[{"data":1,"prerenderedAt":85},["ShallowReactive",2],{"$fRwUEEUZgsd1pO4eivtmi7JMVS3QerX5dwJbfVl-wkDE":3},{"title":4,"date":5,"dateModified":6,"datePublished":7,"dateModifiedISO":7,"image":8,"content":9,"faq":10,"metaTitle":11,"metaDescription":12,"author":13,"authorBio":10,"authorLinkedin":10,"authorTitle":10,"authorPhoto":14,"lastReviewed":10,"noindex":15,"researchBasis":10,"category":16,"readingTime":17,"related":18,"prev":34,"next":35,"toc":36,"takeaways":84},"Data Quality in Web Scraping: Validation, Deduplication & Matching (2026)","02 Nov 2026","02 NOV 2026","2026-11-02","/img/news/data-quality-web-scraping-2026.svg","\u003Cp>You scrape 10,000 product prices from competitors. Your pricing system feeds the data into a pricing engine. The engine recommends prices 30% lower than it should. Your sales team implements the recommendation and margin drops €200K.\u003C/p>\n\u003Cp>What happened? The scraped data had quality problems: duplicate entries, missing fields, encoding errors. Your pricing system made decisions on garbage data.\u003C/p>\n\u003Cp>This guide covers how to ensure scraped data is clean enough to feed into critical business systems.\u003C/p>\n\u003Ch2 id=\"why-scraped-data-quality-matters\">Why Scraped Data Quality Matters\u003C/h2>\n\u003Cp>Scraped data is only as good as the system that consumes it.\u003C/p>\n\u003Cp>\u003Cstrong>Bad data → bad decisions:\u003C/strong>\u003C/p>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Bad Data\u003C/th>\n\u003Cth>Decision\u003C/th>\n\u003Cth>Cost\u003C/th>\n\u003C/tr>\n\u003C/thead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Duplicate products (same product listed 5 times)\u003C/td>\n\u003Ctd>Underbid 5× on one product\u003C/td>\n\u003Ctd>Margin loss\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd>Missing prices (null values)\u003C/td>\n\u003Ctd>Skip product in analysis\u003C/td>\n\u003Ctd>Incomplete competitive data\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd>Encoding errors ("Café" instead of "Café")\u003C/td>\n\u003Ctd>Name mismatch, product not found\u003C/td>\n\u003Ctd>Duplicates in system\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd>Wrong currency (€ mixed with $)\u003C/td>\n\u003Ctd>Compare prices in different currencies\u003C/td>\n\u003Ctd>Wrong pricing decisions\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd>Wrong product match ("Blue Shirt S" vs "Shirt Blue Small")\u003C/td>\n\u003Ctd>Track wrong product, miss competitor move\u003C/td>\n\u003Ctd>Margin loss\u003C/td>\n\u003C/tr>\n\u003C/tbody>\u003C/table>\n\u003Cp>\u003Cstrong>Real example:\u003C/strong> A retailer's scraper picked up 50 duplicate product listings due to deduplication failure. The pricing system thought there were 50 competitors where there was 1. It underpriced by 25% to "compete". Cost: €50K in lost margin before the error was caught.\u003C/p>\n\u003Chr>\n\u003Caside class=\"article__usecase-card\">\u003Cdiv class=\"article__usecase-label\">Related use case\u003C/div>\u003Ch3 class=\"article__usecase-title\">Any-site data scraper\u003C/h3>\u003Cp class=\"article__usecase-blurb\">No-code extraction from any website. Managed infrastructure, no anti-bot headaches.\u003C/p>\u003Ca class=\"article__usecase-link\" href=\"/use-cases/data-scraper\">See how it works →\u003C/a>\u003C/aside>\u003Ch2 id=\"three-levels-of-data-quality\">Three Levels of Data Quality\u003C/h2>\n\u003Ch3 id=\"level-1-field-level-validation-basic\">Level 1: Field-Level Validation (Basic)\u003C/h3>\n\u003Cp>Does each field contain valid data?\u003C/p>\n\u003Cp>\u003Cstrong>Validation rules:\u003C/strong>\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def validate_product_data(product):\n  """Return True if product passes quality gates"""\n\n  errors = []\n\n  # Price validation\n  if not product.get('price'):\n    errors.append('price: missing')\n  elif not isinstance(product['price'], (int, float)):\n    errors.append('price: not a number')\n  elif product['price'] < 0 or product['price'] > 1000000:\n    errors.append('price: out of reasonable range')\n\n  # Title validation\n  if not product.get('title') or len(product['title'].strip()) < 3:\n    errors.append('title: missing or too short')\n\n  # URL validation\n  if not product.get('url') or not product['url'].startswith('http'):\n    errors.append('url: missing or invalid')\n\n  # Return result\n  return len(errors) == 0, errors\n\n# Usage\nis_valid, errors = validate_product_data(product)\nif not is_valid:\n  log_data_issue(product, errors)\n  skip_product(product)  # Do not process invalid data\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Cp>\u003Cstrong>Common validation failures:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Missing required fields (price, title, URL)\u003C/li>\n\u003Cli>Wrong data type (price = string instead of float)\u003C/li>\n\u003Cli>Out-of-range values (price = $0 or $999,999)\u003C/li>\n\u003Cli>Invalid formats (email not containing @, phone with letters)\u003C/li>\n\u003Cli>Encoding issues ("Café" instead of "Café")\u003C/li>\n\u003C/ul>\n\u003Ch3 id=\"level-2-cross-field-consistency-intermediate\">Level 2: Cross-Field Consistency (Intermediate)\u003C/h3>\n\u003Cp>Do fields make sense together?\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def validate_consistency(product):\n  """Check if fields are logically consistent"""\n\n  errors = []\n\n  # If on sale, sale price should be less than regular price\n  if product.get('sale_price') and product.get('regular_price'):\n    if product['sale_price'] >= product['regular_price']:\n      errors.append('sale_price >= regular_price (invalid discount)')\n\n  # If out of stock, should not have price\n  # (or price should be 0)\n  if product.get('stock_status') == 'out of stock':\n    if product.get('price') and product['price'] > 0:\n      errors.append('out of stock but has price')\n\n  # Inventory count should match stock status\n  if product.get('inventory_count') == 0:\n    if product.get('stock_status') != 'out of stock':\n      errors.append('inventory_count=0 but stock_status!=out_of_stock')\n\n  return len(errors) == 0, errors\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Ch3 id=\"level-3-system-level-quality-advanced\">Level 3: System-Level Quality (Advanced)\u003C/h3>\n\u003Cp>Do the data as a whole make sense?\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def validate_system_quality(products):\n  """Check overall data quality across dataset"""\n\n  metrics = {\n    'null_rate': 0,\n    'duplicate_rate': 0,\n    'out_of_range_rate': 0,\n  }\n\n  # 1. Null rate: % of fields that are null\n  total_fields = len(products) * len(products[0])\n  null_fields = sum(\n    1 for p in products for v in p.values() if v is None\n  )\n  metrics['null_rate'] = null_fields / total_fields\n\n  # 2. Duplicate rate: % of products that are duplicates\n  seen_urls = set()\n  duplicates = 0\n  for product in products:\n    if product['url'] in seen_urls:\n      duplicates += 1\n    seen_urls.add(product['url'])\n  metrics['duplicate_rate'] = duplicates / len(products)\n\n  # 3. Out-of-range rate: % of prices outside reasonable bounds\n  out_of_range = sum(\n    1 for p in products\n    if p['price'] < 0 or p['price'] > 1000000\n  )\n  metrics['out_of_range_rate'] = out_of_range / len(products)\n\n  # Set quality thresholds\n  if metrics['null_rate'] > 0.05:  # > 5% nulls\n    return False, f"Null rate too high: {metrics['null_rate']}"\n\n  if metrics['duplicate_rate'] > 0.01:  # > 1% duplicates\n    return False, f"Duplicate rate too high: {metrics['duplicate_rate']}"\n\n  return True, metrics\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Chr>\n\u003Ch2 id=\"handling-common-data-quality-issues\">Handling Common Data Quality Issues\u003C/h2>\n\u003Ch3 id=\"issue-1-duplicate-products\">Issue 1: Duplicate Products\u003C/h3>\n\u003Cp>\u003Cstrong>Problem:\u003C/strong> The same product appears multiple times in your dataset.\u003C/p>\n\u003Cp>\u003Cstrong>Causes:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Same product listed in multiple categories\u003C/li>\n\u003Cli>Multiple colour/size variants scraped as separate products\u003C/li>\n\u003Cli>Pagination loop: page 1 product appears again on page 2\u003C/li>\n\u003Cli>Same product on retailer's site + marketplace at same domain\u003C/li>\n\u003C/ul>\n\u003Cp>\u003Cstrong>Solution: Deduplication by URL\u003C/strong>\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def deduplicate_by_url(products):\n  """Remove duplicate products, keeping first occurrence"""\n\n  seen_urls = set()\n  unique_products = []\n\n  for product in products:\n    if product['url'] not in seen_urls:\n      unique_products.append(product)\n      seen_urls.add(product['url'])\n\n  return unique_products\n\n# Usage\nproducts_before = 5000\nproducts_after = deduplicate_by_url(products_before)\nprint(f"Removed {len(products_before) - len(products_after)} duplicates")\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Cp>\u003Cstrong>Better: Deduplication by SKU/EAN\u003C/strong>\u003C/p>\n\u003Cp>If you have product codes, deduplicate by those (more robust than URL):\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def deduplicate_by_sku(products):\n  """Deduplicate by SKU, keeping lowest price"""\n\n  sku_map = {}\n\n  for product in products:\n    sku = product.get('sku')\n\n    if not sku:\n      continue  # Skip products without SKU\n\n    if sku not in sku_map:\n      sku_map[sku] = product\n    else:\n      # Keep the cheaper price (more competitive)\n      if product['price'] < sku_map[sku]['price']:\n        sku_map[sku] = product\n\n  return list(sku_map.values())\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Ch3 id=\"issue-2-missing-or-null-fields\">Issue 2: Missing or Null Fields\u003C/h3>\n\u003Cp>\u003Cstrong>Problem:\u003C/strong> Fields like price, title, or inventory are missing.\u003C/p>\n\u003Cp>\u003Cstrong>Solution: Data Imputation or Rejection\u003C/strong>\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def handle_missing_data(product):\n  """Handle missing fields: impute or reject"""\n\n  # Strategy 1: Reject if critical field is missing\n  if not product.get('price'):\n    return None  # Reject this product\n\n  # Strategy 2: Impute with defaults for non-critical fields\n  if not product.get('inventory_count'):\n    product['inventory_count'] = None  # Keep as null\n\n  if not product.get('description'):\n    product['description'] = ''  # Default to empty string\n\n  # Strategy 3: Look up from fallback source\n  if not product.get('sku'):\n    product['sku'] = generate_sku_from_title(product['title'])\n\n  return product\n\n# Usage\nproducts = [handle_missing_data(p) for p in products]\nproducts = [p for p in products if p is not None]  # Remove rejected products\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Ch3 id=\"issue-3-encoding-errors\">Issue 3: Encoding Errors\u003C/h3>\n\u003Cp>\u003Cstrong>Problem:\u003C/strong> Character encoding issues: "Café" instead of "Café"\u003C/p>\n\u003Cp>\u003Cstrong>Solution: Normalize Unicode\u003C/strong>\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">import unicodedata\n\ndef normalize_encoding(text):\n  """Fix encoding issues by normalizing unicode"""\n\n  if not text:\n    return text\n\n  # Decode HTML entities\n  import html\n  text = html.unescape(text)\n\n  # Normalize unicode (decompose and recompose)\n  text = unicodedata.normalize('NFKC', text)\n\n  return text\n\n# Usage\nproduct['title'] = normalize_encoding(product['title'])\n# "Café" → "Café"\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Ch3 id=\"issue-4-currency-mismatches\">Issue 4: Currency Mismatches\u003C/h3>\n\u003Cp>\u003Cstrong>Problem:\u003C/strong> Prices in different currencies (€, $, £) are compared as if same currency.\u003C/p>\n\u003Cp>\u003Cstrong>Solution: Normalize Currency\u003C/strong>\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def extract_currency_and_price(price_str):\n  """Extract currency and numeric price"""\n\n  import re\n\n  # Match currency symbol and price\n  match = re.match(r'([$€£¥])\\s*([\\d,\\.]+)', price_str)\n\n  if not match:\n    return None, None\n\n  currency = match.group(1)\n  price_text = match.group(2)\n\n  # Convert to float\n  price = float(price_text.replace(',', '').replace('.', ''))\n\n  # Map symbol to code\n  currency_map = {'$': 'USD', '€': 'EUR', '£': 'GBP', '¥': 'JPY'}\n  currency_code = currency_map.get(currency, 'UNKNOWN')\n\n  return currency_code, price\n\n# Usage\ncurrency, price = extract_currency_and_price('€25.99')\n# Returns: ('EUR', 25.99)\n\n# Store both fields\nproduct['price'] = 25.99\nproduct['currency'] = 'EUR'\n\n# Compare prices only if currency matches\nif product1['currency'] == product2['currency']:\n  price_diff = product1['price'] - product2['price']\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Ch3 id=\"issue-5-product-matching-across-sites\">Issue 5: Product Matching Across Sites\u003C/h3>\n\u003Cp>\u003Cstrong>Problem:\u003C/strong> Same product listed on 3 different sites with different product names and IDs. How do you know they are the same product?\u003C/p>\n\u003Cp>\u003Cstrong>Example:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Site A: "ASICS Gel-Lyte III Black Sneaker Size 10"\u003C/li>\n\u003Cli>Site B: "Asics Gel Lyte III Blk M 10"\u003C/li>\n\u003Cli>Site C: "Gel Lyte III - Black - 10"\u003C/li>\n\u003C/ul>\n\u003Cp>\u003Cstrong>Solution: Multi-Factor Matching\u003C/strong>\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">def match_products(product_a, product_b):\n  """Determine if two products are the same using multiple signals"""\n\n  score = 0\n\n  # Factor 1: Brand + Title similarity (40% weight)\n  if product_a.get('brand') == product_b.get('brand'):\n    score += 10\n\n  from difflib import SequenceMatcher\n  title_sim = SequenceMatcher(\n    None,\n    product_a.get('title', '').lower(),\n    product_b.get('title', '').lower()\n  ).ratio()\n\n  if title_sim > 0.8:  # 80% textual match\n    score += 20\n\n  # Factor 2: EAN/SKU match (40% weight)\n  if product_a.get('ean') and product_b.get('ean'):\n    if product_a['ean'] == product_b['ean']:\n      score += 40\n\n  # Factor 3: Price similarity (20% weight)\n  if product_a.get('price') and product_b.get('price'):\n    price_diff = abs(\n      product_a['price'] - product_b['price']\n    ) / product_a['price']\n\n    if price_diff < 0.05:  # Within 5%\n      score += 20\n\n  # Decision: Match if score > 70\n  return score > 70, score\n\n# Usage\nis_match, score = match_products(product_a, product_b)\nif is_match:\n  print(f"Products matched with score {score}")\n  # They are the same product\nelse:\n  print(f"Products are different (score {score})")\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Chr>\n\u003Caside class=\"article__inline-cta\">\u003Cp class=\"article__inline-cta-text\">Try ScrapeWise on your own URL. \u003Cstrong>Your first 5 requests are free.\u003C/strong>\u003C/p>\u003Ca class=\"article__inline-cta-btn\" href=\"https://portal.scrapewise.ai/register\" target=\"_blank\" rel=\"noopener\">Start Free →\u003C/a>\u003C/aside>\u003Ch2 id=\"building-a-data-quality-dashboard\">Building a Data Quality Dashboard\u003C/h2>\n\u003Cp>Track data quality metrics over time.\u003C/p>\n\u003Cdiv class=\"code-block\">\u003Cbutton type=\"button\" class=\"code-block__copy\" data-copy-code aria-label=\"Copy code\">Copy\u003C/button>\u003Cpre>\u003Ccode class=\"language-python\">class DataQualityMonitor:\n  def __init__(self):\n    self.metrics_history = []\n\n  def measure_quality(self, products, scraper_name):\n    """Measure quality metrics for a scraper run"""\n\n    total = len(products)\n\n    metrics = {\n      'timestamp': datetime.now(),\n      'scraper': scraper_name,\n      'total_rows': total,\n      'null_rate': sum(1 for p in products for v in p.values() if v is None) / (total * len(products[0])),\n      'duplicate_rate': len(products) - len(set(p['url'] for p in products)) / len(products),\n      'invalid_prices': sum(1 for p in products if not isinstance(p.get('price'), (int, float))),\n      'missing_titles': sum(1 for p in products if not p.get('title')),\n    }\n\n    self.metrics_history.append(metrics)\n\n    # Alert if quality drops\n    if metrics['null_rate'] > 0.05:\n      alert(f"Data quality warning: {scraper_name} has {metrics['null_rate']*100}% nulls")\n\n    return metrics\n\n# Usage\nmonitor = DataQualityMonitor()\n\nfor run in daily_scraper_runs:\n  quality = monitor.measure_quality(run['products'], run['scraper_name'])\n  print(f"Quality score: {100 - quality['null_rate']*100:.1f}%")\n\u003C/code>\u003C/pre>\u003C/div>\n\u003Cp>\u003Cstrong>Dashboard visualization:\u003C/strong>\u003C/p>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Scraper\u003C/th>\n\u003Cth>Last 7 Days Avg Quality\u003C/th>\n\u003Cth>Trend\u003C/th>\n\u003Cth>Alert\u003C/th>\n\u003C/tr>\n\u003C/thead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>H&M Prices\u003C/td>\n\u003Ctd>99.2%\u003C/td>\n\u003Ctd>↑ Improving\u003C/td>\n\u003Ctd>✓ OK\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd>ASOS Inventory\u003C/td>\n\u003Ctd>96.8%\u003C/td>\n\u003Ctd>↓ Declining\u003C/td>\n\u003Ctd>⚠️ Watch\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd>Amazon Sellers\u003C/td>\n\u003Ctd>98.1%\u003C/td>\n\u003Ctd>→ Stable\u003C/td>\n\u003Ctd>✓ OK\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd>Zara Trending\u003C/td>\n\u003Ctd>94.5%\u003C/td>\n\u003Ctd>↓ Declining\u003C/td>\n\u003Ctd>❌ Poor\u003C/td>\n\u003C/tr>\n\u003C/tbody>\u003C/table>\n\u003Chr>\n\u003Ch2 id=\"data-quality-checklist\">Data Quality Checklist\u003C/h2>\n\u003Cp>Use this before deploying any scraper or feeding data to a downstream system:\u003C/p>\n\u003Cp>\u003Cstrong>Field-Level Validation:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> All required fields are present (< 5% nulls)\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Numeric fields (price, inventory) are numbers, not strings\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Prices are in reasonable range (not $0 or $999,999)\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> URLs are valid and start with http\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Encoding is correct (no "Café" artefacts)\u003C/li>\n\u003C/ul>\n\u003Cp>\u003Cstrong>Cross-Field Consistency:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Sale price < regular price\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Out of stock items do not have price\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Inventory count consistent with stock status\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Dates are in the past or near future (not 2050)\u003C/li>\n\u003C/ul>\n\u003Cp>\u003Cstrong>System-Level Quality:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Duplicates removed or flagged (< 1% rate)\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Null rate acceptable (< 5%)\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Row count within expected range (±20%)\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Data freshness acceptable (< 24 hours old)\u003C/li>\n\u003C/ul>\n\u003Cp>\u003Cstrong>Product Matching:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Cross-site duplicates merged (if matching)\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Product IDs stable (same product = same ID)\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Colour/size variants handled correctly\u003C/li>\n\u003Cli>\u003Cinput disabled=\"\" type=\"checkbox\"> Matches verified spot-check (10 products)\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"real-example-e-commerce-pricing-system\">Real Example: E-commerce Pricing System\u003C/h2>\n\u003Cp>\u003Cstrong>Scenario:\u003C/strong> A retailer feeds scraped competitor prices into an automated repricing system.\u003C/p>\n\u003Cp>\u003Cstrong>The problem:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Competitor price feed contains 5% duplicates (same product listed multiple times)\u003C/li>\n\u003Cli>Missing price on 2% of products (null values)\u003C/li>\n\u003Cli>Some prices are 10× higher than realistic (data errors)\u003C/li>\n\u003Cli>Currency codes missing (can't tell if $100 USD or $100 CAD)\u003C/li>\n\u003C/ul>\n\u003Cp>\u003Cstrong>Result:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>System tries to price against 5 copies of the same product\u003C/li>\n\u003Cli>Gaps in data cause system to default to high prices\u003C/li>\n\u003Cli>Wrong currency causes system to underprice by 20%\u003C/li>\n\u003Cli>Margin loss: €150K in one quarter\u003C/li>\n\u003C/ul>\n\u003Cp>\u003Cstrong>The fix:\u003C/strong>\u003C/p>\n\u003Col>\n\u003Cli>Deduplicate products by EAN (removes 5% duplicates)\u003C/li>\n\u003Cli>Reject products with missing prices (removes 2% invalid rows)\u003C/li>\n\u003Cli>Validate price range: reject prices > 10× median (removes outliers)\u003C/li>\n\u003Cli>Require currency code on all prices (prevents currency mismatch)\u003C/li>\n\u003C/ol>\n\u003Cp>\u003Cstrong>Result:\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>Data quality improves from 90% to 98%\u003C/li>\n\u003Cli>Repricing decisions now based on clean data\u003C/li>\n\u003Cli>Margin improves €40K per quarter (reduces underbidding)\u003C/li>\n\u003C/ul>\n\u003Chr>\n\u003Ch2 id=\"conclusion-quality-data-requires-discipline\">Conclusion: Quality Data Requires Discipline\u003C/h2>\n\u003Cp>Scraped data is not "raw data." It is data that requires verification before use.\u003C/p>\n\u003Cp>The companies with the most reliable pricing and inventory systems invest in data quality:\u003C/p>\n\u003Cul>\n\u003Cli>Validation rules\u003C/li>\n\u003Cli>Deduplication\u003C/li>\n\u003Cli>Monitoring dashboards\u003C/li>\n\u003Cli>Regular audits\u003C/li>\n\u003C/ul>\n\u003Cp>It takes effort, but it pays for itself.\u003C/p>\n\u003Cp>\u003Cstrong>Ready to improve your data quality?\u003C/strong> Try \u003Ca href=\"/demo\">ScrapeWise's data quality validation\u003C/a> — automatic validation, deduplication, and quality scoring.\u003C/p>\n",null,"Web Scraping Data Quality: Validation, Deduplication, and Data Matching","Guide to ensuring scraped data quality. Validation rules, handling duplicates, product matching across sites, and building data quality dashboards.","Siim Brazier","/img/team/siim.jpg",false,"Scraping",9,[19,24,29],{"slug":20,"title":21,"image":22,"date":5,"category":16,"excerpt":23},"critical-web-scraping-mistakes-2026","7 Critical Web Scraping Mistakes That Break Your Data Pipeline (2026)","/img/news/critical-web-scraping-mistakes-2026.svg","Discover 7 critical web scraping mistakes that kill data pipelines. Real costs, debugging steps, and prevention checklist for each mistake.",{"slug":25,"title":26,"image":27,"date":5,"category":16,"excerpt":28},"scraping-fashion-ecommerce-inventory-tracking-2026","Scraping Fashion E-commerce Sites: Inventory Tracking & Competitive Analysis (2026)","/img/news/scraping-fashion-ecommerce-2026.svg","Complete guide to scraping fashion e-commerce. Handle size/color variants, inventory by size, real-time stock levels, and fashion-specific anti-bot. H&M, ASOS, Zara patterns included.",{"slug":30,"title":31,"image":32,"date":5,"category":16,"excerpt":33},"web-scraping-roi-calculator-business-value-2026","Web Scraping ROI Calculator: Measure the Business Value of Data Extraction (2026)","/img/news/web-scraping-roi-calculator-2026.svg","Calculate your web scraping ROI in minutes. Compare costs of manual data collection vs automation. Includes 3 real case studies, ROI framework, and free calculator.",{"slug":25,"title":26},{"slug":20,"title":21},[37,41,44,48,51,54,57,60,63,66,69,72,75,78,81],{"level":38,"text":39,"id":40},2,"Why Scraped Data Quality Matters","why-scraped-data-quality-matters",{"level":38,"text":42,"id":43},"Three Levels of Data Quality","three-levels-of-data-quality",{"level":45,"text":46,"id":47},3,"Level 1: Field-Level Validation (Basic)","level-1-field-level-validation-basic",{"level":45,"text":49,"id":50},"Level 2: Cross-Field Consistency (Intermediate)","level-2-cross-field-consistency-intermediate",{"level":45,"text":52,"id":53},"Level 3: System-Level Quality (Advanced)","level-3-system-level-quality-advanced",{"level":38,"text":55,"id":56},"Handling Common Data Quality Issues","handling-common-data-quality-issues",{"level":45,"text":58,"id":59},"Issue 1: Duplicate Products","issue-1-duplicate-products",{"level":45,"text":61,"id":62},"Issue 2: Missing or Null Fields","issue-2-missing-or-null-fields",{"level":45,"text":64,"id":65},"Issue 3: Encoding Errors","issue-3-encoding-errors",{"level":45,"text":67,"id":68},"Issue 4: Currency Mismatches","issue-4-currency-mismatches",{"level":45,"text":70,"id":71},"Issue 5: Product Matching Across Sites","issue-5-product-matching-across-sites",{"level":38,"text":73,"id":74},"Building a Data Quality Dashboard","building-a-data-quality-dashboard",{"level":38,"text":76,"id":77},"Data Quality Checklist","data-quality-checklist",{"level":38,"text":79,"id":80},"Real Example: E-commerce Pricing System","real-example-e-commerce-pricing-system",{"level":38,"text":82,"id":83},"Conclusion: Quality Data Requires Discipline","conclusion-quality-data-requires-discipline",[],1793646897873]