had with spreadsheets.
For more columns from Michael Bagalman’s Data Science for Decision Makers series, click here.
