Data Foundations of AI Briefing

1 hour
A comprehensive data strategy requires marrying data, technology and business objectives to achieve desired outcomes. This briefing from WWT explores the importance and foundational components of a sound data strategy, with a special focus on the considerations for Data Mesh versus Data Fabric approaches.


This briefing explores the critical importance of developing a robust data strategy. We'll detail the essential components of a sound data strategy, its integral role in modern business landscapes and AI readiness, and the other benefits it can bring your organization.

We break down the concept of data strategy into a series of foundational components, examining the data flow from source to consumable, actionable insights. The resulting data strategy framework will ultimately align your data assets and business outcomes through technology. 

Whether adopting a Data Mesh or Data Fabric, our primary focus will be on delivering business value through a data-driven framework that provides insights at scale. We'll share lessons learned from WWT's own data strategy work along the way.

Evaluating Data Maturity

We begin by assessing your data maturity, focusing on analyzing current data ecosystems and preparing for the integration of AI technologies. This evaluation is pivotal for understanding your organization's readiness to leverage advanced data capabilities.

Comparative Analysis: Data Fabric vs. Data Mesh

This segment provides an in-depth comparative analysis of Data Fabric and Data Mesh. We'll outline the benefits of Data Fabric, emphasizing centralized data management and interoperability, and the advantages of Data Mesh, which include decentralized data ownership and domain-oriented management. We will also discuss key considerations related to data warehouses, data lakes and data lakehouses in the context of choosing a data fabric approach.

Choosing the Right Approach

To help organizations select the most suitable approach, we discuss selection criteria, the advantages and disadvantages of each method, and strategies for aligning different approaches with organizational goals and existing infrastructure.

Implementation Guidelines

The implementation segment offers high-level steps for adopting either Data Fabric or Data Mesh, providing practical guidance on how to effectively integrate these models into your data strategy.


At the end of the briefing, our closing remarks summarize the key points discussed and outline actionable next steps. This session aims to equip participants with a comprehensive understanding of their data strategy and the tools necessary to align data initiatives with business objectives.  

Topics covered  

The Importance of Data Strategy  

  • Definition and components of a data strategy
  • The role of data in modern business
  • Benefits of a strong data strategy

Evaluating data maturity  

  • Data ecosystems
  • Getting Ready for AI

Data Fabric vs. Data Mesh: Comparative Analysis  

  • Data Fabric: Overview and Benefits
    • Centralized Data Management
    • Integration and Interoperability
  • Data Mesh: Overview and Benefits
    • Decentralized Data Ownership
    • Domain-Oriented Data Management

Choosing the Right Approach: Data Fabric vs. Data Mesh  

  • Criteria for Selection
  • Pros and Cons of Each Approach
  • Alignment with Organizational Goals and Infrastructure

Implementation Scenarios  

  • High-level steps for either data fabric or data mesh

What is a Briefing? 

A scheduled event with a WWT Subject Matter Expert — typically via a live Webex — where our experts present an overview of specific topics, technologies, capabilities or market trends. Your attendees are allotted time for Q&A to pose questions specific to your organization.  

Who Should Attend? CEOs, CIOs, CDOs, data owners, line of business owners, IT Directors or anyone interested in learning more about how a comprehensive data strategy enables and transforms business, delivering timely, actionable insights.

Post Briefing Actions? A data strategy workshop focused on business use case development and data source identification and evaluation.