One of the more common reasons why large-scale analytics programs fail to deliver significant and sustainable business value is: deficiencies in the ways analytics is organized to serve demand-side constituents and information consumers. Centralized models provide control, but usually become bottlenecks, as demand for data and analytics increases with the organization’s analytics maturity. Distributed models, which move some data and most analytics activities into various parts of the business, produce embedded data and analytics teams that can produce “at the speed of the business,” but often create second order problems with technology costs and complexity, and data trustworthiness.
By employing carefully designed federated analytics operating models, featuring the proper distribution of roles and responsibilities between a central D&A team and distributed or embedded D&A teams, D&A leaders can harness the beneficial aspects of both the centralized and the distributed analytics operating model template, while avoiding the pitfalls associated with both.
In this session, IIA Expert Marc Demarest explores:
- Why centralized and distributed analytics operating models often fail to scale
- The core design principles behind a federated analytics operating model
- The starting blueprint for transitioning to—or resetting—a federated analytics operating model
- Common pitfalls and key considerations for analytics operating model elements like data products and resource allocation