Roles
Data & AI Organization & Maturity
A comprehensive framework for modern data roles and structural archetypes.
Organizational Distribution Map
Mapping centralization vs. specialized delivery across the stack.
Strategy & Governance Logic
Centralized Policy: Roles are strictly managed by a central office to ensure a unified enterprise vision and consistent rulebook.
Execution Logic
Centralized Tech, Decentralized Value: Infrastructure is managed centrally for "pipe" consistency, but roles directly enable the business delivery units on the right.
Applied AI Logic
Hybrid Intelligence: Platforms (MLOps) are shared centrally, while AI Science acts as a bridge to solve specific business problems locally.
Enablement Logic
Unified Literacy: Training and security are centralized to scale consistency, while domain stewards provide the local context to the business.
Strategic Leadership & Governance
Roles that define the strategic direction and establish foundational policies. Centralization ensures a unified enterprise strategy.
C-suite executive responsible for entire data strategy, from governance and quality to data monetization.
Oversees AI roadmap and integration. Ensures strategic and ethical oversight across applied AI functions.
Dedicated to enforcing policy frameworks and creating the consistent "rulebook" for enterprise data usage.
Designs the overarching ecosystem blueprint, ensuring scalability, security, and cross-system interoperability.
Data & Analytics Execution
Hands-on roles building and managing assets. A hybrid of centralized shared-platform teams and decentralized business-unit specialists.
Builds the pipelines that move/transform data. Centralized teams maintain core tooling and best practices.
Bridges engineering and analysis by building semantic layers and reusable models to prevent redundancy.
Embedded in business units to perform day-to-day analysis and dashboarding with deep domain context.
Builds visualizations for specific audiences, working closely with business stakeholders to drive action.
Monitors consistency and accuracy across the enterprise to ensure global data quality standards are met.
Embedded in marketing teams to handle funnel analysis and multi-touch attribution nuances.
Acts as the voice of the customer, prioritizing features for data products within specific business units.
AI & Applied Intelligence
Specialized roles managing model lifecycles. Centers of Excellence provide the platforms used by decentralized domain scientists.
Builds and deploys production-grade models, maintaining shared ML platforms for decentralized users.
Automates the entire ML lifecycle—from deployment to monitoring—via shared, specialized platforms.
Prototypes models and conducts statistical analysis embedded within business units to solve specific problems.
Applies advanced NLP or Computer Vision to real-world problems requiring deep domain expertise.
Crafts and optimizes LLM prompts, developing best practices and standards for enterprise-wide application.
Develops consistent ethical frameworks and risk management processes for all enterprise AI initiatives.
Translates business goals into AI capabilities, working closely with teams to manage AI-powered products.
Governance & Enablement
Support roles focused on security, literacy, and metadata management. Centralized to ensure enterprise compliance and consistency.
Responsible for governance within a specific domain, leveraging deep local knowledge of business data.
Manages the data catalog and tracks lineage, ensuring consistent discoverability across the organization.
Enforces uniform security protocols and policies across all systems to ensure regulatory compliance.
Scales data literacy by creating training programs and resources accessible to all employees.
Manages third-party sharing agreements in compliance with legal and procurement standards.