3 Strategies to Balance Your Data and Analytics Team

July 30, 2018

Contributor: Sarah Hippold

Why data and analytics leaders should create organizational models that balance freedom and control across teams.

When a data and analytics (D&A) leader has to craft a new organizational model for centralized and decentralized analytics teams, the long-standing debate between control and freedom emerges. The centralization-versus-decentralization debate often splits organizations into two groups:

  • The "control" faction, which values consistency, consensus and shared best practices.
  • The "freedom" faction, which values autonomy, agility and innovation.

"Centralized teams usually do a good job when it comes to consistency, governance and shared best practices. However, they can't deliver the domain expertise and responsiveness that most organizations require," explains Kurt Schlegel, research vice president at Gartner. "Decentralized teams face the opposite problem. They have plenty of domain expertise and responsiveness, but lack consistency across their information sources and analytical models."

“ It's less about hierarchical control and more about networked cooperation”

Successful data and analytics leaders will create an organizational model that balances the strengths and weaknesses of the centralized and decentralized approaches. Gartner recommends a blend of three strategies such leaders can use to achieve that balance. These approaches are not mutually exclusive and have numerous permutations that could be tailored to fit the culture and skills of your company.


The most conventional approach. We have a centralized team that operates enterprisewide and acts as an umbrella organization for smaller, more-specialized D&A teams in business units or local markets. The central team provides a global process to integrate, report and analyze data, and the role of the specialized teams is to implement those global processes in their respective markets or business unit.

"A key advantage of this strategy is that departments can establish D&A teams much quicker, because the framework is already provided for. At the same time, the centralized team can ensure that consistent best practices are deployed across the organization," Schlegel adds.

Divide and conquer

In this approach, a centralized team is responsible for ensuring consistency throughout the organization. Oversight can include areas such as enterprise resource management (ERP), customer relationship management (CRM) or payroll systems. However, certain functions, such as HR and marketing, have the authority to manage the analytics applications in their respective areas. They decide which tools to purchase and how the solutions are architected within their domains, but must report to the global team in a consistent fashion.

"Communicating jurisdiction is paramount to the success of this approach. It's less about hierarchical control and more about networked cooperation," says Schlegel.


This approach is the opposite of the franchise strategy. Individual business lines and workgroups have the authority to buy whatever tools necessary to fit their specific needs and manage their own data modeling, reporting and analysis. "The role of the centralized team is to collaborate with the decentralized teams, identify valuable analytics content and promote it across the organization," Schlegel adds.

This approach empowers teams to be creative and innovative. The centralized team identifies the most successful work and provides a platform to share and promote the work across the enterprise.

"D&A leaders must aim for a balanced approach between these three strategies," Schlegel concludes. "They need to create a two-tiered model with a centralized team, which works collaboratively with a number of decentralized teams around the organization. Above all, they need to ensure that jurisdictions are clear and all teams act within their responsibilities, but make optimal use of their authority."

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