Create a Centralized and Decentralized Organizational Model for Analytics

The optimal organizational model requires a centralized team working in collaboration with a finite number of decentralized teams. It's a simple enough idea, but there are numerous permutations that data and analytics leaders must choose from to find the ideal balance.


Key  Findings
  • Overly centralized teams can't deliver the domain expertise and responsiveness that most organizations require. Although the centralized team typically does a good job of creating consistency, governance and shared best practices, it often creates a bottleneck with most users, who are waiting too long to get their requirements met.
  • Overly decentralized teams have the opposite problem. This model delivers plenty of domain expertise, agility and responsiveness, but struggles to deliver consistency across its information sources and analytic models. In addition, this approach struggles to share best practices.
  • Whether centralized or decentralized, most companies do not have enough analytic skills to execute on current demands. Competitive pressure makes it difficult for organizations to retain their skilled resources.
Creating the right organizational model is the key to successful analytics. Gartner recommends that data and analytics leaders:
  • Create a two-tiered organizational model with a centralized team working collaboratively with a collection of decentralized teams distributed throughout the enterprise.
  • Empower each local department with a cross-functional team that blends data engineering, data science and domain expertise.
  • Communicate jurisdiction by clarifying when decentralized teams are able to create analytic prototypes, pilots or full-production solutions.

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