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Ideas for Modern Data Teams
This blog is where we share what's working (and what's not) across modern data architecture, data engineering, and business intelligence.
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Practical tips and examples from real-world projects
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Tools we actually use
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Opinions and insights from our team
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Updates from our products and community
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Data Contracts: The Discipline That Stops Bad Data Before It Becomes Your Problem
Every organization that moves data between systems has experienced the same failure: something breaks downstream, and by the time anyone notices, the problem has already propagated through multiple processes. You trace it back and find that a source system changed its structure without warning — a column removed, a format altered, a field shifted — and your pipeline absorbed that change silently until the damage was done. Data contracts exist to prevent exactly that, and they
Jul 94 min read
XML in Your Data Warehouse: What to Expect and How to Handle It
At some point in your data platform journey, you will encounter XML. It may not be your team's first choice, and it almost certainly will not be your engineers' preferred format to work with. But it exists in legacy source systems across industries, and for many organizations it arrives as a non-negotiable part of the data landscape. The question is not whether to deal with it — it is how to deal with it well, and how to recognize when the real problem is not the format at al
Jul 75 min read
Getting Data Into Your Warehouse Is Harder Than It Looks. Here's How to Do It Right
Most conversations about data warehousing focus on what happens once the data is inside the platform: transformation, modeling, reporting, AI readiness. What gets far less attention is the step that makes all of that possible in the first place — getting data from your source systems into the warehouse at all. That process, which practitioners call data landing, is where many organizations quietly struggle. The technical complexity is real, but so is the organizational comple
Jul 25 min read
Why Your Data Platform Needs a Strategy for JSON
Semi-structured data is no longer a niche engineering concern. It is arriving at the heart of your data platform every day, and the decisions your teams make about how to handle it will determine how agile, resilient, and trustworthy your data infrastructure becomes over the next few years. The rise of JSON — the most common format for this kind of data — is not a coincidence. Two structural shifts in modern software have driven it. First, organisations have moved to API-firs
Jun 305 min read
How To Start A Data Model
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
Mar 264 min read
When Is The Right Time To Use Data Vault?
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
Mar 244 min read
When Is Data Vault Not The Right Option?
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
Mar 195 min read
You bought the business. Now fix the numbers
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
Mar 165 min read
Is Data Modeling Still Relevant?
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
Mar 164 min read
Fivetran and dbt Merge: What It Means for Data Teams
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
Dec 4, 20253 min read
Comparing Data Architectures: From Traditional Warehouses to Modern Data Platforms
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
Dec 2, 20253 min read
What Does It Mean To Be Data-Driven?
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
Nov 27, 20254 min read
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