Based on episode 10 of The Business Thinking Podcast with our CEO, Neil Strange, and AutomateDV Product Manager, Alex Higgs. Non-Historized Links, formerly known as transactional links, are a unique and powerful variant of Links within the Data Vault 2.0 architecture. This article explores the intricacies of Non-Historized Links, their applications, and best practices for implementation, drawing from our recent podcast discussion. What Are Non-Historized Links? Non-Historiz

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.

Successful businesses are always looking for ways to streamline and optimize their data workflows. The combination of AutomateDV, Data Vault, and dbt Cloud offers a powerful solution for data teams aiming to build a robust Data Mesh.

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.

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.

What is the purpose of our recently published e-book the ‘extended Datavault framework’? Simply, it is designed to help ensure your data warehouse and analytics project are successful.

Data Vault 2.0 is increasingly being recognised globally as the leading approach for agile Data Warehouse projects.

Data Vault 2.0 is gaining traction as a method for the design and build of Data Warehouses but, as when you try anything for the first time there are some potential pitfalls. We have seen some issues our clients have faced, and we thought we would share three common issues and some tips for avoiding them.

Single Customer View projects traditionally seek to create a single source of truth about customers and their touch points with an organisation. The project attempts to line up data from all the different sources in the load process assuming that customer data will form one tightly integrated result.  It all sounds straightforward – surely customer data is the same whatever the source?

In an age of Big Data and Artificial Intelligence profiling using personal data is inevitable. Profiling is automated decision making based upon the data you hold about an individual. The accessibility of the data and ease of processing mean that more and more organisations will start using profiling.