The Data & AI Organization & Maturity

Strategic Leadership & Governance

This category includes roles that define the strategic direction and establish the foundational policies and standards for an organization's data and analytics functions. These roles are typically centralized to ensure consistent governance and a unified strategy.

• Chief Data Officer (CDO): 📊 Centralized | Must-Have
  The CDO is a C-suite executive responsible for the entire data strategy, from governance and quality to data monetization. This role is inherently centralized, as its purpose is to ensure a cohesive, enterprise-wide approach to data.

• Head of AI / Chief AI Officer: 🤖 Centralized | Emerging
  This role oversees the AI roadmap and is responsible for integrating AI into the business. While AI may be applied in a decentralized way, the strategic and ethical oversight of this function requires a centralized leader to ensure consistency and manage risk.

• Director of Data Governance: 📜 Centralized | Must-Have
  This role is dedicated to establishing and enforcing data governance policies and frameworks. It is fundamentally a centralized function, as its goal is to create a consistent "rulebook" for data that applies to the entire organization.

• Enterprise Data Architect: 🏗️ Centralized | Must-Have
  The enterprise data architect designs the overarching data ecosystem, ensuring that different systems can talk to each other and that the architecture is scalable and secure. This is a centralized role because it involves designing the blueprint for the entire organization's data landscape.

Data & Analytics Execution

This category includes the hands-on roles that build, analyze, and manage data assets. These roles can be a mix of centralized teams that build shared platforms and decentralized teams that work directly with business units.

• Data Engineer: 🛠️ Centralized | Must-Have
  Data engineers design and build the pipelines that move and transform data. While they may work with decentralized teams, the core platform, tooling, and best practices they use are typically developed and maintained by a centralized team to ensure efficiency and consistency.

• Analytics Engineer: 📈 Centralized | Emerging
  This role bridges the gap between data engineers and data analysts, building semantic layers and reusable data models. A centralized analytics engineering team can ensure consistency and prevent redundant work across the organization.

• Data Analyst: 📊 Decentralized | Must-Have
  Data analysts perform day-to-day analysis and build dashboards to support specific business teams. This role is highly effective when decentralized, as it allows the analyst to be embedded within a business unit and understand their specific needs and context.

• Business Intelligence Developer: 🖥️ Decentralized | Must-Have
  Similar to a data analyst, a BI developer builds visualizations and reports for a specific audience. This role is best suited for a decentralized model, allowing the developer to work closely with the business to create highly relevant and actionable dashboards.

• Data Quality Analyst: 🔍 Centralized | Must-Have
  This role is responsible for monitoring and ensuring data quality across the organization. This is a centralized function because data quality standards and monitoring tools must be consistent across the enterprise to be effective.

• Marketing/Data Attribution Lead: 🎯 Decentralized | Emerging
  This role focuses on marketing-specific data analysis, such as funnel analysis and multi-touch attribution. It is best suited for a decentralized model, as this individual needs to be deeply embedded in the marketing team to understand the nuances of their campaigns.

• Product Owner: 🏥 Decentralized | Must-Have
  The Product Owner defines the vision and strategy for a data product, prioritizes the backlog of features, and acts as the voice of the customer to the development team. This role is best suited for a decentralized model, as they need to be embedded with a business unit to understand the specific needs and goals of the product.

AI & Applied Intelligence

This category covers the roles that build and manage AI and machine learning models. This is an area with a mix of centralization and decentralization, depending on the organizational maturity.

• Machine Learning Engineer: 🤖 Centralized | Must-Have
  ML engineers build and deploy production-grade models. A centralized team of ML engineers can build and maintain a shared ML platform and MLOps tools, which can then be used by decentralized data scientists.

• MLOps Engineer: ⚙️ Centralized | Emerging
  This role automates the entire machine learning lifecycle, from model deployment to monitoring. It is a highly specialized and centralized role, as a single team can provide a shared platform that all other teams can use to deploy their models.

• Data Scientist: 🔬 Decentralized | Must-Have
  Data scientists explore data, prototype models, and conduct statistical analysis. This role is often decentralized so that the data scientist can work directly with a business unit to identify and solve specific business problems.

• Applied AI Scientist: 🧠 Decentralized | Emerging
  This is a specialized data scientist who focuses on applying advanced techniques like NLP or computer vision to solve real-world problems. This role is best decentralized, as it requires deep domain knowledge of a specific business area.

• Prompt Engineer / LLM Tuner: ✍️ Centralized | Emerging
  This newer role crafts and optimizes prompts for large language models. While the application of this skill may be decentralized, the underlying expertise and best practices for prompt engineering are often developed by a centralized team.

• AI Ethics & Risk Lead: ⚖️ Centralized | Emerging
  This role is responsible for ensuring the ethical and responsible use of AI. It is a centralized function, as it must create a consistent ethical framework and risk management process that applies to all AI initiatives across the organization.

• AI Product Manager: 📈 Decentralized | Emerging
  The AI product manager translates business goals into AI capabilities. This role is typically decentralized to work closely with business teams, identifying opportunities for AI to add value and managing the development of AI-powered products.

Governance & Enablement

This category includes the roles that support and enable the data organization by managing metadata, ensuring security, and providing training. These roles are typically centralized to ensure consistency and compliance.

• Data Steward: 🛡️ Decentralized | Must-Have
  Data stewards are responsible for the quality, accuracy, and governance of a specific data domain. While they work closely with the data governance team, they are decentralized, as they are embedded within a business unit and have deep knowledge of their specific data.

• Metadata & Lineage Specialist: 🗺️ Centralized | Emerging
  This role is a technical expert who manages the data catalog and tracks data lineage. This is a centralized function, as a single, consistent catalog is essential for data discoverability and governance across the entire organization.

• Privacy & Security Analyst: 🔐 Centralized | Must-Have
  This role is responsible for data privacy and security. It is a highly centralized function, as security policies and protocols must be applied uniformly across all data systems to ensure compliance and prevent breaches.

• Training & Enablement Lead: 🎓 Centralized | Emerging
  This individual is responsible for scaling data literacy and adoption. This is a centralized role that creates training programs and resources that can be used by all employees across the organization.

• Data Contract Manager: 🤝 Centralized | Emerging
  This role is responsible for managing data-sharing agreements with third parties. It is a centralized function that works closely with legal and procurement to ensure all data contracts are compliant and secure.