How Data Teams Earn Trust During Change and Transformation

How Data Teams Earn Trust During Change and Transformation

What happens when a business changes faster than its BI team can respond? Following his Data Community Meetup presentation, Neil Strange joins Alex Higgs to explore the challenges facing modern BI teams, why many organizations still rely heavily on spreadsheets, and what leaders can do to unlock more value from their data. They discuss the realities of data maturity, the politics of BI, the role of AI in modern analytics, and why the first 90 days of a transformation program can shape the future of a data team for years to come. Timestamps 00:00 Introduction 00:22 Why organizations struggle to see the full potential of data 01:14 Finding early adopters and creating momentum for change 02:55 The challenge of growing demand for BI 03:23 What developers should do when technical debt is ignored 04:01 Why BI often becomes invisible until something breaks 04:42 Underinvestment, communication, and organizational responsibility 06:39 The impact of AI on data teams and transformation programmes 07:05 How AI can accelerate delivery without cutting corners 08:00 Productivity gains, risks, and keeping humans in the loop 09:18 AI and the future of documentation, testing, and governance 10:23 Why AI still needs experienced data professionals 11:35 Using AI in real-world BI and analytics projects 12:03 Why BI is a political football inside organisations 12:58 How data teams influence business decisions 13:05 Why understanding business requirements matters more than technology 13:30 Closing remarks Connect with Neil Strange Neil Strange is CEO of Business Thinking and a regular speaker at Data Community events. Learn more: 🌐 https://www.business-thinking.com Join the Data Community: 🌐 https://www.data-community.org

What companies get wrong about Data Mesh

🤝 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 Corne 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.

Why AI Needs Ontologies with Juha Korpela

This is the follow-up podcast from the recent Data Community meetup with Juha Korpela.

Designing AI That Understands Your Data with Stephen Nwoye

In this episode, Alex Hayes 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

How to Plan a Successful Migration

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. If you’re planning a move from legacy systems to the cloud—or considering a major platform change—this episode will help you think through the decisions that matter most. In this episode: - Why organisations 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 Want to go deeper? 📚 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