The Podcast Story

🤝 Join the Data Community here: https://www.data-community.org/join Data Mesh has been one of the most talked-about concepts in modern data architecture. But how much of the conversation is practical reality — and how much is hype? In this episode of the Data Community Podcast, Alex sits down with Corné Potgieter to unpack what Data Mesh actually means in real organisations. They cover: • Why Data Mesh is often misunderstood • The mindset shift organisations underestimate • How Data Products and Data Contracts are changing enterprise data teams • The balance between autonomy, governance, and standardisation • Why AI is bringing renewed interest to Data Mesh • Common mistakes companies make during adoption • Practical lessons from real-world implementations This conversation goes beyond theory and social media buzzwords. If you're leading a data transformation initiative, building modern data platforms, or trying to make your organisation AI-ready, this episode gives you a grounded look at what works — and what doesn't.

🤝 Join the Data Community here: https://www.data-community.org/join In this episode, we sit down with Juha Korpela — data and semantics expert — for a deep-dive conversation following his presentation on semantics and ontologies at our Data Community Meetup. We explore why ontologies are having a moment right now, what's holding back the tooling, and why the real challenge isn't technical at all. Topics covered: Why AI is driving renewed interest in ontologies and the semantic web ✅ The "silo problem" — why building isolated AI agents works short-term but breaks at scale ✅ Why every organization needs a semantic architecture vision before it's too late ✅ The current state of semantic layer tooling (and why it's still too platform-specific) ✅ The gap between data catalogs, semantic models, and what AI actually needs ✅ Why your organization's unique understanding of its own data is its greatest competitive advantage — and why no LLM can generate that for you About the guest: Juha Korpela is a data modelling and semantics practitioner with over a decade of experience. His master's degree focused on ontology visualization, and he now works at the intersection of AI, knowledge graphs, and enterprise data architecture.

📚 Download free resources: https://www.business-thinking.com/resources 🎓 Explore our training courses: https://www.business-thinking.com/datalearn 🤝 Join our community: https://www.data-community.org/join 💼 Work with us: https://www.business-thinking.com/services In this episode, Alex Higgs is joined by Stephen Nwoye following his presentation at a Data Community meetup. They explore what it takes to move beyond basic retrieval-augmented generation (RAG) and build AI systems that truly understand organisational data. Stephen explains how curated vector databases, summarised context layers, and strong governance frameworks help control what AI can access, improve relevance, and protect sensitive information. The conversation also covers the practical challenges of implementing governance in large organisations, why starting with a primary dataset is key, and how building a central knowledge repository improves trust, speeds up development, and reduces reliance on tribal knowledge. They also discuss testing approaches, documentation at scale, and why owning your organisation’s knowledge layer unlocks future AI opportunities — even without direct access to live data. Topics covered: ✅ Why governance matters more than just connecting AI to documents ✅ Using vector databases as a curated knowledge layer ✅ Starting small with a primary dataset ✅ Building trust through documentation and transparency ✅ Reducing tribal knowledge and speeding up onboarding ✅ Testing AI-assisted data development ✅ Owning your organisation’s AI-ready knowledge repository

Migrate your data platform the right way with Business Thinking. Book a free introductory call to work out your next steps: https://bit.ly/4u9RH5O Data migrations can be complex, expensive, and risky if they’re not planned properly. In this episode of the Business Thinking Podcast, Francesco and Catherine break down the key factors every organisation should consider before migrating data to a new platform. They discuss why companies migrate their data in the first place, how to define success before starting the project, and the practical decisions that shape a migration strategy. You’ll also learn about common migration approaches like lift-and-shift vs transformation, when a phased rollout makes more sense than a big-bang migration, and the hidden costs many teams forget to account for. The conversation also covers two areas that are often overlooked in migration projects: security and regulatory compliance. From GDPR requirements to user access management, proper planning can prevent major problems later. In this episode: - Why organizations migrate data platforms - Lift-and-shift vs transformation migrations - Phased migrations vs big-bang approaches - The hidden costs of data migration projects - Security, compliance, and GDPR considerations

📚 Download our FREE Medallion Guide: https://www.business-thinking.com/resources/why-build-your-data-platform-with-medallion-architecture 🎓 Explore our training courses: https://www.business-thinking.com/datalearn 🤝 Join the Data Community: https://data-community.org/join 💼 Work with us: https://www.business-thinking.com/services

In this episode of the Business Thinking Podcast, Neil Strange talks with Julian Redmond (Ignition Data) about a real healthcare data migration program moving hospitals onto a centralized patient management system. What began as a standard rollout changed fast when clinicians pushed back, forcing the team to run legacy and new hospital systems in parallel for over 18 months. They discuss: - real-world healthcare data migration challenges - how to keep patient systems and reporting running during platform change - why enterprise migrations fail due to adoption, not technology - the role of architecture, data warehouses, and ontological modelling - lessons applicable to government, finance, and large enterprises Subscribe for more real-world data architecture and transformation discussions.

In this festive special, Alex and Joe ask a deceptively simple question: how would you model Santa as a business? From global logistics and manufacturing to petabytes of customer data, naughty-or-nice event tracking, and GDPR nightmares, they explore Santa’s operation through the lens of data modeling and business systems. A light-hearted but surprisingly deep thought experiment for anyone who enjoys thinking seriously about ridiculous problems.

Choosing a cloud provider isn’t simple — especially when AWS, Azure, and GCP all promise similar things but deliver them in different ways. In this episode, Joe and Alex break down the reality behind the three major cloud platforms. You’ll hear: 👉 Why Azure is gaining traction with enterprises 👉 When AWS is still the best choice 👉 Why some startups favour GCP 👉 The role of pricing, training, existing tech stacks, and time-to-market 👉 The strengths and gaps across each platform 👉 How cloud outages reveal hidden dependencies you didn’t know you had 📚 Download our FREE Medallion Guide: https://www.business-thinking.com/resources/why-build-your-data-platform-with-medallion-architecture 🎓 Explore our training courses: https://www.business-thinking.com/datalearn 🤝 Join our community: https://forum.ukdatavaultusergroup.co.uk/ 💼 Work with us: https://www.business-thinking.com/services