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 this blog, we will delve into data contracts, focusing on the implementation strategies, best practices, and their crucial role in navigating a highly distributed and agile data ecosystem, specifically Data Vault.

What are the “hard problems” that your team is dealing with? First, you may ask “what are hard problems?”. Scott Ambler – agile data coach and consulting methodologist – outlined exactly what he means.

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.

It’s easy to assume that “bigger is better” when it comes to the size of your data team. With everything that comes with being part of a data team, many people assume that a small team struggles to handle it all.

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