Before investing in AI, every organisation needs an honest answer to one question: is your data good enough? The 8 C's Data Quality Framework - Certainty, Coverage, Completeness, Consistency, Currency, Commonality, Chance, and Consent - gives teams a...

Despite claims to the contrary, dimensional modelling and star schemas are alive and well the in the modern data world. But whilst developers might have great technical skills and understand how to build a star schema, they may lack the business doma...

Most organisations aren't running on a greenfield modern data stack — they're running on legacy on-prem data systems, years of accumulated business logic, and pipelines that quietly power everything. The prevailing narrative says AI belongs to teams ...

If you're a data engineer, data platform engineer, or data architect, you know how difficult it is to build a truly self-serve data platform. In fact, data platforms are often organisational bottlenecks, preventing data publication, consumption, and ...

Neil Strange is the Chairman of the Data Vault User Group and a seasoned data modelling expert. With decades of experience and training under industry pioneers, Neil has a deep understanding of the intricacies of data modelling. He is passionate abou...

Here's an uncomfortable truth: the data team isn't coming to save you.

While requests sit in backlogs and transformation programmes promise future value, people across the business are already solving problems with the tools they have. They always h...

Ever struggled with lengthy, boring and confusing data requirement workshops that leave both your Stakeholders and your Data Team uncertain about what is needed and the next steps?

Imagine a Pattern Storming Workshop and Pattern Template that help g...

Data Mesh promises a bold shift: domain-driven ownership, decentralised architecture, and data-as-a-product thinking. But between the whitepapers and the whiteboards lies a messier, more nuanced reality.

In this talk, we explore the lived experience...

Will be updated in due course

Will be updated in due course

Creating a greenfield data platform using AWS, Snowflake, and dbt is often framed as a technical exercise: pick the right tools, design the right architecture, and the rest follows. In practice, the hardest problems weren't in the pipelines at all.
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