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I've tagged this with {{expert}} because the present article is basically incomprehensible. Given a data set, what is an "item" in that data set? A variable? An observation? How does the example relate to the given definition?  --Lambiam^Talk 04:18, 20 February 2007 (UTC)[reply]

I have to disagree. There are already too many articles on the internet which lump dimension with hierarchy and then elaborate on data warehouses. This article is short and precise, defining exactly what a dimension is and giving important attributes. Short articles are often what a person is searching for. To bury this basic information in a larger context makes it difficult for the person who wants a clear explanation of a data dimension without having to sort through a long article just find this simple information. I vote to keep the article "as is" and not combine it. Marti Masters, Software Engineer and Data Analyst. — Preceding unsigned comment added by 84.24.63.85 (talk) 15:36, 30 December 2014 (UTC)[reply]

No, I agree an expert is needed in order to write a starting definition that is more comprehensible to a lay audience. Later on in the article is a slightly more understandable definition. This should be taken as the starting point and improved to then be used as the articles starting introduction:

• dimension tables contain descriptive attributes (or fields) that are typically textual fields

• Dimension table rows are uniquely identified by a single key field.

The current starting definition is a dimension " categorizes facts", but 'categorize' is vague and could have many meanings. Something more concrete and understandable is needed. While it's always a challenge, it's good to strive for a introductory definition that someone outside the field can read an get the gist of.

If the example give in the wiki page 'https://en.wikipedia.org/wiki/Online_analytical_processing' is correct, it would be very helpful to include here. The example is:

A simple example is given by the time table shown below. The store's sales table is a fact table, and the Date/Time table is a dimension table.

For example:

 Sales Fact Table
+-------------+----------+
| sale_amount | time_id  |
+-------------+----------+            Time Dimension
|      2008.10|     1234 |----+     +---------+-------------------+
+-------------+----------+    |     | time_id | timestamp         |
                              |     +---------+-------------------+
                              +---->|   1234  | 20080902 12:35:43 |
                                    +---------+-------------------+

 — Preceding unsigned comment added by 206.218.52.35 (talk) 17:50, 10 April 2019 (UTC)[reply]