Foundations.
Seven pages, read in sequence. About forty-five minutes. No prior knowledge assumed.
This is the entry module of the CleanDims learning curriculum. It is written for the learner who has not encountered dimensional data as a topic before and wants the full picture from first principles, and for the experienced practitioner who wants to see whether the framing the curriculum uses lines up with the framing they have built up over years of work.
The seven pages build on each other. The first establishes what a dimension is. The second introduces the five forms variance takes. The third describes where dimensions come from and where they break. The fourth examines what dimensional inconsistency costs. The fifth explains why the problem stays unmanaged for as long as it does. The sixth describes what is changing as agents become significant producers of dimensional values. The seventh closes with what well-managed dimensional data looks like in practice.
Each page can be read on its own, but the sequence is the recommended path. Each ends with a “next” link to the page that follows.
The seven pages.
- 1
What is a dimension
The setup: measures, dimensions, and why dimensions behave differently from numbers.
~ 5 min - 2
The five ways dimensions go wrong
Surface, semantic, definitional, granularity, and temporal variance, each named and described.
~ 7 min - 3
Where dimensions come from and where they break
The journey from data entry to analytical consumption, and the points along the way where things go wrong.
~ 7 min - 4
What dimensional inconsistency costs
The downstream consequences, from wasted analyst time to erosion of trust in data.
~ 6 min - 5
Why dimensions stay unmanaged
The structural reasons the problem persists despite being widely recognised.
~ 6 min - 6
What is changing
The agent-era shift and why reactive management is reaching its limit.
~ 5 min - 7
What good looks like
The constructive close: the elements of well-managed dimensional data.
~ 5 min