🔗 Links & Resources:

👉 Explore our training courses to level up your data management skills: https://bit.ly/4eNkczq

👉 Download our free Data Vault resources: https://bit.ly/4f9AYs9

📢 Join our user group for more discussions, events, and resources.

👍 Like this video if you found it helpful

🔔 Subscribe for more episodes on modern data practices

🔗 Links & Resources:

👉 Explore our training courses to level up your data management skills: https://bit.ly/4eNkczq

👉 Download our free Data Vault resources: https://bit.ly/4f9AYs9

📢 Join our user group for more discussions, events, and resources.

👍 Like this video if you found it helpful

🔔 Subscribe for more episodes on modern data practices

🔗 Links & Resources:

👉 Explore our training courses to level up your data management skills: https://bit.ly/4eNkczq

👉 Download our free Data Vault resources: https://bit.ly/4f9AYs9

📢 Join our user group for more discussions, events, and resources.

👍 Like this video if you found it helpful

🔔 Subscribe for more episodes on modern data practices

🔗 Links & Resources:

👉 Explore our training courses to level up your data management skills: https://bit.ly/4eNkczq

👉 Download our free Data Vault resources: https://bit.ly/4f9AYs9

📢 Join our user group for more discussions, events, and resources.

👍 Like this video if you found it helpful

🔔 Subscribe for more episodes on modern data practices

🔗 Links & Resources:

👉 Explore our training courses to level up your data management skills: https://bit.ly/4eNkczq

👉 Download our free Data Vault resources: https://bit.ly/4f9AYs9

📢 Join our user group for more discussions, events, and resources.

👍 Like this video if you found it helpful

🔔 Subscribe for more episodes on modern data practices

In this episode, Neil Strange and Alex Higgs unpack the buzz around data contracts—what they are, why they matter, and how to implement them in real-world data pipelines.

Whether you're dealing with broken data pipes, poor quality, or unclear ownership between systems, data contracts can help. This episode covers the basics, shares practical examples, and points to tools like dbt, as well as books by Andrew Jones and Chad Sanderson.

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⏱️ Timestamps:

00:00 – Intro: Why talk about data contracts?

00:33 – What is a data contract (and why do we need them)?

01:47 – How are data contracts actually enforced?

02:20 – dbt’s approach to data contracts

03:13 – Governance: What happens when a contract is broken

03:43 – Preventative vs reactive data quality

04:52 – Data contracts vs API versioning

05:23 – Recommended reading: Andrew Jones & Chad Sanderson

05:58 – Using contracts in your architecture

06:16 – Are they overkill? When to start using them

06:33 – Lean thinking and early detection

07:03 – When to define data contracts in your pipeline

07:27 – Final thoughts and how to get involved

💬 Drop your thoughts in the comments. Agree, disagree, or want to go deeper? Let’s talk.

📢 Join our user group for more discussions, events, and resources.

👍 Like this video if you found it helpful

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In this episode, Neil Strange and Alex Higgs dive into the real-world pain of working with XML. From legacy systems and unreadable schemas to bloated file sizes and broken audit trails, Alex shares the kind of problems that don’t show up in the docs—but definitely show up in your project timelines.

✅ What you'll learn:

Why XML still shows up in modern data pipelines

The key differences between XML and JSON

How XSD helps (and hurts) when validating XML

Common XML mistakes that break audits and slow down delivery

When to fight for JSON instead

⏱️ Chapters

00:00 – Intro

00:14 – Why XML is a “necessary evil”

01:00 – XML vs JSON: key differences

01:18 – When you're forced to use XML

02:08 – Why JSON is usually easier to work with

02:22 – What is XSD and why does it matter?

03:10 – Hidden pitfalls: empty tags and missing elements

04:05 – Why XML breaks in production

05:00 – Real-world XML horror stories

06:01 – Data quality and governance issues

06:44 – Missing keys and broken audit trails

07:12 – Should you always choose JSON?

07:30 – Final thoughts + join the debate

📢 Got strong feelings about XML or JSON? Let us know in the comments.

🔗 Links & Resources:

👉 Free modelling guides and resources: https://bit.ly/4f9AYs9

👉 Learn more about Data Vault modelling: https://bit.ly/4eNkczq

🔔 Don’t forget to like, comment, and subscribe!

In this episode, Neil Strange and Alex Higgs tackle one of the most misunderstood steps in building a data warehouse: *landing the data*.

From internal systems to APIs and SaaS tools, this isn’t just a technical job—there’s politics, governance, and reliability to think about.

They cover:

🔁 Push vs Pull

🔒 Access challenges

🧰 Tools (Fivetran, Airbyte, etc.)

🧪 When to build vs buy

🧾 CDC and data quality risks

🗂 PSAs and staging areas

Whether you're starting fresh or scaling pipelines, this will save you headaches.

In this episode, we tackle a common challenge for modern data teams: What should you do with all that JSON and other semi-structured data flooding in from APIs, events, and third-party sources?

We break down when to store, when to split, and how to stay sane in a world of endlessly nested payloads.

📌 Chapters:

00:00 – Introduction

00:10 – Why semi-structured data is everywhere

01:02 – Store vs. split: making the right call

03:15 – Handling schema drift and variants

05:24 – Data contracts and API payload challenges

07:41 – Tips for integrating JSON in your warehouse

09:06 – Common mistakes to avoid

10:00 – Key takeaways & next steps

🔔 Don’t forget to like, comment, and subscribe!

In this episode of the Business Thinking Podcast, we sit down with Joe Barter, one of our in-house analysts and data modelling experts, to tackle a hands-on question: *“How do I actually start a data model?”* Whether you're staring at a blank whiteboard or trying to wrangle ideas into a structure, we break down how to move from chaos to concepts — with practical tips, process thinking, and a little help from GenAI.

⏱️ Timestamps:

00:00 – Welcome and intro

00:18 – “I’ve been asked to build a data model — where do I start?”

00:33 – Just start sketching and asking questions

01:01 – Hubs and links: the core of Data Vault

01:23 – Start with a process model, not a data model

02:05 – Identifying concepts through business actions

02:41 – Using GenAI to seed your initial model

03:33 – How roles and perspectives influence modelling

04:16 – Paper, whiteboards, and sticky notes — why tools can come later

05:14 – Miro and collaborative concept modelling

05:39 – Embracing mistakes and iteration

06:10 – Evolving the model over time

06:45 – Concepts become hubs, relationships become links

07:08 – Model just the problem at hand

07:25 – Keep it simple: one A4 page is often enough

07:44 – Call for listener tips and favourite techniques

08:06 – Wrap up and outro

🔗 Links & Resources:

👉 Free modelling guides and resources: https://bit.ly/4f9AYs9

👉 Learn more about Data Vault modelling: https://bit.ly/4eNkczq

🔔 Don’t forget to like, comment, and subscribe!