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

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Legacy data – the information stored in outdated, and often siloed, systems – is frequently seen as a challenge.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.

As we come to the end of 2024, we round up the most popular technical articles read by IT directors, CTOs and other tech leaders on our technology insights blog.

For AI and machine learning systems, technical debt extends beyond code to include complex dependencies in data, models, and operational workflows. In this article, we explore how technical debt differs from traditional software engineering, and actionable strategies with MLOps that can help to manage tech debt effectively.

From our latest Halloween-themed Tech Talk, Principal Test Engineer, Emily, shares tips and tricks to avoid development team horror stories, sharing best practices testers can implement to ensure improved quality, effective communication and continuous improvement.

Generative AI is a technology that can help in simplifying legacy modernisation, offering solutions that go beyond what traditional techniques can achieve. This article explores how generative AI specifically enhances various stages of legacy modernisation, from automated code generation and documentation to testing and data migration.

Emily focuses on a common pitfall among development teams - assuming they know their users because they understand the product. Emily highlights how development teams should step outside of their perspective and embrace user-centric design to improve product quality.