SEO: fix garbled blog article HTML, update H1, fix BI dashboard description

data-quality-validation-pipelines.php:
- Fix H1 to match title (was still "Advanced Statistical Validation..." after title was updated)
- Remove 3 orphaned text fragments from broken AI edit merges ("racy and reliability.", "ta pipelines...", "ust in your analytics.")
- Fix split <strong> tag mid-word
- Fix internal link from /services/web-scraping-services.php to /services/web-scraping

business-intelligence-dashboard-design.php:
- Rewrite meta description - old one concatenated with title into bizarre GSC query
  "2025 ux best practices for displaying data analysis results competitive intelligence dashboard..."
  (74 impressions, 0 clicks)

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

@@ -105,8 +105,8 @@ $read_time = 9;
                 <span class="read-time">9 min read</span>
             </div>
             <header class="article-header">
-                <h1>A Practical Guide to Advanced Statistical Validation for Data Accuracy</h1>
-                <p class="article-lead">Inaccurate data leads to flawed analysis and poor strategic decisions. This guide provides a deep dive into the advanced statistical validation methods required to ensure data integrity. We'll cover core techniques, from outlier detection to distributional analysis, and show how to build them into a robust data quality pipeline—a critical step for any data-driven organisation, especially when using data from sources like <a href="https://ukdataservices.co.uk/services/web-scraping-services.php">web scraping</a>.</p>racy and reliability.</p>
+                <h1>Data Quality Validation for Web Scraping Pipelines</h1>
+                <p class="article-lead">Inaccurate data leads to flawed analysis and poor strategic decisions. This guide provides a deep dive into the advanced statistical validation methods required to ensure data integrity. We'll cover core techniques, from outlier detection to distributional analysis, and show how to build them into a robust data quality pipeline—a critical step for any data-driven organisation, especially when using data from sources like <a href="/services/web-scraping">web scraping</a>.</p>
 
             <section class="faq-section">
                 <h2 class="section-title">Frequently Asked Questions</h2>
@@ -120,9 +120,9 @@ $read_time = 9;
                 </div>
                 <div class="faq-item">
                     <h3>How does this apply to web scraping data?</h3>
-                    <p>For data acquired via our <a href="https://ukdataservices.co.uk/services/web-scraping-services.php">web scraping services</a>, statistical validation is crucial for identifying collection errors, format inconsistencies, or outliers (e.g., a product price of £0.01). It transforms raw scraped data into reliable business intelligence.</p>
+                    <p>For data acquired via our <a href="/services/web-scraping">web scraping services</a>, statistical validation is crucial for identifying collection errors, format inconsistencies, or outliers (e.g., a product price of £0.01). It transforms raw scraped data into reliable business intelligence.</p>
                 </div>
-            </section>ta pipelines, ensure accuracy, and build a foundation of trust in your data.</p>
+            </section>
             </header>
             <div class="key-takeaways">
                 <h2>Key Takeaways</h2>
@@ -132,8 +132,8 @@ $read_time = 9;
                     <li><strong>Core Techniques:</strong> This guide covers essential methods including Z-scores for outlier detection, Benford's Law for fraud detection, and distribution analysis to spot anomalies.</li>
                     <li><strong>UK Focus:</strong> We address the specific needs and data landscapes relevant to businesses operating in the United Kingdom.</li>
                 </ul>
-            </div>ust in your analytics.</p>
-                <p>At its core, <strong>advanced statistical validation is the critical process tha</strong>t uses statistical models to identify anomalies, inconsistencies, and errors within a dataset. Unlike simple rule-based checks (e.g., checking if a field is empty), it evaluates the distribution, relationships, and patterns in the data to flag sophisticated quality issues.</p>
+            </div>
+                <p>At its core, <strong>advanced statistical validation is the critical process that</strong> uses statistical models to identify anomalies, inconsistencies, and errors within a dataset. Unlike simple rule-based checks (e.g., checking if a field is empty), it evaluates the distribution, relationships, and patterns in the data to flag sophisticated quality issues.</p>
 
 <h2 id="faq">Frequently Asked Questions about Data Validation</h2>