To be upfront, I wasn’t expecting to write this chapter. When I first started working on this book a couple years ago, the discussion of meaning and semantics in data modeling was very sparse. But with the entire world’s focus on AI right now, there is fresh attention on these areas. A discussion of data modeling in this new context (no pun intended) is incomplete without covering meaning, semantics, and knowledge.
I can’t say that I’ll make everyone happy with this chapter on Meaning as a building block of data modeling. But this topic requires inclusion because meaning and semantics are more important than ever in a world increasingly dominated by large language models and generativeAI. Again, we’re modeling data for humans and machines.
In other news, the manuscript is nearly done (barring this and another short chapter or three) and I’ve starting the editing process for Book 1, the Foundations of Data Modeling. Older draft chapters will see updates, and sometimes major revisions. I’m targeting a late January 2026 publication date, so stay tuned for pre-order details.
Thanks,
Joe
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What exactly do we mean by “meaning”? This is a question that still comes up, and since this is not a philosophy book and we’re trying to keep things practical, I’ll give a caveman definition of meaning. Meaning is what something is intended to signify or express.
There’s meaning to you. Then there’s meaning to me. Where our individual meanings overlap, we create shared meaning, and that’s the space where data modeling lives.
Classically, data modelers like to point out that very few companies have a single definition for concepts like “Customer.” There might be several varieties of a Customer - when did they last order? How much did they order? What level of time and quantity counts as a Customer? And by the way, what is an Order? If the Customer requested a free sample 10 years ago, is that person a Customer, and was that actually an Order? This is where the hard work of data modeling starts. This is the fun and hairy part of data modeling. Meaning can vary by context.
This ambiguity of meaning in a business context stands in stark contrast to other fields that demand objective, universal truths. In some fields, meaning is (usually) concrete and unambiguous.
My background is in mathematics, and I was subjected to endless lemmas and theorems that I had to prove both forward and backward. Simple things we take for granted, like addition, have rigorous mathematical proofs. Addition is unambiguous. Then there’s the scientific method, which is an agreed-upon practice to test and validate our hypothesis. If enough people test out a hypothesis and reach the same conclusion, that hypothesis is accepted as “fact.” Of course, science always leaves the door open for something to be invalidated. In mathematics, the statement 2+2=4 is an objective truth that doesn’t change based on context or opinion. In chemistry, the molecular structure of water is always H2O. And in my personal life, as an avid rock climber, despite some people arguing otherwise, I can assure you that gravity is a very real thing. Hence, I always tie into a rope or fall onto a crash pad.
I could go on, but my point is that meaning is mostly subjective, sometimes objective. It all depends on the context and the thing to which you’re ascribing meaning. Because data models are seldom used by only one person, but instead a group of people, your job as a data modeler is to capture and represent the shared meaning of what you’re trying to convey in data. This shared meaning creates a shared understanding of concepts, vocabulary, and so on in the organization. It also facilitates a shared understanding between organizations, particularly in the context of standards.
I know philosophers will probably shoot down what I’ve written. That’s ok, since this isn’t a section on philosophy. The main takeaway is that your job is to capture this messy, contextual, but vital meaning in a way that it can be collectively agreed upon and understood. This is the essence of data modeling, whether for humans or machines.
Let’s transition our brief discussion of meaning to semantics, which is how we attempt to capture meaning in language.
(To be continued…)
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