Product Data Quality Checklist: The 30 Fields That Drive Sales + Visibility

Product Data Quality Checklist: Why Field-Level Validation Matters in 2026

A product data quality checklist is no longer just a nice-to-have feed hygiene step. In 2026, it is a direct revenue lever. Research consistently shows that poor product data costs ecommerce businesses up to 25% of annual revenue through feed disapprovals, lost search visibility, and customer returns driven by inaccurate listings. The rise of AI-powered shopping experiences and increasingly strict channel requirements make field-level data quality more critical than it has ever been.

The real challenge is not knowing which fields matter. Most teams already understand that. The challenge is maintaining consistency across thousands of SKUs sourced from multiple suppliers, each with different naming conventions and attribute structures. Without a structured validation framework, data quality degrades with every catalog update.

This checklist is designed for ecommerce operations, catalog, and feed management teams. It covers six field groups that together determine whether a product gets approved, found, clicked, and purchased. If your team is still dealing with inconsistent supplier inputs, our guide on how to standardize supplier product data with AI is a practical companion to this piece.

The Six Dimensions of Product Data Quality

Before diving into specific fields, it helps to anchor your quality program around established data quality dimensions. These six form the foundation of any effective catalog validation framework:

Completeness means every required and recommended field is populated for the destination channel. Missing attributes are one of the most common causes of feed disapprovals, with GTIN-related gaps alone accounting for over 5% of product feed errors.

Accuracy ensures the data reflects the actual product. A color field that says "blue" when the product is navy, or a weight listed in pounds instead of kilograms, erodes trust and increases returns.

Consistency requires that values are uniform across all channels and internal systems. When your PIM says one price and your feed sends another, disapprovals follow quickly.

Timeliness means data is current. Stale stock levels, expired promotional prices, and outdated landing page URLs are among the fastest ways to get listings suppressed.

Validity confirms that values meet the structural rules of each field: correct data types, allowed value lists, proper formatting of GTINs, and ISO-compliant date formats.

Uniqueness prevents duplicate records from polluting your catalog, confusing channel algorithms, and splitting sales history across redundant SKUs.

For teams that want a deeper look at attribute-level enrichment, see our guide to building sellable listings through attribute enrichment.

The 30 Fields Organized by Function

Rather than treating all fields equally, group them by function so different team members can own and validate their domain. Here is the breakdown:

Identity Fields (Fields 1-6)

These fields determine how a product is recognized and matched across systems.

• Product title is the single most impactful field for search relevance and click-through rate. Follow structured product title templates by category to keep them consistent.
• Brand should come from a normalized dictionary with no abbreviations or typos. This directly affects identifier logic and trust signals.
• Product type and category mapping support merchandising, campaign segmentation, and channel taxonomy requirements. Map from your canonical taxonomy to each channel's specific values.
• Variant parent ID keeps product families connected. Every child SKU must reference a valid group identifier.
• Variant attributes (color, size, material) differentiate offers and reduce returns when accurately populated.

Content Fields (Fields 7-9)

Product descriptions and feature bullets are where conversion happens. Generic or thin content underperforms consistently.

• Short descriptions should be concise and factual, giving shoppers enough context without channel-policy violations.
• Long descriptions must include material specifications, use cases, and relevant constraints. Our guide on writing product descriptions that sell covers the patterns that actually move conversion metrics.
• Key features or bullet points improve scannability on marketplace listings. Lead with benefits, remove promotional filler.

Attribute Fields (Fields 10-15)

Structured attributes power filtering, comparison, and algorithmic matching.

• Material, color, and size are the most common filter attributes and must use controlled vocabularies rather than free-text entries. The difference between "stainless steel" and "SS" or "steel, stainless" may seem minor, but it fragments filtering and breaks feed validation.
• Weight and dimensions affect shipping calculations and customer expectations. Store as numeric values with explicit units.
• Compatibility model is critical for accessories and replacement parts. Validate against approved compatibility lists.
• Condition (new, used, refurbished) is required by many channels and subject to policy enforcement.

Media Fields (Fields 16-20)

Images are the top conversion factor in ecommerce, yet media quality is one of the most common sources of feed disapprovals.

• Main image URL must resolve to a crawlable, high-resolution image with the product clearly visible against a compliant background.
• Additional images should show multiple angles, scale, and real-use context. Moving from one image to a richer media set reliably improves conversion.
• Image compliance means no watermarks, promotional overlays, or obstructing text. Channel enforcement is becoming stricter in 2026.
• Aspect ratio consistency creates a polished storefront experience across collections and search results.
• Video URLs, where supported, increase confidence for complex or high-consideration products.

Commercial Fields (Fields 21-26)

Price and availability mismatches remain among the most common feed errors. Platforms require exact parity between your feed, product detail page, and checkout.

• Landing page URL must resolve to the correct variant without redirect loops or 404 errors.
• Price and sale price need real-time synchronization. Expired sale values or mismatched currencies are frequent causes of listing suppression.
• Availability should reflect near-real-time inventory. Overselling or advertising out-of-stock items damages both channel standing and customer trust.
• Availability date is required for preorder or backorder scenarios and must follow ISO date formatting.

Identifier Fields (Fields 27-30)

Product identifiers are the backbone of cross-system operations and channel matching.

• SKU must be unique per variant and immutable once published.
• GTIN, UPC, or EAN improves matching accuracy and is required by many channels and categories. Validate checksum and length, and always source from the manufacturer. For listings where these are missing, see our guide on fixing missing EAN and GTIN issues.
• MPN plus brand logic serves as the required fallback when GTIN is absent.
• Channel custom labels enable campaign segmentation and performance reporting but should follow a governed taxonomy.

Building a Validation Workflow That Scales

Knowing which fields to check is only half the problem. The other half is embedding validation into your daily operations so quality does not degrade between audits. The most effective teams in 2026 treat data quality as a continuous process, not a periodic cleanup project.

A practical workflow follows five stages:

1. Ingest and normalize. Map all supplier inputs into one canonical schema before any transformation or export. This is where tools like Lasso deliver immediate value, standardizing messy multi-supplier data into a clean, unified product model automatically.

2. Validate by field group. Run automated checks separately for identity, content, attributes, media, commercial data, and identifiers. Different field groups have different owners and different validation cadences.

3. Score and gate. Assign each SKU a publish-readiness score based on field completeness and accuracy. Block channel export when critical fields fail. This prevents the costly cycle of submission, rejection, correction, and resubmission that drains team bandwidth.

4. Transform for each channel. Generate channel-specific outputs from the same approved base record. Your canonical model stays clean while the mapping layer handles each platform's requirements.

5. Monitor post-publish. Track disapprovals, suppressions, and warning trends, then route issues back to the responsible field owners. The feedback loop is what turns a one-time checklist into a sustainable quality program.

Before pushing any feed live, run through the validation steps in our feed QA checklist to catch the errors that automated rules sometimes miss.

Operational Metrics Worth Tracking Weekly

A checklist without measurement is just a document. These KPIs connect data quality work to business outcomes:

• Critical-field completeness rate across the 30 fields, segmented by category and channel
• Feed disapproval and suppression rate by platform, tracked as a trend over time
• Median time from product intake to publish-ready status, which directly reflects operational efficiency
• Identifier coverage: percentage of SKUs with valid GTIN or complete MPN-plus-brand fallback logic
• Revenue exposure from unpublishable SKUs, quantifying the business cost of quality gaps

Teams using Lasso for automated enrichment and validation typically see the most immediate improvement in completeness rate and time-to-publish, because the platform handles the repetitive normalization and gap-filling work that otherwise consumes hours of manual effort each week.

How to Implement This Checklist in 30 Days

You do not need a full-scale replatforming project to get meaningful results. Most teams can operationalize this checklist in one focused month.

Week 1: Baseline audit. Measure completeness across all 30 fields for your top-revenue categories. Identify the ten most common failure patterns. Set pass/fail thresholds per channel.

Week 2: Validation rules and ownership. Convert each field into a concrete rule covering format, allowed values, and dependencies. Assign one owner per field group. Define exception handling for legitimate edge cases.

Week 3: Channel mappings and dry runs. Build explicit mappings for your primary sales channels. Run test exports on a controlled sample of SKUs. Fix transformation gaps before scaling up.

Week 4: Go-live and monitoring. Activate publish gating for critical fields. Launch alerting for drift in price, stock, media, and identifiers. Review the first week of data and tighten thresholds based on actual results.

The fastest path forward is maintaining one canonical product model and automating the repetitive mapping and QA work around it. If you want to see how this works in practice with your own catalog data, Lasso's pricing page has the details, or you can book a walkthrough with the team.

For teams dealing with multi-supplier catalogs specifically, our guide on merging supplier catalogs into a clean structure covers the data architecture decisions that make everything downstream easier.