{"id":1436,"date":"2010-07-27T01:00:00","date_gmt":"2010-07-27T09:00:00","guid":{"rendered":"https:\/\/systematichr.com\/?p=1436"},"modified":"2010-06-14T18:24:38","modified_gmt":"2010-06-15T02:24:38","slug":"explaining-facts-and-dimensions","status":"publish","type":"post","link":"https:\/\/systematichr.com\/?p=1436","title":{"rendered":"Explaining Facts and Dimensions"},"content":{"rendered":"

Fact:\u00a0 A turnover rate based on a calculation<\/p>\n

Dimension:\u00a0 Historical time trend of turnover<\/p>\n

Fact:\u00a0 The number of heads in your HR database<\/p>\n

Dimension:\u00a0 The breakdown of headcount by organization<\/p>\n

Fact:\u00a0 The number of FTE\u2019s in any given job code<\/p>\n

Dimension:\u00a0 The demographic analysis of FTE\u2019s in job code<\/p><\/blockquote>\n

\"\"<\/a>I honestly have no idea how many of you have ever heard of a \u201cstar schema.\u201d\u00a0 I\u2019m not sure I ever wanted to, and I\u2019m not sure knowing what it is called has ever really helped me out.\u00a0 But somehow, I picked it up along the way (not surprising for a data warehouse and analytics guy) and every once in a while I am reminded how difficult it is to explain analytical reporting to HR people who don\u2019t already have it.\u00a0 And the simple fact is that most of us don\u2019t really have it yet, or only have small bits in a couple of our applications.<\/p>\n

Facts are all those things that we can quantify.\u00a0 They are\u2026 well, facts.\u00a0 They are the things we run ad hoc or operational reports against, and are usually the things that we have reported against for years.\u00a0 They are those turnover reports, or the headcount reports.<\/p>\n

Dimensions on the other hand, are the attributes we want to dynamically apply to the facts.\u00a0 Lets say I get a turnover report.\u00a0 I\u2019d love to right click on one of the bars in the bar chart and see a historical trend for turnover.\u00a0 So in this case, the dimension is time.\u00a0 Or that same right click might have given me the option to see the turnover by business unit, so now the dimension is organization.\u00a0 Perhaps we want turnover by age or gender instead \u2013 so now it\u2019s a demographic parameter.<\/p>\n

Sometimes, a data element can be a fact in one report and a dimension in another.\u00a0 So age, a dimension in our turnover report, can also be a fact.\u00a0 We run a report on the headcount by age groupings.\u00a0 However, when we see that report, we decide that we want to know the race allocation within our 30-39 band, so now age is the fact and race is the dimension.<\/p>\n

I have no idea if all that makes sense in text (and without actually seeing it), and I\u2019m really avoiding trying to explain a \u2018cube\u201d which I\u2019m fairly sure I\u2019ve tried to do before.\u00a0 But there\u2019s a reason we talk about this stuff.\u00a0 When you set up analytics, you set up these star schema first.\u00a0 After you drop the data into your data warehouse, you get to create these \u201cschema\u201d which form the basis of your analytics.\u00a0 If you don\u2019t know beforehand how you might want to see your data, you\u2019re going to wind up with a very limited set of dimensions and 2 months down the road, you\u2019ll be wondering why your implementer didn\u2019t ask you if you wanted turnover by age, race and gender.\u00a0 Implementers implement what you tell them to.\u00a0 It\u2019s up to you to understand what your own requirements are.\u00a0 The problem with data warehouse, is that understanding what the requirements are only happens when you understand the capabilities of the technology.<\/p>\n","protected":false},"excerpt":{"rendered":"

Fact:\u00a0 A turnover rate based on a calculation Dimension:\u00a0 Historical time trend of turnover Fact:\u00a0 The number of heads in your HR database Dimension:\u00a0 The breakdown of headcount by organization Fact:\u00a0 The number of FTE\u2019s in any given job code…<\/p>\n","protected":false},"author":1,"featured_media":1456,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[27,2,34],"tags":[62,186,185,189,188,187],"class_list":["post-1436","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-metrics","category-hr-technology","category-implementation","tag-analytics","tag-data-mart","tag-data-warehouse","tag-dimensions","tag-facts","tag-star-schema"],"_links":{"self":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1436","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1436"}],"version-history":[{"count":8,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1436\/revisions"}],"predecessor-version":[{"id":1437,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1436\/revisions\/1437"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/media\/1456"}],"wp:attachment":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1436"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1436"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1436"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}