For data to be a key enabler of business success, it needs to meet four requirements: Findable — People should be able to quickly find the data they need to make decisions. Accessible — Access should be provided to the right people, at the right levels, based on clear rules and governance. Interoperable — Data should integrate across systems so it stays relevant and usable across business functions. Reusable — Data assets should be reliable and repeatable so that the orga

No—this image wasn’t created with generative AI. The “Magdeburg Unicorn” is one of the most infamous fossil reconstructions ever made. It’s a vivid reminder of what can happen when we try to force unrelated or incomplete information into a single, tidy story: we can end up confidently arriving at the wrong conclusion. The Magdeburg Unicorn: A Cautionary Tale In the 17th century, a group of well-meaning scientists found a collection of old bones buried deep in what’s now Germa

The key to being AI-ready is to reframe the conversation. Skip the jargon and the hype and start, as I always say, with outcomes. Here are four areas to focus on first—so you can bring some reality to your situation: What problems are we solving? AI for AI’s sake is a waste of resources. I’ve said the same thing about plenty of hype cycles before. Define use cases that clearly tie to value—whether that’s reducing operational inefficiencies, improving customer experience, or

Think of your business data landscape as a flexible system that changes as your business does, not as a rigid machine. Interconnected AI/LLM and analytics data products form an ecosystem that lives and evolves, reconfiguring as the business changes. In this (greatly simplified) example, data products perform different steps across a business and analytics process. For example, let's look at a simplified example from a Michelin-starred fine-dining tasting menu, focusing on res

Anchor Everything in Business Outcomes I’ve seen many data initiatives focus inwardly on data itself rather than on the results it can enable. That’s also where people get data strategy wrong, as they somehow think it’s about the movement of data, which it isn’t. If you want an integration strategy, then call it that! As I’ve always maintained, data is an enabler, whether in raw form or as information. A world-class data operating model begins with a clear vision of the desir

An Old (But Great) Story That Perfectly Illustrates the Difference Between Data Analytics and Data Insights During World War II, the Allies mapped bullet holes in planes that Nazi fire had hit. They planned to strengthen the planes by reinforcing the areas that had taken the most bullets (shown as red dots in the picture). Theoretically, this was a logical deduction. After all, these were the most affected areas. I would also add: this is exactly what artificial intelligence

In a world where data is everywhere, businesses can easily feel overwhelmed. Every customer interaction, operational update, and market shift creates more information to sift through. But the real value isn’t in collecting data—it’s in turning it into clear insights that help people make better decisions. That’s where a strong data strategy comes in. When organizations treat data like a real business asset, they can spot opportunities faster, improve customer experiences, and

In a world where data drives almost every decision, knowing how to collect, manage, and interpret it can be the difference between success and costly mistakes. But using data well isn’t always straightforward. Misread numbers, messy databases, or poor processes can quickly steer teams in the wrong direction. In this post, we’ll look at real-world data mistakes, what organisations have learnt from them, and practical ways to avoid making the same missteps. Close-up view of a d

In today’s fast-moving business world, being able to use data well is a real advantage. Companies that make the most of their data can understand what’s happening inside the business, spot opportunities, and make changes that improve performance and strengthen their competitive edge. This post looks at practical ways businesses can shift from simply collecting data to using it to drive meaningful results. Why data utilization matters “Data utilization” means collecting data,

What about data? In today’s post, we’ll start with the real, tangible value of the data that’s unique to your business. Data has become the currency of digital transformation. Whether it’s uncovering new insights, improving decision-making, or driving better outcomes, the push to unlock the power of data has never been stronger. That’s exactly why data governance matters. Good governance helps ensure the data across your business is optimised for use—so teams can access trust

The AI revolution is here—but whether it succeeds depends on one often-overlooked factor: data. While much of the buzz is about powerful models and clever algorithms, the real work for enterprises is making sure the data feeding those systems is solid. The old saying still applies: models are only as good as the data behind them. The challenge isn’t just building AI applications—it’s making sure data is accurate, trusted, and accessible at a new level of speed and scale. For

Unleashing the Power of Data Conversion In today’s fast-moving market, turning raw data into useful insights is essential. This process—often called data transformation—helps businesses make smarter, faster decisions. Think of it as taking mountains of numbers and turning them into clear patterns you can actually use. It’s not just about data; it’s about understanding what the data is telling you. In this introduction, we’ll cover: How to simplify and organise your data so yo