Insights & Case Studies tagged with: data

AI in the Cloud Is Reshaping the Security Threat Landscape

AI is transforming cloud platforms – and expanding security risk. Learn how AI-driven workloads change the threat landscape and what organisations can do to secure them at scale.

Agentic AI After the Hype – What Actually Drives Business Value

Agentic AI promises productivity gains, but success depends on workflow design, governance, and human collaboration. Learn when agents add value and when they do not.

Your Engineers Are Losing 40% of Their Time — and Your Data Estate Is to Blame

Learn why AI initiatives stall without strong data foundations and how Vertex Agility helps organisations modernise systems, improve data quality and successfully scale AI. Discover how to fix technical debt, unlock engineering capacity and accelerate AI adoption with confidence.

Navigating the EU Data Act: What Free Data Transfers with Google Mean for Multicloud Architectures

Discover how Google’s free UK and EU data transfers align with the EU Data Act — and what this means for cloud migration, multicloud architectures, and data strategy. Learn how Vertex Agility can help your business adapt.

Multi-Cloud Excellence with AWS, Azure, and Google Cloud

Multi-cloud experts in AWS, Azure & Google Cloud. Vertex Agility delivers migrations, DevOps automation, AI solutions & cost optimisation for business growth.

Bridging Data, Cloud, and AI: The New Architecture for Competitive Advantage

Discover how integrating data, cloud, and AI can create a future-proof architecture for innovation and competitive advantage.

Why Data Strategy Is the Foundation of Effective AI

AI doesn’t exist in a vacuum. Even the most advanced models are only as effective as the data infrastructure, governance, and accessibility that underpin them.

10GB Later: Lessons in Data Protection from Toptal’s Breach

Here are the key data-protection practices every organisation should harden now.

Common Pitfalls in Data Modelling and How to Avoid Them

In this article, we’ll explore some of the most frequent mistakes made in data modelling and provide practical solutions to help you avoid them.

A Comprehensive Guide to Data Modelling Types, Approaches, and Use Cases

In this guide, we’ll explore these approaches, various types of data models, their comparative advantages, and real-world use cases for when to use each type of model.

Avoiding Common Data Engineering Pitfalls

Take a look at this article which explores common data engineering challenges in detail and provides actionable strategies to not only anticipate them but also mitigate their potential impact.

Eliminating Data Bottlenecks: A 99% Reduction in Data Conflicts

A leading trade services platform needed assistance in fixing and future-proofing their data pipeline, which we took from 2.2 million conflicts down to just 15,000. During this process, we introduced real-time synchronisation, removed manual overheads, and ensured scalability and stability far into the future.