{"id":1276,"date":"2025-12-26T14:55:25","date_gmt":"2025-12-26T19:55:25","guid":{"rendered":"https:\/\/blog.data-principles.com\/?p=1276"},"modified":"2026-01-06T17:45:41","modified_gmt":"2026-01-06T22:45:41","slug":"enabling-high-quality-analytics-through-a-data-validity-dimension","status":"publish","type":"post","link":"https:\/\/blog.data-principles.com\/index.php\/2025\/12\/26\/enabling-high-quality-analytics-through-a-data-validity-dimension\/","title":{"rendered":"Enabling High Quality Analytics through a Data Validity Dimension"},"content":{"rendered":"\n<p class=\"has-orange-color has-text-color has-link-color wp-elements-e358becb889ef645eca72e62b736ffc4\"><em>By Pete Stiglich<\/em><\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-291a9911f98b43ada261c1a80d93697a\">Explains a practical dimensional-modeling pattern for handling imperfect source data without destroying analytical trust. Instead of rejecting records with invalid dates or other validity issues, the approach creates a \u2018Data Validity\u2019 dimension with a surrogate key and multiple Y\/N flags describing different validity exceptions. Facts reference this dimension so analysts can filter invalid cases, compare results with\/without errors, and quantify issues for upstream correction. The article also shows how to populate the dimension via a controlled Cartesian-product technique and how the pattern can reduce skew and improve confidence.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-orange-background-color has-background wp-element-button\" href=\"https:\/\/www.ewsolutions.com\/enabling-high-quality-analytics-data-validity-dimension\/\">Read More<\/a><\/div>\n<\/div>\n\n\n\n<p class=\"has-grey-color has-text-color has-link-color wp-elements-215e483ed6eace4cc4c76b0916340335\"><em><strong>Disclaimer<\/strong><\/em> <\/p>\n\n\n\n<p><em>Links to third-party articles and resources are provided for informational purposes only. Data Principles, LLC does not claim ownership of, nor imply endorsement by, the referenced organizations.<\/em><\/p>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:30% auto\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"744\" height=\"746\" src=\"https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-3.39.28-PM.png\" alt=\"\" class=\"wp-image-886 size-full\" srcset=\"https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-3.39.28-PM.png 744w, https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-3.39.28-PM-300x300.png 300w, https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-3.39.28-PM-150x150.png 150w\" sizes=\"auto, (max-width: 744px) 100vw, 744px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p class=\"has-orange-color has-text-color has-link-color has-regular-font-size wp-elements-d940b883627d329ff5b661894ecc7ffc\" style=\"text-transform:capitalize\"><strong>Pete Stiglich: Trusted Expert in Data Architecture &amp; Modeling<\/strong><\/p>\n\n\n\n<p class=\"has-text-align-left has-black-color has-text-color has-link-color has-regular-font-size wp-elements-c634ae76efbe08db80e2f28f390bd565\">Pete has over 30 years of data architecture, data management, and analytics experience, most of that time as a consultant in industries such as government, finance, healthcare, insurance, and more.&nbsp;He is an industry thought leader in data architecture and data modeling and has developed and taught many courses on these topics. Pete enjoys helping clients solve complex data problems, leveraging proven approaches such as \u201cModeling the business before modeling the solution\u201d which provides a benefit to clients that many IT professionals miss.<\/p>\n<\/div><\/div>\n\n\n\n<ul class=\"wp-block-social-links is-horizontal is-content-justification-left is-layout-flex wp-container-core-social-links-is-layout-7e5fce0a wp-block-social-links-is-layout-flex\"><li class=\"wp-social-link wp-social-link-linkedin  wp-block-social-link\"><a rel=\"noopener nofollow\" target=\"_blank\" href=\"https:\/\/www.linkedin.com\/in\/petestiglich\/\" class=\"wp-block-social-link-anchor\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M19.7,3H4.3C3.582,3,3,3.582,3,4.3v15.4C3,20.418,3.582,21,4.3,21h15.4c0.718,0,1.3-0.582,1.3-1.3V4.3 C21,3.582,20.418,3,19.7,3z M8.339,18.338H5.667v-8.59h2.672V18.338z M7.004,8.574c-0.857,0-1.549-0.694-1.549-1.548 c0-0.855,0.691-1.548,1.549-1.548c0.854,0,1.547,0.694,1.547,1.548C8.551,7.881,7.858,8.574,7.004,8.574z M18.339,18.338h-2.669 v-4.177c0-0.996-0.017-2.278-1.387-2.278c-1.389,0-1.601,1.086-1.601,2.206v4.249h-2.667v-8.59h2.559v1.174h0.037 c0.356-0.675,1.227-1.387,2.526-1.387c2.703,0,3.203,1.779,3.203,4.092V18.338z\"><\/path><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">LinkedIn<\/span><\/a><\/li>\n\n<li class=\"wp-social-link wp-social-link-mail  wp-block-social-link\"><a rel=\"noopener nofollow\" target=\"_blank\" href=\"mailto:&#112;&#115;&#116;i&#103;li&#099;&#104;&#064;da&#116;&#097;-&#112;&#114;i&#110;&#099;&#105;&#112;les&#046;c&#111;&#109;\" class=\"wp-block-social-link-anchor\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M19,5H5c-1.1,0-2,.9-2,2v10c0,1.1.9,2,2,2h14c1.1,0,2-.9,2-2V7c0-1.1-.9-2-2-2zm.5,12c0,.3-.2.5-.5.5H5c-.3,0-.5-.2-.5-.5V9.8l7.5,5.6,7.5-5.6V17zm0-9.1L12,13.6,4.5,7.9V7c0-.3.2-.5.5-.5h14c.3,0,.5.2.5.5v.9z\"><\/path><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">Mail<\/span><\/a><\/li><\/ul>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-text-align-center has-blue-color has-text-color has-link-color wp-elements-dc4441a923f3306c286cd692d7ae69ed\" style=\"font-size:26px\"><strong><em>Join Our Data Community<\/em><\/strong><\/p>\n\n\n\n<p class=\"has-text-align-center has-black-color has-text-color has-link-color wp-elements-9bdac29360d2b62aa9e765a3bc163366\">At Data Principles, we believe in making data powerful and accessible. Get monthly insights, practical advice, and company updates delivered straight to your inbox. Subscribe and be part of the journey!<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-orange-background-color has-background wp-element-button\" href=\"https:\/\/lp.constantcontactpages.com\/sl\/XIYDUv9\/DataDecisionsPathways\">Subscribe  Now<\/a><\/div>\n<\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"946\" height=\"630\" src=\"https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-6.34.01-PM.png\" alt=\"\" class=\"wp-image-1087\" style=\"width:450px\" srcset=\"https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-6.34.01-PM.png 946w, https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-6.34.01-PM-300x200.png 300w, https:\/\/blog.data-principles.com\/wp-content\/uploads\/2025\/12\/Screenshot-2025-06-02-at-6.34.01-PM-768x511.png 768w\" sizes=\"auto, (max-width: 946px) 100vw, 946px\" \/><\/figure><\/div>","protected":false},"excerpt":{"rendered":"<p>By Pete Stiglich Explains a practical dimensional-modeling pattern for handling imperfect source data without destroying analytical trust. Instead of rejecting records with invalid dates or&hellip;<\/p>\n","protected":false},"author":5,"featured_media":1277,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24,258,14],"tags":[172,225,99,89,119,223,87,195,224,193],"class_list":["post-1276","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-data-modeling","category-data-quality","tag-analytics-2","tag-business-analytics","tag-data-governance","tag-data-quality","tag-data-strategy","tag-data-validity","tag-enterprise-data","tag-modern-data-ops","tag-reliablei-nsights","tag-semantic-clarity"],"_links":{"self":[{"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/posts\/1276","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/comments?post=1276"}],"version-history":[{"count":1,"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/posts\/1276\/revisions"}],"predecessor-version":[{"id":1278,"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/posts\/1276\/revisions\/1278"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/media\/1277"}],"wp:attachment":[{"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/media?parent=1276"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/categories?post=1276"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.data-principles.com\/index.php\/wp-json\/wp\/v2\/tags?post=1276"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}