The Data Journey

TOGAF isn’t just about IT architecture, it’s a method for aligning data, applications, technology, and business strategy into one coherent enterprise model. In this episode, we unpack where data architecture fits within TOGAF, how the ADM cycle guides it, and what it means for designing sustainable data strategies.

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If you think of data management as a city, then architecture is the city plan*. Without it, you just get roads built randomly, buildings popping up everywhere, and eventually, traffic jams and chaos. Let’s explore how DMBOK frames data architecture as both a discipline and a foundation.

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In earlier episodes, we’ve compared Data Lake vs. Data Lakehouse vs. Data Warehouse in Episode 9, and explored the Medallion Architecture in Episode 13. Those gave us the big-picture landscape of where data can live and how it flows.

Today, we’re going to narrow the focus and talk about a choice that shaped enterprise data for decades and still influences decisions today: Inmon vs. Kimball. Let's unpack.

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Observability is like having a health monitor for your data ecosystem. It doesn’t just tell you when something breaks, it helps you understand why, and how to fix it. Let’s dive in.

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What if you could access and join data from multiple systems without physically moving it? That’s the promise of data virtualisation. Let’s explore what it is, how it works, where it shines, and where you still need to be cautious.

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Not every problem needs real-time data. Sometimes batch is cheaper and more reliable. Other times, streaming or event-driven architectures are essential to compete. The trick is knowing when to use which and how to combine them. Lets Unpack.

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How you move and transform your data is just as important as where you store it. Get this choice wrong, and you can slow your pipelines, inflate costs, or compromise trust. So let’s break down the differences, when to use each, and how to decide.

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Not all data deserves to live forever. Holding onto everything leads to spiralling storage costs, compliance risks, and a cluttered ecosystem. The real discipline is knowing when to archive for reference and when to dispose responsibly. Lets unpack.

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Data only creates value when it’s used — but the way we share it determines whether it drives trust or chaos. If data is used without context, governance, or responsibility, it creates risk. If it’s shared with discipline and transparency, it becomes a true asset. Let’s unpack what that means.

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The way you store data determines not just cost and performance, but also trust, governance, and the ability to innovate. And it’s not just about data warehouses, lakes, and lakehouses. We need to zoom out and look at the full spectrum of storage options, from classic relational databases to NoSQL, time-series, and beyond. Let’s explore.

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