Satellites are a cornerstone of Data Vault 2.0, providing the descriptive attributes that contextualize Hubs and Links. In this article,...

PII Data in Data Vault While Data Vault itself does not provide specific guidance on handling PII, the challenge lies in implementing...

Data Vault Hubs Explained If you're working with Data Vault or just starting to explore it, understanding hubs is essential. They are...

Understanding Business Keys in Data Vault Business keys are a core concept in Data Vault modeling . They play a key role in identifying...

The Data Vault Q&A Forum If you're working with Data Vault and looking for answers to your toughest questions, you’re not alone. The...

Based on our episode 2 of The Business Thinking Podcast with our CEO, Neil Strange , and AutomateDV Product Manager, Alex Higgs . Is...

This blog post is b ased on episode 1 of The Business Thinking Podcast with our CEO, Neil Strange, and AutomateDV Product Manager, Alex...

When is Data Vault not the best option? - Business Thinking Podcast #20 Data Vault has become a popular data warehousing solution for...

The shocking truth is that Chief Information Officers (CIOs) are being convinced that hand-coding their Data Vault solutions is a good idea. This is a mistake. There are two groups who convince the CIO to make this expensive mistake. I have seen examples of both in recent weeks.

Starting with Data Vault data modeling can be a daunting task, and it's easy to fall into several common pitfalls. Here, we’ll explore five of the most frequent mistakes that can lead to problems in Data Vault projects and provide insights into how to avoid them.

Whether you are an experienced data professional or completely new to the industry, understanding Data Vault and its core concepts could be really helpful to your organisation. In this article, we will explore the core principles of Data Vault, exploring 13 terms that every data professional needs to know.

Staying ahead of your competition is not just an advantage—it's a necessity. For Business Intelligence managers, data engineers, and IT executives, understanding and adopting Data Vault can be a crucial part of doing that.

In the world of data and business, your choice of a data warehousing methodology can significantly impact your organisation’s ability to harness data for strategic decision-making.

In the constantly evolving landscape of data management, Data Vault has emerged as the platform of choice for large corporations worldwide. However, its impact extends far beyond IT departments.

Testing is vital to any successful Data Vault 2.0 project. It helps you build robust and trustworthy data solutions. In a recent Data Vault User Group meet-up, our Data Engineer, Chris Fisher, spoke on the role of testing in a Data Vault project.

Data warehousing has long been the backbone of business intelligence and analytics. Traditional approaches like the Inmon and Kimball methods have served organisations well in building centralised data repositories.

In the insurance industry, data management poses significant challenges that can hinder efficiency and growth.

We recently rebranded our popular Data Vault automation tool from dbtvault to AutomateDV. The open-source tool provides an opportunity for anyone to build a Data Vault.

Understanding the journey of your organisation's data is essential to making informed decisions. This process is known as data lineage.

Data Vault 2.0 is a method and architecture for delivering data analytics services to an enterprise supporting its business intelligence