Modernising the Data Platform for AI and Scale
Defining future-state capabilities for a flexible, performant and cost-efficient platform supporting trusted data, analytics and AI services.
ROLE
Technical Business Analyst
DOMAIN
Data Platform · Cloud · AI
SCALE
Enterprise Transformation
Project Overview
I am contributing to a large-scale enterprise data platform transformation focused on creating a more flexible, performant, cost-efficient and AI-ready foundation for data, analytics and AI-enabled services.
The programme brings together platform modernisation, cloud-agnostic technologies, streamlined data processing and consistent, AI-enabled engineering capabilities. Its aim is to reduce vendor dependency and unnecessary compute costs, simplify how data moves through the platform and help engineering teams deliver trusted data and business value more quickly.
The transformation also establishes the reusable patterns, governance controls and scalable platform foundations needed to support evolving data and AI workloads safely, reliably and consistently.
The Challenge
The existing cloud-based platform had evolved through different technologies, tools and delivery approaches, resulting in duplicated capabilities, inconsistent engineering practices and complex data-processing journeys. Data passed through multiple transformations, increasing compute usage, delivery time and operational cost, while dependency on provider-specific technologies limited flexibility and increased the risk of vendor lock-in.
The platform needed to evolve beyond its existing architecture to support a broader range of processing patterns, workloads and AI-enabled capabilities, while reducing dependency on any single cloud provider. This required a more performant, scalable and observable foundation, supported by consistent engineering capabilities and the governance and security controls needed to make trusted data available safely and efficiently.