The Business Thinking Podcast

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

🤝 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.

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

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

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

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In this episode of the Business Thinking Podcast, Neil Strange talks with Julian Redmond (Ignition Data) about a real healthcare data migration programme moving hospitals onto a centralised 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

📚 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