A Practical Guide to Cleaning Product Information
From Rough Data to Smooth Sailing
- Ceejay S Teku
- September 2, 2025
- 12:13 am
Table of Contents
Introduction – Why Clean Data is Your Compass
Is your product catalog moving along at a steady clip, or is it drifting off course?
If your data quality is lacking, you may find that you’re experiencing costly delays, customer confusion, and overall operational inefficiency. That’s because poor data will hurt your search visibility and ground your feeds, creating cascading problems across each of your channels.
In reality, most product catalogs accumulate quality issues over time, like barnacles on your ship’s hull. The individual problems appear small when they first appear. But collectively, they’re a shipwreck waiting to happen.
A missing dimension here, an inconsistent unit there, an outdated image somewhere else … These small issues compound into major obstacles that affect everything from warehouse operations to customer satisfaction. Listing rejections, product returns, and downranked listings are just a few examples.
So what’s the solution? It’s simple: systematic application of simple principles: consistency, completeness, and accuracy are key to your success.
Thankfully, there’s a clear, repeatable approach to clean and trustworthy product data. This guide will outline practical steps that your teams can implement over time.
1. Spotting Choppy Waters (Comprehensive Data Quality Assessment)
Before charting a course toward cleaner data, you’ll need to understand your specific problems. Data quality issues rarely distribute evenly across product lines, categories, or information types.
Let’s take a look.
Missing Attributes and Information Gaps:
The most obvious data quality problems arise from missing information. These gaps often appear in customer-facing, decision-prompting information that your customers view. Inconsistent data may also lead to listing rejection from your channels.
Missing attributes may impact your business in a number of ways. For instance, if a customer can’t view your dimensions, colors, or materials, they’re less likely to purchase. Similarly, compliance gaps like missing safety warnings or certifications can prevent your products from being listed in entire regions.
Fortunately, the pattern of missing information often reveals systemic issues, not random gaps. For example, products from certain suppliers may be consistently lacking in specific attributes. In the same way, legacy products may be missing attributes that are no longer systematically captured.
Formatting Inconsistencies and Standards Drift:
Inconsistent formatting isn’t just ugly, it’s problematic. Formatting issues can affect your search functionality, feed compatibility, and operational efficiency.
When similar information appears in different formats throughout your catalog, it causes confusion. Unit inconsistencies, for example, can impact customer understanding. Deviations can also reduce the efficacy of your filter and search functions.
Text formatting inconsistencies affect professional appearance and search performance. Product names with inconsistent capitalization, descriptions with varying punctuation styles, or specifications formatted differently across similar products can create an unprofessional impression … This undermines customer confidence.
Duplicate Products and Variant Management:
Product duplication causes confusion for your customers, but it also complicates your inventory management and your retail feeds. This issue is commonly caused by multiple data sources, from system migrations, or from unaudited product creation processes.
The simplest duplication problems are true duplicates: identical products that are listed multiple times.
More complex issues involve slight variations in product information for the same item, creating uncertainty about which version contains accurate information and which should be maintained or eliminated.
If you have product variants that are related but appear as separate items, this creates additional complexity. Color variations, size options, or packaging alternatives that aren’t properly structured make it difficult for customers to understand available choices
Outdated Information and Content Drift:
Your product information becomes outdated over time, but the processes you use for identifying and updating this info may not be able to keep up.
Do you have pricing information that no longer aligns with your strategy? Old images that display a previous version of packaging? Are there discontinued model numbers that appear in your descriptions?
This is undermining your credibility.
Marketing information requires regular updates to keep you relevant and effective, but content refresh cycles often don’t align with product evolution, seasonal considerations, or competitive positioning changes.
Operator Prompt: Conduct a systematic audit of your top 50 performing SKUs across key categories. Document the missing critical fields, formatting inconsistencies, potential duplicates, and obviously outdated information. Then, identify the three most common recurring issues that appear across multiple products. These represent your highest-priority targets for improvement.
2. Building Your Data Map – Categories and Comprehensive Attribute Management
To effectively clean your data, you’ll need to understand your product information architecture as a navigational system. Each category needs clearly defined routes to their destinations, whether these be sales channels, customer touchpoints, or your own operations.
Core Attribute Identification and Prioritization:
Each product category requires specific information to support customer decision-making, operational efficiency, and channel compatibility. With that said, not every attribute carries the same level of importance.
Identify your core attributes – the ones that drive your business. These may be physical specs, performance specifications, or regulatory compliance.
Marketing attributes like key benefits, target applications, and competitive differentiators support customer understanding and search visibility, but their core attribute status depends on how customers research and compare products within specific categories.
Once you’ve put your finger on what matters, tweak your information to deliver maximum output.
Mandatory Versus Optional Field Classification:
Which attributes are mandatory and which are optional?
By differentiating between the two, you enable efficient QA processes that focus attention on information gaps. These gaps can prevent your business from achieving objectives.
So, what should you look for?
Well, mandatory attributes should include all the information that’s required by your most important sales channels. This is the data that your customers need to establish confidence and that you need to supply to ensure regulatory compliance.
On the other hand, optional attributes provide enhancement value that improves customer experience, search performance, and operational efficiency… but it’s not data that’s strictly necessary for basic business functionality.
The mandatory versus optional classification should reflect actual business impact rather than theoretical ideals. Attributes that seem important but don’t measurably affect conversion rates, operational efficiency, or channel acceptance may deserve to be categorized as optional rather than mandatory.
Logical Attribute Organization and Relationship Management:
Grouping related attributes into logical clusters improves both data management efficiency and customer information presentation. Tech specs, marketing content, operational logistics, and regulatory compliance information, for instance, all serve different purposes. These categories benefit from separate organizations that will reflect their distinct roles.
Technical specification clusters should group related performance data, physical characteristics, and compatibility information that customers evaluate together during product assessment.
Marketing content clusters should organize messaging, positioning, and promotional information that supports brand presentation and customer engagement.
Operational clusters should group information needed for fulfillment, inventory management, and channel distribution processes. This organization enables efficient operational access while keeping operational details separate from customer-facing information.
System Configuration and Implementation Support:
If your catalog is managed in Catsy, you can implement mandatory attribute requirements for each category and configure automated flagging for missing critical information. This systematic approach enables proactive data quality management rather than reactive problem-solving when issues arise.
Your configuration approach should reflect your designations of either mandatory or optional. Focus your automated alerts on truly critical gaps, but maintain visibility into your optional attributes’ completeness.
3. Quick Wins: Strategic Standardization Without Operational Overload
Data cleaning initiatives are successful when they deliver visible improvements quickly enough to maintain team momentum.
So how do you implement strategic standardization?
Unit Standardization and Measurement Consistency:
Unit inconsistencies create customer confusion, operational complications, and technical problems with feeds and integrations. Standardizing measurement units across your catalog provides immediate clarity while establishing the foundation for more advanced data quality improvements.
Choose standard units that align with your primary market expectations and channel requirements. US-focused catalogs typically benefit from imperial unit standardization, while international operations often require metric. Consistency is the key!
As you standardize, focus first on customer-facing attributes where inconsistency creates the most confusion. Then, extend that standardization to operational attributes that affect fulfillment and inventory management.
Document, document, document! Track your unit standards clearly and implement validation processes that prevent future inconsistencies.
Text Formatting and Presentation Standards:
Consistent text formatting creates professional presentation while supporting search functionality and feed compatibility. That could mean tasks as simple as standardizing your capitalization and punctuation.
Title case standardization for product names creates professional appearance and consistent search behavior. Choose a specific title case approach, such as capitalizing major words while keeping articles and prepositions lowercase, then apply it systematically across all product names.
Description formatting standards should address paragraph structure, bullet point usage, and punctuation. Standard formatting improves readability while ensuring compatibility with different presentation contexts across sales channels.
Specification formatting standards should address unit presentation, range formatting, and technical detail organization. Consistency improves customer understanding and enables better comparison between similar products.
Digital Asset Organization and Naming Conventions:
Systematic digital asset organization prevents confusion, improves workflow efficiency, and supports automated processes that depend on predictable file naming and organization patterns.
Image naming conventions should incorporate product identifiers, image types, and sequence information in predictable formats. For example, “SKU123_front_01.jpg” provides clear identification that supports both your teams’ workflow and automated processing.
Asset organization should separate different image types: primary product shots, lifestyle images, detail views, and packaging shots.
Don’t forget your file extensions! File format standardization ensures compatibility across different systems and presentation contexts while optimizing file sizes for web performance and storage efficiency.
Implementation Tools and Shortcuts:
Spreadsheet functions like UPPER(), PROPER(), TRIM(), and find/replace operations allow you to quickly clean up common formatting issues before data import. These simple tools can address thousands of records quickly!
Regular expression tools provide more sophisticated pattern matching and replacement capabilities for complex formatting standardization. Simple spreadsheet functions cannot handle this efficiently.
Tip: Focus standardization efforts on the most visible customer-facing attributes first, then extend systematic standards to operational and technical attributes.
4. Prioritizing the Most Important Fields for Maximum Impact
Effective data cleanup requires strategic focus on attributes that deliver the greatest business impact. Remember that not all missing data affects your business the same! Focus on the changes that will drive measurable results.
Conversion-Critical Attributes and Customer Decision Factors:
Research consistently shows that certain product attributes disproportionately affect customer purchase decisions across different categories. The most important are dimensions, colors, materials, and key performance specifications.
Missing or inaccurate dimension information creates problems because your customers want to make an informed decision. Incorrect information leads to questions about fit, compatibility, or suitability for their specific application.
Incomplete dimension data also affects you! Shipping cost calculations and fulfillment operations are impacted, creating problems that extend beyond customer experience to operational efficiency.
Color, finish, material composition, and other attributes will prevent your customers from making an informed decision. This creates uncertainty and increased bounce rate. Those who do purchase are more likely to initiate a return or leave a negative review.
Compliance and Regulatory Requirements:
Regulatory compliance attributes are non-negotiable. If your products are listed incorrectly, your platforms may delist you. This includes safety warnings, certifications, country of origin, and other compliance information – fix this to avoid legal risks or channel restrictions.
Safety information is critical for products that may present a hazard, such as electrical components. Failing to include this information opens your teams up to legal action.
Certification information impacts market access as well as customer confidence.
Country of origin and manufacturing information affects tariff calculations, supply chain transparency, and customer preferences.
Search and Discoverability Optimization:
Keywords within your product titles and descriptions directly affect search visibility across e-commerce platforms and marketplaces. Missing or poorly optimized search-related content reduces product discoverability … and limits your sales potential.
As you optimize your product titles, include primary keywords that customers would organically search. Use titles that accurately describe products – this isn’t the time to get creative!
Your descriptions are next. Optimize your descriptions with secondary search terms, application keywords, and language that focuses on the benefits of your product. Of course, you’ll also want to include comprehensive product information!
Finally, category and attribute classification affects algorithmic recommendations, filter functionality, and comparative shopping features. This helps customers find and evaluate products. Incomplete or inaccurate classification reduces product visibility and limits recommendation algorithm effectiveness.
Channel-Specific Critical Requirements:
If you sell across multiple channels, you’ve likely noticed that each channel prioritizes different attributes. This may be based on their own operational requirements, their customer expectations, and their competitive positioning.
Marketplace requirements often focus on standardized attributes that support comparison shopping, search filtering, and automated categorization. Amazon and eBay, for example, have specific attribute requirements that affect your SKUs’ search visibility and eligibility for promotional programs.
Retail partner requirements usually emphasize operational attributes like case pack quantities, shipping dimensions, and inventory management information. This is displayed alongside customer-facing attributes that support presentation.
Direct-to-consumer requirements, on the other hand, often emphasize brand storytelling, detailed specifications, and lifestyle information that supports premium positioning and customer education.
Operator Prompt: Create a comprehensive priority attribute matrix for your top three product categories. List the attributes in order of business impact. Consider conversion influence, compliance requirements, search optimization, and channel-specific needs. Then, focus your initial improvement efforts on the top five attributes per category instead of attempting comprehensive improvement across all information types simultaneously.
5. Using AI as a Deckhand – Strategic Automation with Human Oversight
Done right, AI can make your data cleaning processes faster. The key is to identify which data quality improvements should be automated – and which require a human’s strategic thinking.
Content Generation and Enhancement Support:
AI is phenomenal for generating first-draft content from your structured product data. These descriptions, bullet points, and specs are consistent and easy to refine and approve. Your teams can focus on refining content to reflect your brand’s voice, not updating dimensions.
AI will do its job effectively when it’s presented with clear templates and examples. These templates should demonstrate your preferred structure and voice, as well as designate your priorities.
Once AI understands your patterns, it can generate useful first drafts that only require minimal editing.
Generation of bullet points from your detailed specs allows for scannable content that improves your customer’s understanding. Use AI to convert technical data into customer-friendly bullet points.
Finally, meta description and search optimization content can be easily generated based on your target keywords and your prioritized attributes. This supports your SEO and your visibility across channels.
Formatting Standardization and Consistency Application:
AI can systematically apply formatting standards across large catalogs, ensuring consistent unit presentation, capitalization, punctuation, and structural organization that would otherwise require a lot of manual work – and payroll hours.
Title formatting standardization can be applied across thousands of products simultaneously, using consistent capitalization, punctuation, and structural organization. This creates professional presentation and improves search performance.
Specification formatting can ensure consistent unit presentation, range formatting, and technical detail organization across related products.
Description formatting standardization can implement consistent paragraph structure, bullet point usage, and punctuation patterns that improve readability and professional appearance across your entire catalog.
Translation and Localization Assistance:
AI is invaluable to your global business. It handles the initial translation work – your human reviewers can verify and refine the content.
Basic translation capabilities are ideal for tech specs, dimensions, and structured product information. AI will follow predictable patterns across languages, then your human teams can review for cultural appropriateness and accuracy.
Localization support includes currency conversion, unit standardization for different markets, and adaptation of product descriptions for regional preferences or regulatory requirements that vary across each of your international markets.
Content Optimization and Enhancement Suggestions:
AI can analyze existing product information and suggest improvements based on completeness, keyword optimization, and competitive analysis that help prioritize enhancement efforts efficiently.
Gap identification capabilities can systematically review catalogs for missing attributes, incomplete descriptions, or optimization opportunities that your team might overlook due to catalog size or complexity.
Keyword optimization suggestions can identify opportunities to improve search visibility by incorporating relevant terms that customers use when searching for similar products.
Implementation Guidelines and Quality Control:
If your product data is stored in Catsy, you can configure AI prompts for improvements while maintaining review-first workflows.
Focus your approach on your low-risk applications first, building your confidence before you expand your AI assistance to customer-facing applications.
Quality control procedures should include systematic review of AI-generated content, feedback loops that improve AI performance over time, and clear rollback procedures if automated changes create unexpected problems. Remember – AI isn’t perfect!
Strategic Limitations and Human Oversight Requirements:
AI can’t make strategic decisions for you. Brand positioning, product priorities, and market-specific messaging are your responsibilities. For that reason, human oversight is still critical!
Final approval processes should continue to fall on experienced team members who understand brand standards, customer expectations, and competitive positioning factors.
6. Setting Up Data Health Checks (Sustainable Quality Management Systems)
Creating clean data isn’t a one-time event. It requires ongoing maintenance to be sustainable. As you continually improve, you’ll identify problems early and prevent regression in your quality.
Monthly Core Attribute Monitoring:
Review your mandatory attribute completeness monthly. This will allow you to catch problems before they impact your customer or your channel performance.
The monthly review should focus on attributes that directly affect conversion rates, channel acceptance, or operational efficiency. Don’t attempt a complete overhaul – that’s overwhelming and simply not sustainable.
Automated reporting capabilities should highlight missing critical attributes, products with incomplete core information, and categories experiencing systematic quality regression that requires attention.
Quarterly Comprehensive Catalog Assessment:
Quarterly, you’ll want to conduct a more thorough review. Assess your formatting consistency, the freshness of your content, and your competitive positioning. This helps identify opportunities for improvement in a way that your monthly audits can’t.
These quarterly reviews should include an assessment of discontinued products, outdated marketing content, competitive positioning updates, and seasonal content refresh requirements.
Your quarterly assessment should also evaluate data quality trends, improvement project effectiveness, and resource allocation priorities for upcoming improvement initiatives.
Channel Export Validation and Error Monitoring:
Monitor your channel export errors and feed rejections – these indicate issues with your data quality. By doing this, you’ll receive an early warning flag for problems that may eventually impact your sales performance.
GDSN submission errors, marketplace feed rejections, and retailer onboarding delays often indicate systematic data quality issues that can be addressed proactively, reactively.
Seasonal and Campaign-Driven Quality Checks:
Schedule a data quality review before each major selling season! This will ensure that you’re catalog-ready when you reach a critical business period.
Pre-holiday catalog preparation should include content freshness assessment, seasonal keyword optimization, gift-focused messaging updates, and inventory data validation that supports peak season performance.
New channel launch preparation may require comprehensive data quality assessment that’s focused on channel-specific requirements.
Process Documentation and Team Training:
Make sure your data is accessible. Data quality standards, review procedures, and improvement processes should all be ready for your team members to put to use.
Don’t forget onboarding! Your training materials should address common data quality issues (and how to prevent them).
Technology Integration and Workflow Optimization:
If your catalog is managed in Catsy, configure dashboard reminders, automated reports, and workflow alerts that support smooth quality management.
Include critical quality metrics in your dashboard configuration, and list the areas that require attention in easy-to-assess formats.
Your automated reporting should focus on actionable information that guides your workflows. Keep your data cleanup manageable!
7. Practical Workflow Template for Systematic Implementation
Our workflow template provides structure for a comprehensive approach to data quality management.
Step | Task | Owner | Frequency | Success Criteria | Tools/Resources |
1 | Generate missing attribute report for top categories | Data Operations | Monthly | <5% gaps in critical fields | Catsy reporting, Excel analysis |
2 | Review unit standardization and formatting consistency | Data Operations | Quarterly | 95% format compliance | Standardization checklist, bulk editing tools |
3 | Audit discontinued SKUs and update product status | Product Management | Quarterly | Zero discontinued products in active feeds | Inventory integration, lifecycle management |
4 | Validate channel-specific field requirements | E-commerce | Before peak seasons | 100% channel compliance | Channel requirement matrices, export validation |
5 | Assess content freshness and competitive positioning | Marketing | Quarterly | Content less than 12 months old | Competitive analysis, content audit |
6 | Execute AI-assisted content generation and review | Content Team | Monthly | 50% reduction in manual content creation time | AI tools, approval workflows |
7 | Monitor feed errors and retailer rejection patterns | Technical Operations | Weekly | <2% error rate on channel submissions | Error logs, retailer feedback systems |
8 | Update regulatory compliance and certification information | Compliance | Quarterly | 100% compliance with current regulations | Regulatory databases, certification tracking |
Detailed Task Implementation Guidelines:
Missing Attribute Analysis:
Your monthly missing attribute report should focus on fields that directly affect your customer or your channel acceptance. Use automated reporting to identify patterns that suggest issues with your overall process, not just your data.
Standardization Review Procedures:
Your quarterly formatting review should focus on a few representative products from each category. Document your decisions for standardization, then create reference guides that are consistent for each of your teams.
Product Lifecycle Management:
As you audit your discontinued products, assess related products and strategies. Your plan is to minimize customer disruption while maintaining accuracy.
Coordinate these lifecycle updates with marketing campaigns, inventory management, and channel communication to ensure consistent information across all touchpoints.
Channel Compliance Validation:
Pre-season channel validation should include testing with small product samples before you submit to catalogs. Identify errors and enable correction without impacting your whole catalog.
Of course, don’t forget to document! Maintain channel requirement documentation that reflects current specifications and any recent updates that affect your submission success rates.
Performance Measurement and Optimization:
Track the changes that have led to improvement in your business, not just completion rates. Conversions, a reduction in errors, and your channel acceptance rates are examples of these metrics.
8. Avoiding Over-Correction and Maintaining Operational Balance
Many companies fail in their initiatives to improve data. That’s because of overly ambitious, perfectionist approaches.
Sustainable improvement requires focused baby steps combined with realistic expectations.
Strategic Category Prioritization:
Focus your initial improvement efforts on categories that bring you the most money. You may also choose those that are most visible to your consumers, or that have the strictest distribution requirements across channels.
Your top-performing categories typically justify more intensive efforts because they directly impact your revenue. As a bonus, these categories will provide you with clear metrics for success.
Emerging or strategic categories may warrant attention based on growth potential, competitive positioning, or channel expansion opportunities even if their current performance doesn’t justify using extensive resources.
Source-Level Problem Resolution:
Did you find data quality issues? Address them at the source. Your ERP systems, supplier data feeds, and your product development processes are a good start. Beginning here will decrease the frequency of occurrences in the future.
Supplier data improvement often provides the highest return on investment because it prevents problems across multiple products while reducing ongoing maintenance requirements. Work with key suppliers to improve their data collection and submission processes.
Internal process improvements prevent quality issues from being introduced. These improvements also reduce the ongoing correction effort required to maintain your catalog standards.
Documentation Standards and Process Institutionalization:
When it comes to documentation, keep it simple. A one-page style guide is often much more effective than a multi-page manual. Your team members will thank you!
Style guide documentation should address the most common decisions affecting consistency and quality while avoiding confusing, excessive detail.
Your documentation should focus on repeatable procedures, decision frameworks, and escalation guidelines that empower each of your teams.
Resource Allocation and Timeline Management:
Allocate resources based on a realistic assessment of your available time, competing priorities, and sustainable work levels. In other words, don’t burn out your teams.
Your improvement timelines should reflect actual work capacity, not optimistic estimates. Aiming too high can create undue pressure and is almost sure to increase the likelihood of error. Sustainable improvement is key.
Technology Integration Without Over-Dependence:
Use your available tech stack to support improvement efforts. There’s no need to reinvent the wheel or create problems that require long-term maintenance.
Simple tools and processes often prove more sustainable than sophisticated solutions that require extensive training, maintenance, or troubleshooting.
9. Preparing for Multi-Channel Export and Distribution Success
You can’t expand your business with dirty data. Clean, well-structured information is the foundation for distribution. This allows for consistent presentation as well as increased compliance with multichannel requirements.
Universal Attribute Standards and Channel Flexibility:
Your core attribute standards should meet requirements across your most important sales channels while maintaining the flexibility needed to optimize for channel-specific requirements.
Universal standards should focus on information that all channels require or that customers expect consistently, such as basic specifications, safety information, and core product details that affect purchase decisions.
Channel-specific optimization should enhance universal standards, adding information or formatting that improves performance on specific platforms. It should not create confusion or inconsistency across your overall catalog presentation.
GDSN Compliance and Retailer Requirements:
Each of your core product attributes should align with GDSN standards as well as with major retailer requirements. This can reduce submission delays and will lessen ongoing maintenance requirements.
GDSN attribute mapping should address mandatory fields, formatting requirements, and validation rules that affect successful data submission and retailer acceptance.
rather than handled reactively when submission problems arise.
Marketplace Optimization and Search Performance:
Structure product information to support search visibility and customer experience across major marketplaces, but keep your brand’s “flavor!”
Marketplace search optimization should incorporate platform-specific keyword strategies, attribute completion with the goal of improving your discoverability.
Competitive analysis should inform optimization strategies that differentiate your products while also meeting marketplace algorithm requirements and customer search patterns.
Direct-to-Consumer Channel Integration:
Ensure that your product data supports your own e-commerce platform optimization and contributes to your brand’s storytelling!
Direct-to-consumer optimization often requires more detailed product information, lifestyle content, and brand messaging that supports premium positioning and customer education beyond basic marketplace requirements.
Cross-channel consistency should maintain brand integrity while enabling optimization for different customer contexts.
System Configuration and Workflow Efficiency:
If your catalog is managed in Catsy, configure your attribute mapping and export templates to enable efficient multi-channel distribution. This should be done without the need for extensive manual adaptation or custom formatting requirements.
Automated mapping capabilities should handle routine formatting but be flexible enough to adapt to improve performance on specific channels.
Export workflow efficiency should minimize manual effort while maintaining quality control and brand consistency across each customer touchpoint.
Conclusion – Set Your Course and Stay the Course
There’s nothing glamorous about cleaning up your product data, but it’s the reliable anchor for each and every sales channel. Much like maintaining a seaworthy vessel, keeping on top of your data requires ongoing attention.
The most successful data quality initiatives start small! Your team will focus on high-impact improvements and build sustainable processes that will withstand the evolution of your business.
The systematic approach we outlined in this guide provides a solid, proven start to incremental improvement – even for businesses with limited resources!
Regular health checks, automated quality monitoring, and strategic use of available tools create sustainable improvement momentum without overwhelming your team.
