A data strategy  is a practical roadmap that explains how an organisation will use and manage its data to achieve its business goals. It sets out a clear approach for how data is collected, stored, analysed, and governed. This white paper walks through how to design and implement a data strategy , including the key steps involved, common challenges, and best practices. 1) Start with business goals Building an effective data strategy begins with understanding what the organisa

A common scenario is unfolding in boardrooms: a polished AI demonstration is presented, stakeholders express approval, a roadmap is developed, and budgets are allocated. However, challenges quickly emerge: data is inconsistent, processes lack standardisation, and user trust is limited. EASA’s AI Roadmap 2.0 and NIST’s AI Risk Management Framework (AI RMF) address these issues by focusing on the requirements for safe and responsible AI adoption, rather than promoting AI itself

Business leaders are hearing a lot about AI—usually through polished decks, confident roadmaps, and big promises. But before you scale anything, there’s one question that matters most: Is your data foundation strong enough for AI to be trusted in the real world? AI isn’t a strategy. It’s a capability. The strategic decision is simpler—and harder—than most roadmaps admit: Will this reliably improve a decision you care about (pricing, risk, service, fraud, forecasting) at scale

Introduction Today let’s take a look at how digital technology is reshaping aerospace and military manufacturing. This eBook will help you understand the key ideas behind digital transformation and how to apply them in industries where precision, reliability, and compliance matter most. The Beginning of a New Era In the age of Industry 4.0, digital transformation isn’t a “luxury” to have”—it’s becoming essential. It’s changing how organisations design, build, and maintain the

In today’s tech world, many companies are entering the realm of artificial intelligence with great enthusiasm—only to quickly face a disappointing reality: inconsistent results, unreliable models, and performance that fails to meet expectations. What makes these businesses different from those that successfully deploy robust, high-performing AI systems? The quality and readiness of their data. This article explains what it truly means to have data that is suitable for modern

Boards aren’t funding AI because it’s fashionable—they’re funding it for growth, cost reduction, cash discipline, and risk control . Yet many programmes stall after the first dashboard because the business can’t confidently answer simple questions, such as “What is revenue?” “Who is the customer?” “Which numbers are audit-ready?” That’s not a data-science issue. It’s a management system issue: inconsistent definitions, weak ownership, and processes that don’t reconcile end-t

Despite all the excitement—and the clear monetisation opportunities—around IoT and machine/sensor blockchain over the past few years, one major concern keeps coming up. From my experience working on IoT projects as a lead evangelist architect, a key high-risk area—discussed both publicly and behind closed doors—is end-to-end data security throughout the entire data journey, in both states: data at rest  and data in motion . While data and communication protocols at the machin

One thing stays true in today’s fast-changing corporate world: data volumes will keep growing. And as AI continues to accelerate, it’s clear that companies need to put their data to work to stay competitive. Traditional data and analytics (D&A) operating models have served us reasonably well—but in an era of rapid transformation, many are starting to show their age. Like legacy software, they can struggle to meet the complex demands of modern businesses. That’s why the case f

Most of us who have worked for organisations have lived through at least one "big transformation", which promised everything but quietly faded away.  New platforms launched, consultants turned up, ran workshops, and disappeared. Slide decks multiplied, and a year or two later, people were still emailing spreadsheets around and wondering what, if anything, had actually changed. That usually isn’t because the tech was bad. The deeper issue is that transformation isn’t really a

How to Align People, Data & Agents We’re moving from the era of “digital transformation” into a new phase: digital autonomy. It’s not happening overnight—and that’s normal. We tend to rush into new trends and only later realise they aren’t magic solutions. Recent stories about AI "agents" make that clear. Klarna, for example, put major effort into building an AI-driven workforce culture, but a year later, the promised gains in efficiency and customer satisfaction reportedly d

Enterprise data management (EDM): The processes, policies, and technologies used to effectively manage an organisation’s data assets. Core goals Ensure data quality Improve data accessibility Reduce data management risk Data management lifecycle (typical stages) Collection Storage Processing Integration Analysis Dissemination Archiving Governance and key disciplines Data governance:  Overall management of data availability, usability, integrity, and security . Data quality:

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 Ger

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

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