Tags are often dismissed as technical conveniences — tiny labels stuck to columns or datasets like digital post-it notes. But to dismiss them is to miss out on their true purpose: flexibility.
In essence, a tag is a name - a declaration of identity or purpose. To tag something is to say: this matters in this way. It’s a semantic fingerprint, applied to a data object in a way that transforms ambiguity into meaning, and meaning into action.
Much like how language gives us categories for thought, tags give data structure for use.
In a data context, tags are lightweight labels applied to objects in a data estate – like data products, tables, columns, dashboards, jobs, or infrastructure.
They come in two varieties:
Unlike strict schemas, tags are fast to apply, easy to ready, and adaptable. They don’t just describe data, they help organize, govern, and automate it.
In a modern data platform, tags serve as the connective tissue, bridging data discovery, governance, quality, automation, and value attribution. Tags help us:
Without tags, datasets blur together into undifferentiated sprawl. Tags make visible what is valuable, sensitive, ready, or raw.
In a world of rising regulatory and ethical expectations, a tag like pii is not mere decoration it is a boundary in metadata that systems and stewards must not cross.
Tags give systems something to act upon. If data is tagged deprecated, a pipeline can skip it. If it’s gold, it can be promoted. If it's experimental, it can be flagged for review. The tag becomes an instruction to the machine.
Here’s where philosophy meets practice. A good tag, like a good concept, should be:
Tags are metadata about your data, but we often forget - tags themselves have metadata. If you don't track where a tag came from, who owns it, and how it's supposed to behave, you risk chaos.
We must ask:
This is where we move from tagging to meta-tagging — from the act of naming to the governance of names.
Each tag should carry:
This may appear like overhead, but it's not - it is ontological hygiene. It ensures that tags do not become corrupted abstractions but remain faithful representations of truth in context.
To tag well is to impose just enough structure to liberate potential without choking possibility.
As data ecosystems grow more autonomous (with data products, self-service platforms, and AI-driven pipelines) tags become the language of decision-making. They are the way we imbue digital systems with ethics, context, and memory.
If we get tagging wrong, we lose trust, transparency, and control.
If we get tagging right, we gain insight, action, and meaning. So:
And never forget: to tag something well is to understand it with precision and care. In the end, every good tag is a question answered; and every bad tag is a question deferred.
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This post covers the why, but the how is just as critical. Contact us to understand how we can help create a tagging system that drives clarity, governance, and automation.
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