Technical insights, stories and opinions from our team of consultants, analysts, developers and testers.

Technology leaders regularly make decisions that shape the technical direction of organisations. Each choice regarding frameworks, languages or tools influences how systems are built and maintained. The challenge is keeping those decisions aligned across organisations. One of the most effective ways to achieve that alignment is by using a Tech Radar.

As we’re over halfway through 2025, we’re rounding up the most popular content read by IT directors, CTOs and other tech leaders on our technical insights blog so far this year.

Successful software projects are rarely the result of chance. They emerge from a disciplined approach to understanding the problem, structuring requirements , and aligning teams. These same principles – clarity of vision, scope control and collaboration – are as essential to replacing a medieval Lego castle as they are to delivering a complex software system.

For many businesses, documents are the backbone of daily operations. Yet manual processing of these documents can be slow, error-prone, and often risky when it comes to handling sensitive data. This article explores how Azure Document Intelligence helps organisations automate document workflows, improving speed, accuracy, and compliance.

Without legacy constraints, greenfield projects allow teams to bake in modern practices, cloud-native architectures and a developer-first culture from day one. This article discusses how development teams can lay solid foundations when starting from scratch.

As data continuous to become the biggest operational challenge for organisations, here we discuss the question of whether teams should look to build their own data solutions in-house or, instead, leverage pre-built third-party data products.

In the rush towards AI and machine learning, it’s easy to forget that many business problems can be solved, often more transparently and robustly, with traditional mathematical and statistical techniques. This article highlights examples where statistical models or mathematical techniques can provide appropriate solutions in place of complex AI.

Legacy data – the information stored in outdated, and often siloed, systems – is frequently seen as a challenge. The challenge is how to make this old data serve new objectives - such as real-time analytics, customer 360 views, AI-driven insights - without a complete overhaul of legacy systems. Here we focus on different approaches that enable organisations to leverage legacy data in modern architectures.

AI systems rely on vast amounts of data, where data quality is the biggest factor when ensuring model performance, however we must also carefully consider how well the data is structured, processed and made accessible. This article outlines the key strategies for designing data architectures that are robust, scalable and capable of supporting AI workloads in large organisations.