How should we evaluate and align our team structure and roles to support the evolution of data products for Al agents?

• Start with a centralized data team
• Gradually transition to a hub-and-spoke model
• Introduce specialised roles: Data Product Owners, ML Engineers, Site Reliability Engineers, Knowledge Engineers
• Establish formal AI governance boards
• Create cross-functional teams with clear ownership and accountability
• Develop training programs to up-skill existing team members

What are the key steps and considerations in developing a roadmap for our data product maturity journey?

Foundation Phase

• Understand AI agent business objectives
• Map current data landscape
• Identify initial use cases
• Define context requirements for AI agents

Standardisation Phase

• Create consistent data schemas
• Develop governance frameworks
• Establish data quality standards
• Implement metadata management protocols

Optimisation Phase

• Enable real-time data access
• Improve API performance
• Enhance agent integration capabilities
• Implement low-latency data retrieval

Innovation Phase

• Develop adaptive data experiences
• Create self-learning data products
• Implement advanced observability
• Enable autonomous data management

How can we assess the current maturity of our organisation's data products and identify gaps?

• Create a scoring rubric across five dimensions:
  
  • Data Access: Evaluate real-time vs. batch capabilities
  • Schema: Check strictness and consistency
  • Metadata: Assess contextual richness
  • Error Handling: Review structured error responses
  • Observability: Measure monitoring and feedback mechanisms

• Use a maturity matrix with levels
  
  • Not Ready
  • Basic Compliance
  • Intermediate
  • Advanced
  • Agent-Native

• Conduct cross-functional workshops
• Perform gap analysis
• Develop targeted improvement strategies
• Continuously reassess and iterate