For many data professionals, the hardest part of data modeling is not the technical work. It’s getting started . You sit down with a blank sheet of paper and a request to “produce a data model.” At that moment, the challenge is figuring out where to begin. In a recent Business Thinking Podcast discussion, we explored a practical approach to starting a data model from scratch—especially when working with Data Vault . The goal is not to produce a perfect model immediately. The

Organizations often hear about Data Vault  as a modern approach to building data warehouses. But adopting it at the wrong time can lead to disappointing results. The real question is not whether Data Vault is good or bad. The question is whether the organization is ready for it . In this article, we look at the conditions that need to be in place before a Data Vault initiative can succeed. These insights come directly from a discussion on the Business Thinking Podcast about w

Modern organizations are investing heavily in data platforms. One approach that often appears in these conversations is Data Vault . Data Vault has gained strong adoption across enterprises because it helps organizations integrate large volumes of data from multiple systems while maintaining historical accuracy and auditability. However, like any architectural method, Data Vault is not always the right solution . Understanding when it works—and when it doesn’t—is critical for

You have just completed an acquisition. Due diligence is finished. You tested the numbers, challenged assumptions, and committed capital based on a clear investment thesis. Your first actions are structural. You reshape parts of the organization. You align resources to strategic priorities. You appoint key managers. These steps reduce cost, clarify accountability, and inject new energy into the business. They stabilize the platform. But they do not yet build value.  The next

Many organizations are investing heavily in data platforms, analytics tools, and AI initiatives. Yet one foundational discipline often gets overlooked: data modeling . In recent years, some teams have questioned whether data modeling is still necessary. Modern data stacks emphasize rapid development, code-first workflows, and large-scale processing frameworks. As a result, design practices like conceptual and logical modeling sometimes get pushed aside. That shift creates ris

The recent announcement of the merger between dbt  and Fivetran  has been one of the hottest topics in the data community. Both Joe and Alex from the Business Thinking podcast recently discussed what this could mean for companies using these tools, and the implications for the wider data ecosystem. Two Big Players, One Company Fivetran and dbt are two major players in data engineering. While they are not renaming or fully combining into a single product, they will now oper

Choosing the right data architecture is crucial for handling the complexities of modern data ecosystems. From traditional warehouses to real-time streaming and decentralized models, each approach has unique strengths. In this blog, we compare key architectures based on criteria such as design principles, scalability, agility, auditability, and programming languages. 1. Traditional Data Warehousing Architectures Operational Data Store (ODS) Date:  Introduced in the 1990s Key P

Based on episode 18 of The Business Thinking Podcast with our CEO, Neil Strange, and AutomateDV Product Manager, Alex Higgs. Data literacy is a critical factor in becoming a truly data-driven organization. In a recent podcast episode, Neil Strange and Alex Higgs dive deep into the importance of data literacy, how it impacts decision-making, and how to cultivate it across your entire organization. Here's a breakdown of their insights on fostering data literacy in today’s fas

Based on episode 17 of The Business Thinking Podcast with our CEO, Neil Strange, and AutomateDV Product Manager, Alex Higgs. In a recent podcast episode, Neil Strange and Alex Higgs delved into the crucial topic of defining success in data engineering and data warehousing projects. This discussion goes beyond simply avoiding failure; it's about proactively establishing the conditions for a successful outcome. From Failure to Success Building upon our previous conversation a

Based on episode 15 of The Business Thinking Podcast with our CEO, Neil Strange, and AutomateDV Product Manager, Alex Higgs. Despite decades of experience, the data warehousing industry continues to grapple with project failures. In this episode, Neil Strange and Alex Higgs delve into the common themes contributing to these failures and offer insights on how to steer projects towards success. Defining Failure A failed project isn't merely one that encounters technical glitc

Based on episode 14 of The Business Thinking Podcast with our CEO, Neil Strange, and AutomateDV Product Manager, Alex Higgs. In a recent podcast episode, we tackled a persistent an interesting question: what is the ideal team size for a data warehousing project?  We’ve observed a staggering disparity in team sizes across various projects, ranging from a lean two or three individuals to sprawling teams of 50 or 60. This begs the question: where does the sweet spot lie, and wh

Are you working in data engineering, business intelligence, or analytics and want to connect, learn, and grow your impact? Then save the date for the Data Community Event 2026  on Monday, 26 January 2026 at the Gtech Community Stadium, London. This is the flagship in‑person event of the re‑branded community formerly known as the Data Vault User Group . We’re broadening the scope beyond the Data Vault method: more topics, more voices, and more connections. The theme for the da