Claro

Browse Resources

Free tools, a product-data glossary, neutral comparisons, step-by-step playbooks, articles, and evergreen guides for catalog teams — matching, deduplication, classification, enrichment, AI validation, and AI search.

Glossary
  1. 01 What Is Entity Resolution?
  2. 02 PPWR (Packaging and Packaging Waste Regulation): What It Means for Product Data
  3. 03 Golden Record Product Data: What Is a Canonical Product Record?
  4. 04 PPWR / EU-Verpackungsverordnung: Was Unternehmen über Verpackungsdaten wissen müssen
  5. 05 What Is Fuzzy Matching?
  6. 06 PPWR: il nuovo Regolamento UE sugli imballaggi e i dati prodotto
  7. 07 Deterministic vs Probabilistic Matching
  8. 08 PPWR Declaration of Conformity (DoC): What It Is and What Data Supports It
  9. 09 Confidence Score in Data Matching: A Practical Guide
  10. 10 What Is Data Provenance?
  11. 11 Write-back: Safely Updating ERP and PIM Systems After AI Validation
  12. 12 What Is Record Linkage?
  13. 13 What Is Schema Mapping?
  14. 14 Product Data Normalization: What It Is and Why It Matters
  15. 15 What Is Master Data Management (MDM)?
  16. 16 What Is a PIM? Product Information Management Explained
  17. 17 What Is Schema Drift?
  18. 18 Product Knowledge Graph: What It Is and Why It Powers AI Search
  19. 19 What Is Product Compliance Readiness?
  20. 20 Supplier Scorecard: How to Grade Vendor Data Quality
  21. 21 What Is a Safety Data Sheet and When Is One Required?
  22. 22 SKU vs MPN vs GTIN: What Each Identifier Does and Why All Three Matter
  23. 23 What Is Generative Engine Optimization (GEO)?
  24. 24 Schema.org Product Structured Data: The Complete Guide
  25. 25 What Is Product Content Syndication?
  26. 26 What Is Google's Universal Commerce Protocol (UCP)?
  27. 27 What Is GDSN? The Global Data Synchronisation Network Explained
  28. 28 What Is a Data Pool? GS1, GDSN, and Synchronized Product Data
  29. 29 What Is Unit of Measure (UOM) in Product Data?
  30. 30 GTIN vs EAN vs UPC: The Definitive Guide for Product Data Teams
  31. 31 What Is a GLN? Global Location Number Explained
  32. 32 ECLASS IRDI Format: Structure, Segments, and Why It Matters
  33. 33 ETIM in BMEcat: Structured Product Classification for Technical Catalogs
  34. 34 UNECE Rec 20 Unit Codes Explained
  35. 35 What is item master data? Fields, owners, and failure modes
  36. 36 What Is a CAS Number?
  37. 37 SVHC Candidate List: What It Is and Why Product Data Teams Track It
  38. 38 What Is RAL Classic? The Color Standard for Product Catalogs
  39. 39 HS Codes and Country of Origin in Product Data
  40. 40 IP Rating Explained (IP54, IP65, IP67)
  41. 41 IP Rating Chart (IEC 60529): What Every Digit Means
  42. 42 IK Rating Explained: The Impact-Resistance Scale for Product Data Teams
  43. 43 How to Read an ATEX Marking
  44. 44 Supplier master vs vendor master: what is the difference?
  45. 45 ATEX Zone Classification: Zones 0-2 and 20-22 Explained
  46. 46 What Is a Digital Product Passport (DPP)?
  47. 47 Was Ist das EU-DPP-Register? (Und Was Es Nicht Ist)
  48. 48 IEC 60309 Colors and Clock Positions Explained
  49. 49 What Is the EU DPP Registry? (And What It Isn't)
  50. 50 NEMA Enclosure Types Explained
  51. 51 CENELEC Cable Designation: HD 361 Type Codes Explained
  52. 52 What Is the EU Battery Passport? Deadline, Scope, Requirements
  53. 53 Cos'è il Registro DPP dell'UE? (E cosa non è)
  54. 54 BSP Thread Dimensions: Nominal Sizes, TPI, and Catalog Matching
  55. 55 Was Ist der EU-Batteriepass? Frist, Umfang, Anforderungen
  56. 56 Is Claro a PIM? What It Does Instead
  57. 57 Cos'è il Passaporto della Batteria UE? Scadenza, Ambito, Requisiti
  58. 58 IES vs LDT Photometric Files
  59. 59 ETIM EC000042: Miniature Circuit Breaker
  60. 60 MAP vs RRP
  61. 61 ETIM EC000141: The Contactor Classification Code Explained
  62. 62 Price Skimming
  63. 63 Was Ist ein Kanonischer Produktdatensatz (Golden Record)?
Comparisons
  1. 01 ADR vs IATA: What's the Difference for Product Data Teams?
  2. 02 Fuzzy Matching vs Entity Resolution: Which Does Your Catalog Actually Need?
  3. 03 PIM vs MDM vs DAM: Which System Does What?
  4. 04 Akeneo vs Pimcore: PIM Platform Comparison for Distributors
  5. 05 Salsify vs Syndigo: Which Platform Fits Your Syndication Stack?
  6. 06 SEO vs GEO for Product Catalogs: What Your Data Needs to Win Both
  7. 07 PIM vs DAM: What Each Actually Manages
  8. 08 Build vs Buy Entity Matching: In-House Scripts vs a Matching Platform
  9. 09 GDSN vs Direct Feed: Which Syndication Path Fits Your Catalog?
  10. 10 PIM vs ERP: Why Your ERP Is Not a Product Catalog
  11. 11 Build vs Buy Catalog Infrastructure: A Total-Cost Comparison
  12. 12 CSV vs EDI vs API: How Suppliers Should Send You Data
  13. 13 BMEcat vs GDSN: B2B Catalog Exchange Formats
  14. 14 ETIM vs UNSPSC vs eCl@ss: Which Classification Standard Does Your Catalog Need?
  15. 15 Data Cleansing vs Data Enrichment: Not the Same Job
  16. 16 Product Taxonomy Comparison: ETIM vs UNSPSC vs Google Product Category
  17. 17 Amazon ASIN vs GTIN: Product Identity Across Marketplaces
  18. 18 ECLASS vs ETIM for Distributors: Which Classification Standard Do You Need?
  19. 19 GTIN vs MPN vs SKU: Which Product Identifier Does What?
  20. 20 GLN vs GTIN: Two GS1 Identifiers That Break Catalogs When Mixed
  21. 21 CAS Number vs EC Number: Which Identifier to Store and Why
  22. 22 SVHC vs SCIP vs REACH Annex XVII: Which Obligation Applies to Your SKU?
  23. 23 IP54 vs IP65 vs IP67: Which Ingress Protection Rating Fits Your Product Record?
  24. 24 UN 3480 vs UN 3481: What's the Difference?
  25. 25 HS vs CN vs TARIC: What Product Teams Need to Know
  26. 26 NEMA vs IP Ratings: Enclosure Ingress Standards Compared
  27. 27 NPT vs BSP vs Metric Threads: Side-by-Side Comparison
  28. 28 ATEX vs IECEx: What Product-Data Teams Need to Know
  29. 29 IK08 vs IK10: Impact Ratings in Product Data
  30. 30 UL vs CE Marking: Certification Data in Product Records
  31. 31 H05VV-F vs H07RN-F: Cable Code Comparison for Accurate Catalog Records
  32. 32 REACH vs RoHS: Which Compliance Data Belongs on the Product Record?
  33. 33 IP68 vs IP69K: Washdown and Submersion Ratings
  34. 34 M8 vs M12 Connectors: Identifying Industrial Sensor Cables
  35. 35 UNSPSC vs HS Codes: Classification vs Customs
  36. 36 AWG vs mm²: Cable Sizing Conversion for Product Records
  37. 37 Datasheet vs SDS: Document Types in Product Enrichment
  38. 38 Material master vs item master vs product master: same thing, three ERPs
  39. 39 MDM vs data quality tools: governance vs execution
  40. 40 Golden record vs canonical record vs master record
  41. 41 PIM vs Spreadsheet: When Does a Catalog Outgrow Excel?
  42. 42 Catalog Data Management Platform vs PIM Add-Ons
  43. 43 One-Time Enrichment vs Continuous Catalog Operations
  44. 44 Multi-domain MDM vs product-only MDM
  45. 45 Akeneo vs Salsify: which PIM fits B2B distribution?
  46. 46 Claro vs Onedot: two approaches to supplier data onboarding
  47. 47 Syndigo vs 1WorldSync: GDSN data pools compared
  48. 48 Plytix vs Akeneo: PIM for mid-market catalogs
  49. 49 Akeneo vs Pimcore vs Plytix: the mid-market PIM shortlist
  50. 50 inriver vs Akeneo: PIM choice for complex product catalogs
  51. 51 Pimcore vs Plytix: flexible platform or focused PIM?
  52. 52 Salsify vs 1WorldSync: syndication platform or data pool?
  53. 53 Contentserv vs Akeneo: enterprise PIM options compared
  54. 54 Riversand vs Akeneo: MDM-first or PIM-first product data?
  55. 55 SAP MDG vs Akeneo: master data governance or PIM?
  56. 56 Stibo Systems vs Akeneo: MDM suite or PIM platform?
  57. 57 Catsy vs Salsify: product content management for suppliers
  58. 58 Sales Layer vs Akeneo: PIM for fast-growing catalogs
  59. 59 Proplanet vs Claro: manual data services or continuous AI operations?
  60. 60 PIM vs. MDM: Was Hält Ihre Produktdaten Wirklich Korrekt?
Playbooks
  1. 01 Battery Evidence Gap Audit: A Playbook for Industrial Distributors
  2. 02 How to Deduplicate a Product Catalog
  3. 03 Match Supplier Catalog to Inventory: A Step-by-Step Playbook
  4. 04 SDS Readiness Playbook for Multi-Supplier Product Catalogs
  5. 05 Build a Golden Product Record: Step-by-Step Playbook
  6. 06 Auto-Merge Confidence Threshold: How to Set and Tune It
  7. 07 Find Alternative Suppliers in Your Catalog: A Step-by-Step Playbook
  8. 08 How to Find Functional-Equivalent Products Across Suppliers
  9. 09 PIM Migration Deduplication: Migrate Catalogs Without Duplicates
  10. 10 Onboard a New Supplier Range in 24 Hours
  11. 11 Agentic Commerce Readiness Framework for B2B Catalog Teams
  12. 12 Map Supplier Attributes to Your Schema: A Step-by-Step Playbook
  13. 13 Extract Product Specs From PDFs With Full Traceability
  14. 14 Marketplace Catalog Onboarding Checklist
  15. 15 ADR and IATA Product Data Readiness Playbook
  16. 16 Supplier Data Scorecard: How to Build and Run One
  17. 17 Catalog Data Drift: How to Detect and Fix It
  18. 18 EU Packaging Data Readiness Playbook (PPWR)
  19. 19 Standardize AI-Generated Spare Part Short Descriptions
  20. 20 Customs Classification Readiness Playbook for Large Catalogs
  21. 21 PPWR Packaging Data Gap Audit: A SKU-by-SKU Playbook
  22. 22 Validate AI Product Data Before Publishing
  23. 23 Continuous Compliance-Data Monitoring: Keeping Evidence Current as Catalogs Change
  24. 24 Make Your Catalog AI-Search Ready: A GEO Playbook
  25. 25 Is Your Catalog Passport-Ready? The 5-Area Audit
  26. 26 Product Schema Markup at Scale: A Catalog Team Playbook
  27. 27 Fix Google Merchant Feed Errors: A Step-by-Step Validation Playbook
  28. 28 ETIM Classification Workflow for Distributors
  29. 29 Validate ETIM XML Export: A Step-by-Step Playbook
  30. 30 Validate ECLASS in BMEcat: A Step-by-Step Playbook
  31. 31 Validate IES and LDT Photometric Files Before PIM Upload
  32. 32 How to Identify an IEC 60309 Plug From Markings
  33. 33 Identify Thread Diameter and Pitch: A Catalog Enrichment Playbook
  34. 34 How to Extract Safety Data Sheet (SDS) Data for Every SKU
  35. 35 How to Keep Taxonomy and Catalog in Sync as Both Change
  36. 36 How to Group Product Variants Into One Family
  37. 37 How to Merge Overlapping Taxonomies After an Acquisition
  38. 38 How to audit your item master (a 90-minute workflow)
  39. 39 How to Validate SKUs Before They Enter Your Catalog
  40. 40 Why Supplier Codes Never Match Your Catalog—and What Actually Fixes It
  41. 41 The Cold-Start Problem in Supplier Onboarding (and Why It Doesn't Have to Exist)
  42. 42 How to Set Confidence Thresholds for AI Agent Actions
  43. 43 How to Deduplicate a Product Catalog Without Merging the Wrong Parts
  44. 44 Wie Sie Doppelte SKUs im Katalog Vermeiden
  45. 45 Come Eliminare gli SKU Duplicati nel Catalogo
  46. 46 ETIM-Klassifizierung für Elektrogroßhändler: Der Workflow
  47. 47 Lieferantendaten: So Bereinigen Sie Sie vor dem ERP-Import
Articles
  1. 01 Agentic Commerce Runs on Machine-Readable Product Data — and Who Maintains It Is the Open Question
  2. 02 AI Trends in Supply Chain in 2026: From Automation to Governed Decisions
  3. 03 Attribute Completeness Is Becoming a Growth Metric
  4. 04 B2B Ecommerce for Distributors: Why Product Data Is the Real Growth Bottleneck
  5. 05 Your Catalog Now Has Two Customers: Humans and AI Buyers
  6. 06 Agentic AI Is Now a Default in Retail — Which Is Exactly Why It Still Needs Humans in the Loop
  7. 07 Procurement Agents Need Decision Boundaries, Not Just Good Data
  8. 08 How Product Matching Actually Works at Scale (and Why LLMs Alone Aren't Enough)
  9. 09 Product Matching Is Moving From Data Cleanup Into the Quote Desk
  10. 10 AI Data Centers Are Creating a Product Data Problem for Electrical Distributors
  11. 11 AI-Ready Product Data: Why Agents Can't Read Most Catalogs (and What to Fix)
  12. 12 AI Search Has a Citation Problem — Product Data Needs Validation Before It Gets Cited
  13. 13 Amazon Business at $60B: Agentic B2B Procurement Has a Product-Data Problem
  14. 14 GEO for Product Data: When AI Replaces the Browser, Your Catalog Is the Storefront
  15. 15 Product Data Provenance: Why the Source Matters More Than Another Extracted Attribute
  16. 16 Product Matching in Ecommerce: From Duplicate Listings to Trusted Offers
  17. 17 Why Product Compliance Is Usually a Product Data Problem First
  18. 18 AI Won't Replace Your ERP — It Can Finally Make the Data Inside It Trustworthy
  19. 19 E-commerce in Italia nel 2026: 7 implicazioni per retailer, brand e distributori
  20. 20 From Regulations to Product Records: The Data Layer Every Compliance Check Runs On
  21. 21 Your Catalog Is the Context AI Uses — Product Metadata Is Now Operational Infrastructure
  22. 22 The Catalog Is Becoming a Product: Why Industrial Companies Need Unified Catalog Ownership
  23. 23 Bots Now Outnumber Humans Online. The Real Question Is Whether They Can Use Your Catalog.
  24. 24 Amazon vale il 52% dell’e-commerce italiano: cosa significa per i team dati prodotto
  25. 25 Industrial AI Needs More Than Models: The Product Data Infrastructure Beneath It
  26. 26 PPWR Is Live: Now Audit Your Packaging Data SKU by SKU
  27. 27 Why Product Taxonomy Alone Cannot Determine Compliance Requirements
  28. 28 Care Products: la categoria e-commerce più in crescita in Italia ha un problema di dati per la compliance
  29. 29 Why a Compliance Document Repository Is Not Enough
  30. 30 Your AI ERP Project Still Starts With Master Data
  31. 31 E-commerce arredamento in Italia: perché i dati prodotto frenano ricerca, filtri e operazioni
  32. 32 Product Compliance Readiness vs Product Certification: What's the Difference?
  33. 33 When PIM Becomes a System of Action, Product Data Trust Becomes the Control Layer
  34. 34 AI Agents Need Permission Levels. Product-Data Confidence Can Provide Them.
  35. 35 Your ERP Is Getting AI Agents. Your Product Data Is About to Become Executable.
  36. 36 You Deployed AI Search and Relevance Got Worse — The Catalog Was Never Ready
  37. 37 Perché i pure player dominano l’e-commerce italiano: il gap operativo dei retailer multicanale
  38. 38 Entity Resolution Is Becoming Infrastructure for AI Agents
  39. 39 The Missing Execution Layer Between Supplier Data and the Agentic Enterprise
  40. 40 The Hidden Costs & Opportunities of MRO Inventories
  41. 41 Why Industrial Product Data Is Not Ecommerce Content
  42. 42 How to Use AI for MRO and Bring Your Factory Into the Future
  43. 43 The Missing Data Layer Between Supplier Documents and Your PIM
  44. 44 Catalog, Taxonomy, and Attribute Schema Are Not the Same Thing
  45. 45 The Hidden Costs of Manual Marketplace Operations
  46. 46 MRO Spare Parts Intake: The Front Door for Better Factory Data
  47. 47 MRO Sourcing Starts With Better Spare Parts Data
  48. 48 Pricing Errors Are Product Data Errors in Disguise
  49. 49 Product Content Audit for Industrial Catalogs
  50. 50 The Hidden Cost of Product Data Debt in Industrial Distribution
  51. 51 MRO Category Management Needs Product Data, Not Just Spend Data
  52. 52 Product Identity Is the First Compliance Check
  53. 53 AI in Service of Data Quality—and Why Most Distributors Cannot Do What Brickworks Did
  54. 54 From Spec Sheet to Trusted Product Record
  55. 55 One Product, Five Part Numbers: Why Industrial Product Identity Is So Difficult
  56. 56 Spare Parts Data for Equipment Replacement and Maintenance
  57. 57 Why AI Product Enrichment Fails Without Validation and Provenance
  58. 58 How Structured Product Data Powers Replacement and Alternative-Supplier Discovery
  59. 59 What AI Shopping Feeds Reveal About the Future of Product Data
  60. 60 AI Product Discovery Starts With Product Data, Not Search UI
  61. 61 Digital Product Passport Readiness Starts With Product Data Provenance
  62. 62 AI Catalog Enrichment Needs a Production Architecture, Not a Demo Pipeline
  63. 63 The Industrial Catalog Readiness Gap
  64. 64 A QR Code Is Not a Battery Passport
  65. 65 A Distributor Requested ETIM Data. What Should You Do Next?
  66. 66 Your AI Agent Is Only as Reliable as the Data Beneath It
  67. 67 PIM Vendors Are Moving Upstream. Supplier Data Is the New Battleground.
  68. 68 Supplier Master Data Is Becoming a Transaction Control
Guides
  1. 01 Battery and Dangerous-Goods Data Checklist
  2. 02 PPWR Product Data Requirements: What Packaging Data Do You Need for Each SKU?
  3. 03 The Complete Guide to Product Compliance Data Readiness
  4. 04 Speed Up Supplier Onboarding: Why It Takes Weeks and How to Cut It to Days
  5. 05 Cost of Manual Supplier Data Entry: What Distributors Actually Lose
  6. 06 Welche Verpackungsdaten benötigen Sie für die PPWR? Produktdaten-Checkliste für Unternehmen
  7. 07 Why Your PIM Needs an Upstream Product Data Layer
  8. 08 Quali dati servono per la PPWR? Checklist dei dati di imballaggio per prodotto
  9. 09 Supplier Onboarding Checklist for Distributors
  10. 10 Clear a 5,000-SKU Backlog in 90 Days Without Hiring
  11. 11 How to Collect PPWR Packaging Data From Suppliers
  12. 12 PPWR-Lieferantendaten: So sammeln und prüfen Sie Verpackungsdaten von Lieferanten
  13. 13 Fuzzy Matching at Scale Problems: Why Scripts Break and What to Do Instead
  14. 14 How to Assemble the Product Data Needed to Determine Which Requirements Apply
  15. 15 Dati PPWR dai fornitori: come raccogliere e verificare le informazioni sugli imballaggi
  16. 16 Reconcile Supplier Catalogs: A Practical Guide for Distributors
  17. 17 Catalog Matching Cost Savings: A Distributor's Sourcing Guide
  18. 18 How to Match Compliance Documents to the Correct Product and Variant
  19. 19 Battery Passport Readiness for Distributors: 7 Data Gaps That Block Compliance
  20. 20 Duplicate SKUs and Pricing Problems: How to Detect, Merge, and Prevent Them
  21. 21 How to Build a Supplier Compliance Documentation Gap Report
  22. 22 Fashion & Textile DPP: What's Actually Mandatory, and When
  23. 23 How to Prepare a Product Catalog for Automated Compliance Checks
  24. 24 Reversible Product Merge: Deduplicate Your Catalog Without Losing History
  25. 25 Cost of Duplicate Products: The Hidden Margin, Fulfillment, and Analytics Damage
  26. 26 Which Classification Standard Do You Need: ETIM, UNSPSC, or eClass?
  27. 27 Classify an Inherited Catalog: A Practical Workflow
  28. 28 How to Evaluate Product Matching Software: 12 Questions to Ask
  29. 29 Classification Drift: How to Detect, Measure, and Stop It
  30. 30 How to Match Supplier Products Without a Shared GTIN
  31. 31 Complete Product Record Fields: All 58 You Need to Stay Sellable
  32. 32 Product Data Quality Metrics Every Catalog Team Should Track
  33. 33 Why Product Matching Looks Easy in a Demo and Falls Apart in Production
  34. 34 The Canonical Record: Why Matching Is Only as Good as What It Knows
  35. 35 AI Enrichment Hallucination: How to Ground Every Attribute in Source Docs
  36. 36 Why AI-Enriched Product Data Needs Evidence
  37. 37 Competitor Price Monitoring Starts With Product Matching
  38. 38 Fill Missing Product Attributes With Provenance
  39. 39 Product Onboarding Software Buyer's Guide
  40. 40 Trust AI-Generated Product Data: A Practical Validation Framework
  41. 41 How to Optimize a Product Taxonomy With Attributes
  42. 42 AI Output Provenance: Why Every AI Enrichment Needs a Source Link
  43. 43 Per Piece, per Pallet, per m²: Why Building-Materials Catalogs Break on Unit of Measure
  44. 44 Product Compliance Readiness Scorecard
  45. 45 Product Data Quality: Seven Dimensions and a Practical Scorecard
  46. 46 The Declaration of Performance You Cannot Find: The Compliance-Evidence Gap in Construction Catalogs
  47. 47 Human in the Loop Data Review for Product Catalogs
  48. 48 Product Compliance Evidence Matrix
  49. 49 Product Data Cleansing: Workflow, Checklist, and Before-and-After Example
  50. 50 Product Catalog Management Software: A Multi-Supplier Buyer's Guide
  51. 51 SDS Validation Checklist for Product Data Teams
  52. 52 ChatGPT Product Recommendations: Why Competitors Appear and You Don't
  53. 53 GEO for Ecommerce Catalogs: Make Your Products Citable by AI Engines
  54. 54 Item master data management: a practical guide for distributors
  55. 55 PIM–ERP Integration: Ownership, Synchronization, and Write-Back
  56. 56 Supplier Documentation Gap Report Template
  57. 57 How AI Shopping Agents Work: Retrieve, Rank, and Verify
  58. 58 Product master data management (PMDM): what it is and when you need it
  59. 59 The MDM data model for product catalogs, explained
  60. 60 Product Data AI Search Visibility: What Your Catalog Needs to Get Cited
  61. 61 Manufacturer Price Update Cost: What Distributors Actually Spend
  62. 62 Supplier master data management: one supplier, one record
  63. 63 Margin Leakage in Supplier Price Files: How to Catch It Before It Ships
  64. 64 Product MDM without an MDM platform
  65. 65 Monitor Competitor Prices Without Drowning in False Alerts
  66. 66 ERP to Ecommerce Data Gap: Bridge 150k SKUs to a Live Storefront
  67. 67 ERP Integration Gaps in Product Data (and How to Close Them)
  68. 68 Manage Multichannel Product Feeds from One Source of Truth
  69. 69 Barcode Errors in Supplier Feeds: A Field Guide to Finding and Fixing Them
  70. 70 The cost of a dirty item master
  71. 71 Catalog Launch Errors: 7 Field-Level Failures That Bounce Feeds
  72. 72 How to Clean Up the Item Master in NetSuite
  73. 73 Vertical SaaS Catalog Data: Why You Inherit the Chaos and How to Stop It
  74. 74 Build vs Buy Catalog Data API: A Platform Team Decision Guide
  75. 75 HS Code vs HTS Code: What's the Difference?
  76. 76 Master data governance for product data: the lightweight version
  77. 77 SAP Material Master Data: Fixing Duplicates and Missing Attributes
  78. 78 Apparel HS Code Classification Guide: Knit vs Woven, Fiber Type, and Gender
  79. 79 Deterministic Product Enrichment API: Choosing Traceable Over Black-Box
  80. 80 Do I Need a PIM? A Decision Guide for Multi-Supplier Catalog Teams
  81. 81 Keeping the item master clean after ERP go-live
  82. 82 U.S. HTS vs EU CN/TARIC: Where Tariff Schedules Diverge
  83. 83 When a PIM Is Overkill: A Sizing Guide for Catalog Teams
  84. 84 HS Codes for Pharmaceuticals and Medical Devices
  85. 85 Product hierarchy in MDM: families, variants, and where they break
  86. 86 6 Best HS Code Lookup Tools for Importers and Exporters
  87. 87 Does a Manufacturer Actually Need a PIM?
  88. 88 MDM implementation for mid-market distributors: what to skip
  89. 89 The 40 item-master fields that actually matter
  90. 90 Why Manufacturers Lose Channel Revenue to Bad Product Data
  91. 91 Your PIM Isn't the Problem — Your Inbound Supplier Data Is
  92. 92 How to Build a Product Evidence Graph from PDFs, ERP and Supplier Files
  93. 93 How to Choose a PIM (When You Actually Need One)
  94. 94 Internal master data vs customer-facing product content
  95. 95 MDM ROI: how to build the business case for clean product data
  96. 96 Keeping Product Data in Sync Between Shopify and Your ERP/PIM
  97. 97 Product Data Management for Plumbing Distribution
  98. 98 Wholesale Fastener Product Data: A Distributor's Guide
  99. 99 Product Data Import in Dynamics 365: Avoiding Duplicate Records
  100. 100 SAP Business One + Ecommerce: Closing the Product-Data Gap
  101. 101 Automated vs Manual Product Data Management
  102. 102 Brauche Ich Wirklich ein PIM? Entscheidungshilfe
  103. 103 How Manufacturers Get Products Onto Grainger Faster
  104. 104 Ho Davvero Bisogno di un PIM? Guida alla Decisione
  105. 105 Why Product Data Belongs in a Knowledge Graph, Not a Document Folder
  106. 106 Dati Fornitore: Come Pulirli Prima dell Import in ERP
  107. 107 SAP-Stammdatenqualität: Duplikate und Fehlende Attribute Beheben
  108. 108 Supplier Data Integration Platforms: What to Evaluate
  109. 109 Warum Ihr PIM Nicht das Problem Ist
  110. 110 When Your Product Catalog Outgrows Excel