There is likely some data organization in place today. During the initial assessment, we discover how well it meets the business's current needs. That's a strong dose of introspection, but just like building out the data and AI strategy, nothing can move forward without knowing the current state.
This process can be difficult because resources are scattered across the business. They will likely need to be centralized to succeed. The data team may poach resources from other groups and organizations. They will need ownership over infrastructure and their budget, which other leaders could own.
Building the organization won't and shouldn't happen overnight. The pace of change must match the business's timelines. In most cases, more has already been built than the company can currently leverage. Data scientists were hired before the data and infrastructure were in place for them to do their work.
These early phases involve not only growing pains but also transformation challenges. In this chapter, I explain how a data and AI Center of Excellence (CoE) is built to minimize costs while moving maturity forward.
The Need for an Executive or C-level Data Leader
A C-level data leader should be brought in to help build the data and analytics organization. That's rarely done, so what we inherit has been built in several competing directions. The data team is often distributed across multiple organizations at early maturity businesses. Each team ...