As a Trainer, I am always looking for ways to make learning more interesting. I look for analogies that I can turn into stories. Telling a story with a message, helps students remember the lesson and be able to apply the learning. That is my mission!
Walking the streets of Edinburgh: Where Data Foundations makes sense
My most recent data parallel comes from a walk through my hometown of Edinburgh, Scotland. I was wondering why I find inspiration when I am exploring, and I believe it’s because my mind is opened to seeing things just a little differently.
From the narrow, dark, winding closes and wynds of the ‘Old Town’ to the precision and symmetry of the ‘New Town’, Edinburgh shows how history and heritage, and deliberate design can and do work together.
Stand in the ‘Old Town’ and you feel the centuries around you. The closes twist, the stones are uneven, and there is always something more to discover. Walk a little further northward and you reach the ‘New Town’, with its symmetry, straight streets, clean lines and thoughtful design that reshaped the city.
Walking between the two parts of the city you can feel how the established history combines with the deliberate design that came after. One city, created over time, with different architectures that co-exist. The perfect metaphor for Data Foundations & AI.
AI brings new possibilities, but we still need to understand the data we are using, where it comes from, whether it is suitable, and how it should be handled. Organisations need strong Data Foundations: clear understandable data that everyone understands in a consistent manner, strong metadata and trusted lineage. These elements of Data Management have been around for a long time but AI gives us reason to think about these aspects with new designs in mind.
Old Town: understanding the data we already have
The ‘Old Town’ is like the raw data organisations have collected over decades; rich, textured, layers of information, created over years, which remain the basis for future constructs.
The care taken to preserve Edinburgh’s historic buildings ensured that ‘Old Town’ is still operational. But just like those winding streets, you can’t navigate confidently without understanding what you’re looking at. That’s where metadata comes in. Metadata is like the street signage that helps us find our way. A street name helps us identify a place and information about that place helps us understand it’s purpose. In our metaphor, it is what this dataset contains, who created it, what it’s terms mean, how it should be used.
And then there is governance, the rules and constraints establish controls to look after your data and ensure it is sustainable. Much like the centuries‑old building regulations that keep the ‘Old Town’ standing.
New Town: planning for what comes next
Cross into the ‘New Town’ and the experience changes. Streets run straight, buildings align, and sweeping curves reflect deliberate design. It is easier to see where you are, where you’re going, and how everything connects.
This is a reminder of how we should plan, organise and collect data, with thought, understanding how each new addition connects with what we already have.
We also need to consider the data lifecycle: when was it created, where is it stored, how is it used, when should it be archived or retired? Data lineage is the ability to trace where data comes from, how it moved, and how it has been transformed.
These questions apply to inherited data and newly created data. The ‘Old and the Newtown’ help me think about the different landscapes and how they developed, both needing understanding and care.
What does this mean in the age of AI?
AI can work with data at a scale and speed that is difficult for people and that makes it particularly important to know what data it is working with. This new use for data does not remove the need to understand it, it reinforces it. An unclear definition, an outdated record or a missing piece of context can affect the usefulness of the result.
Rabbie Burns once wrote, “Facts are chiels that winna ding”, facts are stubborn things. And in the age of AI, facts (our data) matter more than ever.
We may have plenty of data but we still need to establish what it tells us, and whether we can rely on it. This brings me to the questions people need to be able to answer about their data:
Being able to answer these questions will create the Data Foundation you need, as well as enable you to judge the AI output.
AI and Data Management need to be approached in the same manner in which Edinburgh developed. Edinburgh did not lose its history when a ‘New Town’ was built. The different parts of the city remain connected, contributing to the place we know today.
This walk was a reminder that helping people understand their data, remains a good place to start.
The short takeaway:
Strong Data Foundations aren’t optional for AI; they are the starting point.
Sometimes the most powerful insights come from walking familiar streets with fresh eyes.
Just as Edinburgh thrives by honouring its past while building new with precision; organisations will succeed with AI when they blend established data heritage with new capabilities.
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