SEO: rewrite meta descriptions, add FAQ schema, add CTA box to all articles

- Rewrite meta descriptions on 4 high-impression articles (churn, compliance, data quality, ecommerce)
- Fix data-quality-validation-pipelines title & description to capture zero-click statistical validation queries
- Add FAQPage schema to churn prediction and data quality articles
- Add service CTA box to article-footer.php (appears on all blog articles)
- Add responsive CSS for CTA box in main.css

blog/articles/data-quality-validation-pipelines.php

@@ -3,8 +3,8 @@
 header('Strict-Transport-Security: max-age=31536000; includeSubDomains');
 
 // Article-specific SEO variables
-$article_title = "Building Robust Data Quality Validation Pipelines";
-$article_description = "Implement comprehensive data validation systems to ensure accuracy and reliability in your data processing workflows. Expert guide for UK businesses.";
+$article_title = "Data Quality Validation Pipelines: Complete UK Guide (2026)";
+$article_description = "Step-by-step guide to building data quality validation pipelines: schema checks, statistical validation, anomaly detection & automated alerts. Built for UK data teams.";
 $article_keywords = "data quality validation, data pipeline UK, data validation systems, data accuracy, data processing workflows, UK data management";
 $article_author = "UK Data Services Technical Team";
 $canonical_url = "https://ukdataservices.co.uk/blog/articles/data-quality-validation-pipelines";
@@ -455,5 +455,38 @@ $read_time = 9;
     <!-- Scripts -->
     <script src="../../assets/js/main.js"></script>
 <script src="../../assets/js/cro-enhancements.js"></script>
+
+<script type="application/ld+json">
+{
+  "@context": "https://schema.org",
+  "@type": "FAQPage",
+  "mainEntity": [
+    {
+      "@type": "Question",
+      "name": "What is advanced statistical validation in data pipelines?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "Advanced statistical validation uses techniques such as z-score analysis, interquartile range checks, Kolmogorov-Smirnov tests, and distribution comparison to detect anomalies in data pipelines that simple rule-based checks miss. It catches issues like distributional drift, unexpected skew, or out-of-range values that only become visible when compared to historical baselines."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "What tools are best for data quality validation in Python?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "The most widely used Python tools for data quality validation are Great Expectations (comprehensive rule-based validation with HTML reports), Pandera (schema validation for DataFrames), Deequ (Amazon's library for large-scale validation), and dbt tests for SQL-based pipelines. Great Expectations is the most popular choice for production data pipelines in UK data teams."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "How do you validate data quality automatically in a pipeline?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "Automated data quality validation involves: (1) defining schema and type constraints, (2) setting statistical thresholds based on historical baselines, (3) running validation checks as pipeline steps, (4) routing failed records to a quarantine layer, and (5) alerting the data team via Slack or email. Tools like Great Expectations or dbt can run these checks natively within Airflow or Prefect workflows."
+      }
+    }
+  ]
+}
+</script>
 </body>
 </html>