The traditional, centralized model of enterprise architecture, where a single team in the corporate headquarters dictates the technology standards for the entire organization, is a major bottleneck to regional innovation. A regional team in the MENA region that needs to deploy a new Arabic AI service might have to wait months for the central EA team to approve the new technology. This is no longer tenable in a fast-moving digital world. The solution is to move to a federated enterprise architecture.

• The Central EA Team: The Guardians of the Core: In a federated model, the central EA team does not try to control everything. Instead, it focuses on defining and governing the “core” of the enterprise architecture. This includes:
  
  • Global Standards: Defining a set of high-level, non-negotiable standards for security, data privacy, and interoperability.
  • The Common Platform: Managing the common, global platforms that are used by all regions, such as the core ERP system, the global network, and the identity and access management system.
  • The Reference Architecture: Providing a set of pre-approved reference architectures and patterns that regional teams can use to build their solutions.
• The Regional EA Teams: The Engines of Innovation: The regional EA teams have the autonomy to extend and adapt the enterprise architecture to meet the specific needs of their local markets. They can choose the best technologies for their regional AI models, as long as they adhere to the global standards set by the central EA team. This model, as described by frameworks like The Open Group Architecture Framework (TOGAF), allows for a balance between global consistency and local agility.

Data is the lifeblood of AI, and in a multi-region environment, the data architecture is the most critical and complex part of the enterprise architecture.

1. Federated Data Governance

Just as the overall enterprise architecture is federated, so too must be the data governance model.

• The Central Data Governance Council: This council, composed of senior leaders from across the organization, is responsible for setting the high-level data policies, such as the data classification standard and the data privacy policy.
• Regional Data Stewards: In each region, there are data stewards who are responsible for the day-to-day implementation of the data governance policies. They understand the specific legal and business context of their region and are empowered to make decisions about how data is managed in their local systems.

2. The Enterprise Data Catalog

In a distributed, multi-region environment, it can be difficult to even know what data exists and where it is located. An enterprise data catalog is an essential tool for creating a single, searchable inventory of all of the organization’s data assets. A good data catalog will provide:

• Automated Data Discovery: The ability to automatically scan databases, data lakes, and other data sources and to ingest their metadata into the catalog.
• Rich Metadata: For each data asset, the catalog should store rich metadata, including its business definition, its owner, its data classification, and its lineage (where it came from and how it has been transformed).
• A Collaborative Hub: The catalog should be a collaborative platform where data analysts, data scientists, and other data consumers can search for data, understand its meaning and quality, and share their knowledge.

3. The Data Mesh: A Decentralized Approach to Data Ownership

A data mesh is a modern, decentralized approach to data architecture that is a perfect fit for a federated enterprise. Instead of a single, centralized data lake or data warehouse, a data mesh is composed of a distributed network of domain-oriented, self-service data products.

• Domain Ownership: Each “data product” (e.g., a set of curated data about customers in the MENA region) is owned and managed by the team that knows the data best (e.g., the MENA marketing team). They are responsible for the quality, the security, and the reliability of their data product.
• Data as a Product: Each data product is treated like a real product. It has a clear owner, a well-defined API for access, and a service-level agreement (SLA) for quality and availability.
• Self-Service Infrastructure: A central data platform team provides a common, self-service infrastructure that makes it easy for the domain teams to build, deploy, and manage their data products.