In this episode of the Business Thinking Podcast, we sit down with Joe, our in-house data modelling expert, to unpack a common but crucial question: "When is the right time to use Data Vault?" From business readiness to migrations and AI integrations, we explore the key indicators that tell you when your organisation is ready to get value from Data Vault — and when it might not be the right fit just yet.

**⏱️ Timestamps:**

00:17 – What do we mean by the “right time” for Data Vault?

01:07 – Why a data warehouse is a **product, not a project**02:16 – The need for a **real business driver**

03:29 – Feeding clean data into **GenAI and LLMs**

05:04 – Data quality, clean datasets, and executive buy-in

06:07 – Strategy and the cost of missing insights

07:04 – Churn analysis, lifetime value, and drift modelling

08:29 – Using Data Vault for system or platform **migrations**

10:00 – Ensuring **uninterrupted reporting** during system changes

11:14 – Why Data Vault works well during **acquisition sprees**

---

🔗 Like this episode? Subscribe for more conversations on modern data strategy, data engineering, and business transformation.

In this episode, we tackle a critical question for data professionals: When is Data Vault *not* the best choice for building a data warehouse? We discuss:

00:07 – When a data warehouse might not be needed

00:36 – The complexity of multiple source systems

03:49 – When Data Vault might be overkill

04:20 – Dealing with massive, unstructured data streams

06:31 – The need for data integration to justify Data Vault

07:36 – The importance of a conceptual understanding of data

10:02 – Key takeaways for choosing the right approach

10:38 – Outro & next steps

🔗 Links & Resources:

👉 Explore our resources on data warehousing methodologies: [Insert relevant link here]

👉 Learn more about Data Vault principles: [Insert relevant link here]

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

In this episode, our CEO, Neil Strange, sits down with Joe Barter, our specialist in data modelling and analysis, to unpack the role of data modelling in modern organizations. We explore whether it's still a critical skill, and why it might be more relevant than ever.

🔗 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

In this episode, Neil and Alex discuss the importance of data literacy and what it takes to build a truly data-driven organisation. They cover everything from understanding basic data concepts to using the right tools and applying critical thinking when analysing data.

🔔 Subscribe for more insights on data engineering and data warehousing!

⏰ Timestamps

00:00 – Intro

00:08 – Meet Neil and Alex

00:13 – Why data literacy matters for a data-driven organisation

00:40 – The importance of basic data concepts (mean, mode, standard deviation)

02:53 – Appropriate use of tools (Excel vs. SQL)

04:13 – Using statistics to support business decisions

04:48 – Understanding causation vs. correlation

06:02 – Effective data visualisation and storytelling

07:36 – Critical thinking when working with data

08:41 – The danger of incorrect data driving decisions

10:14 – Fixing data issues and handling resistance to change

10:42 – Outro and next steps

In this episode, Neil Strange and Alex Higgs break down what success looks like in data projects. They discuss how to define success, align teams, avoid failure conditions, and tackle challenges like tech debt and documentation. If you’re involved in data engineering or data warehousing, this is a must-listen!

📌 Chapters

00:00 – Welcome to our podcast

00:08 – Introducing Neil Strange and Alex Higgs

00:20 – Why defining success matters

00:47 – Insights from the Worldwide Data Conference

01:42 – Making success clear to your team

02:24 – Understanding different business areas

03:28 – Importance of clear scope and documentation

04:08 – Involving engineers in decision-making

04:50 – The power of automation

05:42 – Managing tech debt

07:29 – Engaging the team and providing context

08:10 – Wrap-up and final thoughts

👉 Don’t forget to like, subscribe, and check the links in the description for more resources and ways to connect with us!

In this episode, Alex Higgs and Neil Strange discuss the...interesting topic of data warehouse colors. What do you think?

Failed data projects are still a major problem in the industry—even after decades of experience in data engineering and warehousing. In this episode, **Neil Strange** and **Alex Higgs** break down why projects fail and what you can do to improve your chances of success.

From poor processes and skills gaps to organisational politics, we explore the key challenges that cause delays, budget overruns, and missed business objectives. Plus, we discuss how to set clear success and failure conditions to keep your project on track.

⏱ Chapters:

00:00 – Intro

00:10 – The Big Topic: Failed Projects

00:50 – What Defines a Failed Project?

02:00 – Why Technology Isn’t Usually the Problem

03:07 – Skills Gaps in Data Teams

04:09 – Organisational Politics and Stakeholder Conflicts

05:44 – Scope Creep and Competing Priorities

06:55 – The Role of Process in Project Success

08:10 – Lessons from Successful Projects

09:02 – Defining Success and Failure Conditions

09:20 – Future Episode Teaser

09:41 – Outro

📢 Enjoyed this discussion? Like, subscribe, and hit the bell to stay updated on future episodes!

In this episode, we dive into one of the biggest challenges in data engineering and data warehousing—team sizes. Should a team be as small as two people, or do you need a large group to get the job done? We discuss:

- The impact of team size on productivity

- The role of automation in reducing workload

- Why some projects slow down despite large budgets

- The importance of skill distribution within a team

- How onboarding and documentation affect efficiency

🚀 Whether you're running a data warehouse or managing a data engineering project, this episode will help you rethink your approach to team structure and tooling.

📌 Chapters:

00:00 – Introduction

00:12 – The challenge of team size variation

00:52 – Do large teams slow projects down?

01:49 – Automation vs. manual coding: The real cost

02:43 – The essential skills for a data team

03:44 – Finding the ideal team size

05:00 – The problem with unnecessary manual work

06:16 – Why throwing more people at a problem doesn’t help

07:06 – Key takeaways for optimizing data teams

08:11 – Outro & next steps

🔗 Check out our resources and links in the description to continue your learning journey!

AI is everywhere, but how well do we really understand its capabilities and limitations? In this episode, Alex Higgs sits down with Ignition Data CEO, Julien Redmond, to break down the strengths and weaknesses of AI in business, including how large language models (LLMs) process information, the risks of AI bias, and why human expertise remains essential. We also dive into the environmental and financial costs of AI adoption and how businesses can prepare for AI integration.

🧠 What you’ll learn:

00:00 - Introduction

02:15 - How AI models process information

06:42 - The limitations of AI-generated insights

11:30 - Why human oversight is still crucial

15:55 - AI bias and misinformation risks

21:10 - The environmental and financial cost of AI

26:45 - Practical steps for businesses adopting AI

32:20 - The future of AI in business

🔗 Links & Resources:

🌍 Learn more about Ignition Data:

👉 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

👉 Book a free consultation with our team: https://bit.ly/3BT6VXC

🔔 Don't forget to like, comment, and subscribe! 🚀

In this episode of the Datavault Podcast, we sit down with Julien Redmond, CEO of Ignition Data, to explore the evolving role of data management in the age of AI. With automation and AI rapidly and expanding, Julien shares why experienced data professionals are more important than ever and how businesses can balance new technologies with trusted data governance practices.

🧠 What you’ll learn:

00:00 - Intro

00:08 - Meet Julien Redmond

00:40 - Why data management matters more than ever

03:10 - The role of AI in data governance

06:10 - Scaling expertise with automation

09:55 - Training the next generation of data professionals

11:20 - Where to learn more

🔗 Links & Resources:

🌍 Learn more about Ignition Data:

👉 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

👉 Book a free consultation with our team: https://bit.ly/3BT6VXC

🔔 Don't forget to like, comment, and subscribe! 🚀