Mike Hyde

The UK data talent market right now feels “selective, but still hungry”, especially for people who can ship production-grade data platforms and support AI/ML use cases. On the demand side, the work hasn’t slowed. What has changed is how picky teams are getting about scope and evidence. UK tech leaders are calling out AI skills scarcity as a major constraint, and that pressure is flowing straight into data teams (engineering + analytics + platform) because data quality, governance, and reliability are what make AI usable.  On the supply side, the market is tighter at the entry level and more robust for experienced hires. That’s consistent with recent coverage showing UK grad/junior job postings have been under pressure, while employers prioritise fewer, higher-signal hires.  Compensation is also holding relatively firm. IT Jobs Watch data has UK median Data Engineer pay around £69k over the six months to 1 Feb 2026, with vacancy volumes below the 2024 peak but stronger than the same period in 2025.  If you’re hiring: Tighter role outcomes (first 90–180 days), clear ownership boundaries, and a real view on hybrid expectations are what’s helping teams close roles faster right now. If you’re a candidate: The quickest way to stand out is showing what you’ve owned in production, what improved (reliability, cost, quality), and how you partnered with stakeholders. #DataEngineering #DataTalent #UKJobs #TechHiring #AI #AnalyticsEngineering #DataPlatform #MLOps

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Reliable data is the foundation of sound economic policy. In the UK, the "Office for National Statistics" (#ONS) which is an independent department reporting directly to Parliament, has delayed its latest UK retail sales release by two weeks, citing the need for additional quality checks. Retail sales are a key barometer of consumer confidence and spending, shaping decisions at the Bank of England on interest rates and informing government policy on growth and living standards. While the pause raises questions about consistency, it also underlines the critical importance of accuracy. A single misstep in reporting inflation, GDP, or jobs data can ripple through markets, impact borrowing costs, and alter the economic narrative for millions. Delays may frustrate, but I believe it is better to have a short wait for numbers which are reliable, than to publish uncertain numbers with the risk that decisions are based on flawed numbers. In statistics, as in policy, robustness, consistency through time and meaningful data matters more than outright speed. In the private sector, companies such as #Kantar #GfK #NIQ and #YouGov regularly face similar decisions and dilemmas - where some of the key input data looks noisy or is missing. That is why having strong processes based on rock solid scientific methodology, and sufficient expertise and bench strength in #statistics at all levels of the organisation is essential for any organisation publishing such data. Source - https://lnkd.in/eN8Ukc6R #datascience #confidence #decisionscience

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𝟮𝟬𝟮𝟱: 𝗧𝗵𝗲 𝗬𝗲𝗮𝗿 𝗼𝗳 𝗔𝗜 & 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝘁 𝗟𝗹𝗼𝘆𝗱𝘀 𝗕𝗮𝗻𝗸𝗶𝗻𝗴 𝗚𝗿𝗼𝘂𝗽 What an incredible journey the last 12 months have been! All driven by a single goal: maximising the potential of AI and advanced analytics to help Britain prosper. 3 X themes stood out: strong sponsorship, sustained momentum, and continued investment in people, technology, and ways of working. 𝗩𝗮𝗹𝘂𝗲  • £50m+ of value delivered from 50 GenAI use cases in production, alongside the launch of a first-of-its-kind direct-to-customer agent pilot.  • Industrial-scale generative BI, automating thousands of reports and dashboards for faster, better data-driven decision-making.  • Real-world AI use cases delivered in production, including Athena, Merlin, Maddison, CRE AI, and a patented Global Correlation Engine. Read more on our AI @ LBG Blog https://lnkd.in/em-5Kzyk  • Lloyds Banking Group recognised as “Outstanding” in Euromoney’s 2025 MarketMap of the World’s Best Digital Banks. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆  • Extended our advanced analytics capabilities from data science to Gen AI >> to now building an enterprise-grade agentic AI platform, enabling more autonomous, intelligent systems.  • Migrated our ML to Vertex, empowering 300+ data scientists and AI engineers to build, deploy, and operate models on a modern, scalable platform with improved flexibility and speed.  • Launched Athena — an AI knowledge hub surfacing answers from 13,000+ internal articles, transforming how customer service colleagues access trusted information.  • Rolled out Copilot to more than 30k colleagues, so we can be a frontrunner in enterprise AI productivity tooling. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀  • Data & AI Tech Academy had 40,000+ colleagues registered across 156 courses. Recognised at the British Data Awards 2025, as the Education Initiative of the Year.  • Over 110 senior leaders completed the 80-hour “Leading with AI” programme at Cambridge University, further rollout planned through 2026.  • Launched #AINinjas, providing senior leaders with their own AI Ninja to build confidence, capability, and safe adoption of AI.  • AI is becoming mainstream within the Lloyds Technology Centre, supported by the appointment of a our LTC AI Lead. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀  • We strengthened the foundations for scale and trust. A leadership-aligned AI Control Tower, 11 AI Big Bets, and the build-out of a central AI and Agentic platform now provide focus, governance, and speed.  • Continued investment in Responsible AI, recognised by DataIQ Award for Best Responsible AI Programme. This disciplined approach underpinned the most significant improvement in the Evident AI Index among UK banks (from #27 to #15). Thank you to our leaders, engineers, colleagues, and partners for an exceptional year of progress. 2026 will be about scaling impact even further.

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Thrilled to reshare the podcast episode I was featured on with the brilliant Erin Evans! We unpacked three huge topics that are top of mind for everyone right now: Enabling Power: How AI is truly empowering individuals and reshaping organizations. The Ethics First Approach: The non-negotiable imperative of building AI systems with ethics at the core. The Agentic Frontier: Breaking down the complexity of agentic AI and what it means for the future of work. Take a listen and let me know your biggest takeaway in the comments! 👇

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In 2006, Clive Humby coined the phrase “Data is the new oil.” He was working as a data scientist at Tesco, the British grocery store, and was making the point that just collecting a bunch of data about customer shopping behavior wasn’t enough to increase profits. There was a lot of data science work required to actually get value out of it. It wasn’t enough to have the data (oil) in the database (ground), you had to refine it. It was a fine point then, but it never fully resonated because the value of the raw data was so low, unlike oil. And data was not fungible at all. A bunch of analytics data from a random mobile game was nowhere near as valuable as, say, all of Reddit. How could data be the new oil if it was not a commodity? Well maybe we can bring the metaphor forward into the AI era by reexamining some of the similarities. If data is crude oil, the refinery process is more important than ever. We wring intelligence out of raw data and the result can be low octane gasoline or jet fuel depending on the processing methodology. The shape of the data industry also seems to be morphing to look slightly more like the oil and gas industry. Not only are exploratory tracts of data like Reddit comments traded speculatively before they can be fully processed, but the modern data firm has significant capex and needs to use debt as effectively as the supermajors. The tech / VC community isn’t super used to understanding the implications of marshaling the debt markets to their fullest extent possible. Most tech people still dream of the early Google years when revenue grew from $86M in 2001 to $3.2B in 2004, while net income went from $10M to $400M over the same period (this is after stock-based comp btw). That doesn’t look like an oil business! But big tech and big oil are now converging, both in the scale of infrastructure buildouts and also literally, with Chevron announcing that it will bring a power plant in West Texas online in 2027 specifically for an AI data center. In traditional venture, assessing where risk lives is so simple. Preferred stock gets paid out first, then common. Startups are usually either huge or effectively zeros. Even if there’s a correction, venture plays in so many uncorrelated industries that the industry was even able to make it through the SVB crisis without any of the flagship firms blowing up. _______________________________________ This is a preview of today's TBPN newsletter. Get the full piece, plus headlines and breaking news, in your inbox every morning by dropping your email at tbpn.com

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It’s been really great working with the Economist Intelligence: EIU team to bring the first of many AI capabilities to life. At first glance this might look like a straightforward RAG implementation (how easy!) - but to make something enduring there's actually a lot of subtlety that goes in to making it work really really well for EIU users, use cases and style As with most AI initiatives, the secret sauce here is really about the tight coupling between the technical teams and EIU's research SMEs. AI presents such a fast-moving tech frontier that relying on technical innovation alone makes for a flimsy moat. My experience is that the real IP advantage comes from the intersection of proprietary data, human expertise and thoughtful implementation - and that’s exactly what makes this launch distinctive.

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Sure, working in data is about uncovering insights but it is also about destroying assumptions and pushing on the bounds of what’s possible. There has been absolutely no shortage of that in the WineFi 🍷 quantitative journey so far. Much more to come I am sure. 📹 Video credits to Marcus Grip

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Automated reporting of budgets and pacing can be tricky. Often involves spreadsheets and lookups and manual effort. Bright Analytics taps into campaign budget information through our platform API connections, meaning reporting on how budgets are being spent and campaigns are pacing can be automated and easy to act on, saving time and avoiding a nasty shock. #marketinganalytics #marketingops #automation

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Fantastic time had at the 3rd Annual Responsible AI Summit in London this week. Some new things I picked up: - New Consensus: AI governance has to be about enablement, not just compliance. - New Priority: Everyone is mobilising around governing AI agents. - New Question: What's your organisation's policy on AI wearables? - New Evidence: The level of AI bias in the same task varies across different languages (new proof from BBVA research). - New Risks: Agentic risk of cascading hallucinations, and dynamic value drift (where agents can learn to over-prioritise short-term goals) - amongst many more. - New Challenge: What's your human in the loop really for - to take the blame, or to review, ratify, or perhaps justify the decision? - New Analogy: If AI is Hannibal Lecters, AI Security is keeping them in prison, and AI Safety is stopping them from wrecking themselves and the prison New Contacts: Loads! So energising to meet many bright brilliant people working in the field. Well done to Mhairi Macbride and the team for putting on a fantastic event. See you next year. #RAISummit #aigovernance #responsibleAI

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Spot the difference 👀 I was reading a recent BBC article on where new UK #datacentres are being planned—based on Barbour ABI analysis—and couldn’t help but compare it to our work at Frontier Economics. Alex Whittaker’s analysis shows where datacentres should go, if you optimise for thinks like fibre connectivity, power infrastructure, water access, and workforce availability. The contrast is striking—and it highlights the challenge facing the UK Government’s #AI Growth Zones strategy. See our article here: https://lnkd.in/exSSMzQ8

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✨ Reflections from Snowflake World Tour London ✨ I had the chance to attend the Snowflake World Tour in London, and it was an inspiring look at where the future of data and AI is heading. The message was clear: we’re entering a new era where data platforms are evolving beyond storage and analytics into engines of intelligence. Snowflake is positioning itself at the centre of that shift — not just as a data warehouse, but as a foundation for AI, governance, and innovation. Some key themes that really resonated with me: 🚀 From potential to readiness: AI opportunities are enormous, but many organisations still face challenges with siloed, low-quality data and legacy architectures. Bridging this gap is becoming a strategic priority. 🔗 Trusted data as the foundation: Solutions like Semarchy’s native MDM integration with Snowflake show how unifying and governing core data is essential for building reliable analytics and enabling AI at scale. 🧠 AI, but contextual: Capabilities like Cortex AI and Semantic Views highlight a future where AI isn’t just powerful, but also deeply aware of the business context it operates in — unlocking more relevant insights and intelligent decision-making. ⚙️ Acceleration and agility: Innovations like Gen 2 Warehouses and Snowpark Connect for Apache Spark show how quickly the ecosystem is evolving to meet the demands of modern data products and real-time intelligence. More than anything, the event reinforced how critical it is for organisations to rethink their data strategies holistically — focusing not only on technology, but also on governance, trust, and usability to truly unlock AI’s potential. I’m walking away with fresh ideas on how we can apply these principles to deliver more impactful, intelligent, and trusted data products in our own work. #SnowflakeWorldTour #DataStrategy #AI #DataLeadership #Innovation #MDM #CortexAI #ProductManagement

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We're pumping some 🔥 into the belly of deep AI research at digiLab 👇👇👇 Launching 'theLab' is about a whole new way to do AI research. The toughest problems in mathematics & AI, high-stakes applications and grand challenges that impact society. Sound like a bit of you? Here’s what we’re cooking: 🧠 SLIMs — AI agents on the edge: tiny, probabilistic specialists, redefining the future of meta-learning and zero-shot inference. 📐 Building a probabilistic understanding of geometry into every AI operation. For the maths nerds out their, we're applying our Bayesian outlook to the frontier of foundational AI. 🤝 Humans in the loop — exploring the future of full-stack human–machine teamwork. What does “AI that collaborates” really look like? And the best part… ✨ Welcoming Dr. Katie Finch as Director of theLab — here to fuel innovation, grow partnerships, and push the frontier of what’s possible. Let’s build the future. 🚀

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NotebookLM is an awesome tool for research and study. I think it is turning to be almost as a new interface for Search, we have been using Google as it is for years but sometimes our questions and what we are looking for are more specific or more complex than a websearch. Examples of usage: - Opening a coffee shop in Brazil -> It will give you many sources and information that you can filter or deep dive into. - Planning a trip - Learning a new skill This new way of addin sources expand our hability of using it as a "new Search" system. https://lnkd.in/g95dWSVe

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When AI works well, it can feel like a superpower 🦸💪 So, how can you make the most of that feeling at work? Tom Cheesewright's latest article with MHR and the Telegraph Media Group breaks down some top tips to help you find the right balance of streamlined automation and human creativity 👇

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How much of the £9 billion spent on #insight is TOTALLY wasted? Estimates of the size of the UK research, insight and analytics industry is currently around £9 billion (source: Market Research Society). But a significant proportion is spent on insight that never gets used - never gets implemented - never creates any impact. Sad times 😢 This topic is actually a little more complex and nuanced than you might expect from the headline. Why? I think a multiple set of reasons behind this: 😱 THE RESEARCH MISSES ITS MARK - FINDINGS ARE IRRELEVANT This certainly can and does happen. Typically when the client & supplier are out of sync, perhaps thru miscommunication or below par briefing. Or the lack of experience and commercial nous from the researchers mean that findings are just a bit too meh, or a bit too out of touch. 🤔 CLIENTS DON'T KNOW HOW TO IMPLEMENT INSIGHT What I've always referred to as the 'insight-adoption gap'. This is where the task of implementing insight is beyond the scope of the researcher and beyond the experience of the client (who's skills and experience may well lie somewhere very different). A kind of stalemate position is reached - the opportunities are wasted. 🚧 THE RESEARCH WAS NEVER INTENDED FOR CHANGE This might feel wrong instinctively but often research is commissioned to provide evidence for something the organisation already 'knew' but didn't have 💯 certainty over. It might never have been about driving change. Or, worse - I've witnessed research being used to prove a point, to win an argument between people, teams, depts. So what can we do about this? Some top line thoughts: ✅ Ensure you're only buying insight when you REALLY need it ✅ Brief your suppliers well (use our research version of #BetterBriefs) ✅ Assign a team who will be responsible for implementing insight ✅ Bring your research partner in to help you bridge the gap Our new diagnostic tool (see Customer iQ website) is designed to find out how good you are at both 1) understanding your customers and CRUCIALLY 2) how well you action that insight. What are your thoughts about this? 💬 _______________ My name is Gideon, founder of Customer iQ I live in the world of customer insight Like this post? Follow me for more 🖖

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Are large language models a dead end? BBC Radio 4 explored whether LLMs genuinely “understand” the world — or simply optimise probabilistic pattern prediction at scale. It’s an important distinction. We’ve built an entire commercial ecosystem around scaling transformer architectures. But scaling is not the same as grounding. And next-token prediction is not cognition. There’s a growing tension between: • Parameter scaling as progress • Structural world modelling • Neuromorphic / brain-inspired architectures • Governance layers sitting above probabilistic systems If LLMs are a hack (as some argue), then what replaces them? Hybrid systems? Cognitive architectures? Layered governance frameworks? The more interesting question may not be “Are LLMs a dead end?” It might be: What architectural layer comes next? Curious where others working in AI systems see the trajectory.

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Too many segmentation models never make it out of PowerPoint. You might have a snazzy set of personas, or archetypes to make the c-suite swoon, but are they actually helping drive results? Which customers are in which segment? How are you targeting your most valuable segments? Do you change how you speak to them? And how is this driving value? (What do you mean you don't know how to measure value?...) You catch my drift. Laura's blog explores why so many segmentations fail, and how to make sure they don't.

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Plenty to unpack at the Startup Network Europe's London AI Conference. True thought leaders from Schroders Peter Jackson Bridgepoint Group Dominic Gallello RWS Group Inspired Thinking Group (ITG) Lauren McBride picking apart key areas that resonated deeply: Highest impact areas of AI don't always have to be grand projects - immediate ROI comes from the lowest hanging fruit opportunities (e.g., automation of manual processes). Innovation mentality. This is a core element to deliver success in the sea of AI applications - being wary of what's impactful vs nice to have. Whilst our Cortex shows a 5-10% uplift when AI modules are successfully embedded, pricing for AI remains a core discussion point. What can be leveraged as features to make customers sticky vs what is transformative that is realised in the value of the AI product suite? Benchmarks are shifting - you must adapt. Extracting value from AI is more than just the deck stack. Pivotal to embed innovation across culture, mindset and capabilities to stand out in this ever developing market.

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The 'Participation Trophy' Era of AI is Over The headline statistic from McKinsey’s State of AI 2025 report is that while 88% of organisations have adopted AI, only 6% (the "Vanguard") are seeing significant financial returns. The remaining 94% are stuck in "pilot purgatory," sprinkling AI on legacy processes rather than reworking them. We have entered the era of Agentic Darwinism, defined by three shifts: - Generative to Agentic: We are moving from chatbots that write to autonomous agents that do (executing multi-step workflows with minimal oversight). - IT to Strategy: For the Vanguard, AI is a CEO-led strategic imperative, not an IT support ticket. - Efficiency to Innovation: The 94% use AI to cut costs, whilst the 6% use it to build entirely new business models. The market is bifurcating. Organisations will either become Agentic Innovators that are rewiring for scale or remain niche, human-centric boutiques. The "middle pack", or those treating AI as a bolt-on tool, will be squeezed out, bearing the costs of tech without the speed of the Vanguard. #AI #McKinsey #AgenticAI #FutureOfWork #TechStrategy

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