{"id":22781,"date":"2025-09-02T12:18:41","date_gmt":"2025-09-02T12:18:41","guid":{"rendered":"https:\/\/www.cleverrepublic.com\/?post_type=blog&p=22781"},"modified":"2025-10-02T12:35:59","modified_gmt":"2025-10-02T12:35:59","slug":"why-you-should-embed-data-quality-throughout-the-data-pipeline","status":"publish","type":"blog","link":"https:\/\/www.cleverrepublic.com\/nl\/resources\/blog\/why-you-should-embed-data-quality-throughout-the-data-pipeline\/","title":{"rendered":"Waarom u Data Quality in de gehele datapijplijn moet verankeren."},"content":{"rendered":"\t\t
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When something goes wrong with a dashboard, report, or KPI, most people start at the end. They ask: was this data checked before publishing? Is the report broken? But these questions come too late. By the time data hits the end of the pipeline, many opportunities to catch issues have already passed.<\/span>\u00a0<\/span><\/p>

\u00a0<\/span><\/p>

In our previous blogs, we discussed <\/span>why measuring data quality is essential for developing <\/span>high quality Data Products<\/span><\/a>, and <\/span>how backwards thinking when developing Data Products<\/a> enables a quality mindset<\/span>. This blog builds on our previous blogs, diving deeper into embedding Data Quality throughout the data pipeline. Delays, errors, and inconsistencies are often symptoms of something deeper in the chain. And just like any well-run production process, you need to monitor quality as early and often as possible. One final inspection never catches everything.<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Three reasons for monitoring data quality throughout the pipeline <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Going from singular Data Quality checks to multiple checks throughout the data pipeline is both a technical choice and a cultural shift. It requires more collaboration but ultimately pays off. These are our main three reasons to adopt this approach:<\/span>\u00a0<\/span><\/p>

1: It raises awareness and makes Data Quality a team effort<\/span><\/b>\u00a0
<\/span>In modern organizations, data flows through complex data landscapes and passes through many hands (source system owners, engineers, analysts). If checks only happen at a singular point, people upstream assume quality is someone else\u2019s job. This creates blind spots and weak accountability.\u00a0<\/span>\u00a0
<\/span>Embedding DQ checks at multiple points in the pipeline shifts this mindset, as it brings visibility of potential issues to every team involved. It builds awareness, requiring people responsible for source data to be mindful of business needs.<\/span>\u00a0<\/span><\/p>

2: It helps trace issues in complex data landscapes<\/span><\/b>\u00a0
<\/span>Today\u2019s data pipelines span multiple systems, environments, and teams. Data flows from ERP to cloud warehouses, through transformation scripts and orchestration tools, into dashboards and APIs. But when something breaks, where do you look? A single DQ checkpoint cannot tell you where the issue began. Debugging becomes guesswork.<\/span>\u00a0
<\/span>When multiple DQ checks are embedded in the pipeline, it adds traceability. It becomes easier to pinpoint where a value changed, or a field became null. This makes root cause analysis of your issue much faster. Instead of treating symptoms, problems can be fixed at the source.<\/span>\u00a0<\/span><\/p>

3: It builds trust long before the product is delivered<\/span><\/b>\u00a0
<\/span>Trust in data can be lost quickly. Persisting data issues in dashboard will result in users losing confidence. Ultimately people will rely less on the dashboard, which is problematic as we want data to drive better business decision-making.<\/span>\u00a0
<\/span>By checking Data Quality early, most issues are caught before they reach end users. For example, freshness problems can be flagged right after ingestion and business rules can be validated before data hits reports. When end-users see fewer issues, it will build quiet confidence in data.\u00a0<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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How it looks in practice <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In our previous blog, we described the \u2018Pension Payout\u2019 Data Product developed at Groove, which calculates the monthly pension payments for all employees at Groove. In this simplified example the data moves from its source system, Snowflake, through a Databricks pipeline to be consolidated into a Data Product.\u00a0<\/span>\u00a0<\/span><\/p>

As the data moves from its source through the data pipeline, Groove has implemented Data Quality checks at every critical step using their Data Quality tool: Soda. We have visualized this below.<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Even in a simple data pipeline, quality checks are deployed at three different stages: pre-ingestion, post-ingestion, and post-transformation. At each stage there are slightly different checks. Some examples:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Post-ingestion check<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We will take the simple example of the employee date of birth as a data point. For our Data Product, it is important to have complete and accurate data on employee date of birth to calculate the year of retirement as well as payout eligibility. After the data comes into Databricks, we do the following post-ingestion check:<\/p>

Date of birth cannot be empty or null.<\/p>

Or, written in Soda Check Language:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
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\n\t\t\t\t\n\t\t\t\t\t-\tmissing_count(Date_Of_Birth) = 0:\r\n    \tname: \"Date of Birth should not be null\" \r\n    \tattributes: dimension: [Completeness] \r\n    \tpipeline_stage: Pre-transformation\r\n<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5f3c3f elementor-widget elementor-widget-text-editor\" data-id=\"a5f3c3f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This simple Completeness rule ensures we have no missing date of birth in our Data Product.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-00d2dd5 e-flex e-con-boxed e-con e-parent\" data-id=\"00d2dd5\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f67bf05 elementor-widget elementor-widget-heading\" data-id=\"f67bf05\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Post-transformation check<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62ab732 elementor-widget elementor-widget-text-editor\" data-id=\"62ab732\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>After transformation of the source tables, we need to check the Data Product for any other rule violations before we make it available for consumption. One of the business rules that the Data Product should comply to is the minimum age of 65 for pension payouts. We can check this easily with the following data quality rule:<\/p><p>Pension Age must be 65 or above.<\/p><p>This too can be written in Sode Check Language:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb67f6b elementor-widget elementor-widget-code-highlight\" data-id=\"cb67f6b\" data-element_type=\"widget\" data-widget_type=\"code-highlight.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"prismjs-default copy-to-clipboard \">\n\t\t\t<pre data-line=\"\" class=\"highlight-height language-javascript line-numbers\">\n\t\t\t\t<code readonly=\"true\" class=\"language-javascript\">\n\t\t\t\t\t<xmp>-\tfailed rows:\r\n    \tname: \"Pension age should be above 65\"\r\n\t    fail condition: Pension_Age < 65\r\n    \tattributes:\r\n\t    \tdimension: [Validity]\r\n\t    \tpipeline_stage: Pre-transformation\r\n\r\n<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-65c0e59 elementor-widget elementor-widget-text-editor\" data-id=\"65c0e59\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>These Data Quality checks ensure a robust pipeline and catch issues early. Adding pre-ingestion checks to this process will improve this setup even more. In more complicated pipelines this will be beneficial, as there might be multiple different sources being ingested.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6470880 e-flex e-con-boxed e-con e-parent\" data-id=\"6470880\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dc2444d elementor-widget elementor-widget-heading\" data-id=\"dc2444d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Final thoughts<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-55167dd elementor-widget elementor-widget-text-editor\" data-id=\"55167dd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Just like a regular production line, a data product production line should have quality checks embedded. Never lose sight of the bigger picture: ultimately data is used to achieve business objectives. Whether your focus is compliance or using AI, high-quality data products will help you achieve your goals. And the best way to ensure high-quality data products is by integrating Data Quality throughout your data pipeline.<\/p><p>Clever Republic has worked on multiple successful Data Quality implementations. We are always open to share our thoughts on how to fit Data Quality in your strategy, governance, and technology stacks. <a href=\"https:\/\/www.cleverrepublic.com\/contact\/\">Get in touch with us<\/a>\u00a0to learn more!<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>When something goes wrong with a dashboard, report, or KPI, most people start at the end. They ask: was this data checked before publishing? Is the report broken? But these questions come too late. By the time data hits the end of the pipeline, many opportunities to catch issues have already passed.\u00a0 \u00a0 In our&hellip;<\/p>","protected":false},"author":7,"featured_media":22783,"template":"","categories":[148],"class_list":["post-22781","blog","type-blog","status-publish","has-post-thumbnail","hentry","category-data_quality"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Why you should embed Data Quality throughout the Data Pipeline - Clever Republic<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cleverrepublic.com\/nl\/blog\/\/resources\/blog\/why-you-should-embed-data-quality-throughout-the-data-pipeline\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why you should embed Data Quality throughout the Data Pipeline - Clever Republic\" \/>\n<meta property=\"og:description\" content=\"When something goes wrong with a dashboard, report, or KPI, most people start at the end. 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