Key Takeaways
Product data quality affects your conversion rate, your return rate, your search rankings, and how much customers trust your brand. Yet most companies treat it as an afterthought until something goes wrong.
The key pillars of product data quality are accuracy, completeness, consistency, relevance, timeliness, and regular review.
The companies that get this right sell more, handle fewer returns, and spend less time firefighting internal data problems. This article explains how companies can improve product data quality with 11 practical, actionable steps.
What Is Product Data Quality?
Product data quality describes how accurate, complete, and useful your product information is across every channel where it appears.
The concept is well established in information management. The DAMA Data Management Body of Knowledge, a widely referenced standard in the field, defines data quality through dimensions that translate directly to product data:
- Accurate — values reflect reality and can be traced to a reliable source
- Complete — every attribute customers and downstream systems need is present
- Relevant — focused on what matters; not padded with unnecessary detail
- Consistent — formatted the same way across every platform and channel
- Timely — updated promptly whenever something changes
- Up-to-date — reviewed on a schedule, so nothing quietly goes stale
In practice, most product catalogs have problems in at least two or three of these areas simultaneously, which is exactly why improving data quality tends to require a structured approach rather than ad hoc fixes.
Why Poor Product Data Quality Is So Costly
The business case for investing in product data quality is well documented. Shotfarm's Product Information Report found that 87% of online shoppers rate product content as extremely or very important when making a purchase decision. Separately, industry research consistently shows that inaccurate or incomplete product descriptions are among the leading causes of e-commerce returns, with some categories reaching 30–40% of all orders.
The mechanism is straightforward. Shoppers who can't find the information they need leave and rarely come back. Those who buy based on a misleading description return the product, leave a negative review, or both. Listings with thin or inconsistent data tend to rank poorly in search, whether on Google or a marketplace like Amazon, where completeness and accuracy directly influence algorithmic visibility.
Inside the business, the damage compounds differently. Teams reconcile conflicting versions of the same data. Errors get caught late, when they're expensive to fix. New channels can't launch on schedule because the data isn't ready.
IBM has estimated that poor data quality costs U.S. businesses trillions of dollars annually across industries. Product data is a significant part of that figure for any company selling physical goods. The investment required to improve it is almost always smaller than the cost of leaving it broken.
Many companies struggle with inconsistent or incomplete product data. The following 11 tips provide a clear roadmap to fix common issues and maintain high-quality data over time.
1. Break Down Product Data Silos
A data silo forms when product information gets trapped inside one team, one spreadsheet, or one system, inaccessible to everyone else who needs it.
In practice, this is the default state for most growing businesses. Marketing works from a spreadsheet they've maintained for years. The e-commerce team has its own version, edited incrementally over time. The logistics team pulls from the ERP. None of them fully agree, and no one has a clear mandate to resolve the conflict.
The solution isn't to consolidate everything into a shared folder and hope for the best. It's to share product data without duplicating it — one authoritative record that all systems read from. When an update happens in one place, it propagates automatically. There's no second copy to fall out of sync.
This is the foundational change that makes every other improvement on this list more effective.
2. Audit Your Existing Product Data
You can't prioritise what you haven't measured. Before attempting improvements, run a proper audit to understand the actual state of your catalog.
Look for:
- Duplicate product entries — particularly common after ERP migrations or catalog merges
- Missing required fields — attributes left blank because they were never mandatory
- Inconsistent formats (e.g., "2kg", "2 kg", "2000g" — all meaning the same thing, recorded differently)
- Outdated prices, specs, or descriptions — especially for products that have been revised since launch
- Images that are missing, incorrect, low resolution, or inconsistently cropped
A useful approach is to score each product against your quality criteria and generate a completeness percentage per category. This gives you a clear picture of where the worst gaps are — and a baseline to measure improvement against.
Resist the urge to fix everything at once. Start with the products that drive the most traffic or revenue. Visible progress in the right areas builds momentum and makes the case internally for continued investment.
3. Define Clear Data Standards
Vague expectations produce inconsistent results. If your team doesn't know precisely what a complete, correctly formatted product record looks like, they'll each interpret it differently — and you'll end up with exactly the kind of inconsistency an audit reveals.
Define your standards in writing, covering:
- Which fields are mandatory for each product category
- What format each attribute must follow (e.g., dimensions always in cm, weights always in kg, no exceptions)
- Minimum description length per product type
- Accepted image dimensions, file formats, and background requirements
- Naming conventions for product titles, variants, and SKU codes
These standards should govern every product entering your system going forward — not just the ones you're retroactively cleaning up. Apply them at the source, not as an afterthought.
For businesses that exchange data with supply chain partners, distributors, or marketplaces, adopting established classification frameworks is worth the effort. GS1 is the global standard for product identification and data exchange. ETIM is widely used in technical and electrical products. ECLASS and UNSPSC serve broader cross-industry classification needs. Using any of these reduces friction significantly when onboarding new partners or listing on external platforms.
4. Apply Automated Validation Rules
Documenting standards is the easy part. Enforcing them consistently, across every person and system that touches your catalog, is where most companies struggle.
Automated validation rules close that gap. Configured at the point of data entry, they prevent non-compliant records from being published. Common examples:
- A product cannot be published without a description of at least 100 characters
- The "weight" field rejects non-numeric input
- Products in a specific category require a minimum of five images before the record is considered complete
This shifts quality control from a downstream review task to an upstream enforcement mechanism. Errors are caught before they reach the catalog, not after a customer reports them.
Layer human approval workflows on top, requiring sign-off before publication, and you have a system that combines the speed of automation with the judgment of an experienced reviewer for edge cases.
5. Establish Data Governance
Data governance is often framed as a compliance exercise. In practice, it's something more fundamental: a clear, documented answer to the question of who owns your product data and what they're authorised to do with it.
Without governance, quality degrades at the rate your catalog grows. Teams make uncoordinated local edits. Conflicting versions accumulate. When something goes wrong, there's no process for resolving it — and no one with the authority to enforce a fix.
Effective governance covers:
- Who can create, edit, or approve product records
- How changes are tracked, versioned, and reviewed
- What procedure applies when two sources provide conflicting information
- How errors are flagged, escalated, and corrected
For regulated industries — medical devices, chemicals, food and nutrition — data governance also needs to account for compliance requirements and audit trails. But even for general retail, the absence of governance is one of the most reliable predictors of persistent data quality problems.
6. Assign a Product Data Manager
Governance frameworks without human ownership don't hold. Someone needs to take personal responsibility for product data quality, with it as a defined part of their role, not an add-on to something else.
A product data manager (sometimes called a data steward or data owner) is responsible for maintaining and evolving your data standards, monitoring quality metrics, handling data-related questions from internal teams, and managing the relationship with suppliers and content agencies to ensure incoming data meets your requirements.
In smaller businesses, this might be part of an existing role: a senior e-commerce manager or catalog manager who takes on the function formally. In larger organizations, it's a dedicated position. Either way, the critical requirement is accountability. When no one specifically owns it, data quality becomes everyone's lowest priority.
7. Create a Single Source of Truth
Every person who touches product information: copywriters, inventory managers, channel managers, agency partners, should pull from the same place and push updates back to the same place.
A single source of truth is a central repository where product data is authored, stored, and distributed. It's the definitive record. Everything downstream reads from it; nothing writes to its own private copy.
In organisations without this, the same product might be described differently on the website, the marketplace listing, the print catalog, and the wholesale price list, not because anyone made a deliberate decision to differentiate, but because each channel was managed independently. When one changes, the others don't follow.
Establishing a single source of truth requires both a technical solution (more on that in section 11) and an organisational commitment to routing all edits through one place rather than allowing teams to maintain their own versions.
8. Adapt Your Data for Each Channel
A single source of truth doesn't mean identical content everywhere. Different channels have different requirements, audiences, and technical formats — and product data quality means meeting each channel's standard, not just maintaining a generic master record.
A product description written for your own website might run 300–500 words, structured for both SEO and conversion. The same product on a B2B wholesale portal might need a concise technical summary and a precise set of specifications. On a social commerce channel, the image carries most of the weight. In a print catalog, space is limited, and descriptions need to be precise and punchy.
Your PIM or central data system should handle this by maintaining channel-specific variants of key content fields, so you're not manually reformatting for each destination. The core data (dimensions, weight, materials, identifiers) stays consistent everywhere. The presentation adapts.
9. Build a Process for Ongoing Updates
Product data degrades. Prices change. Specifications are revised. Regulatory requirements shift. A product that was accurately described at launch may be quietly misleading twelve months later — and unless someone is responsible for catching that, it will stay misleading.
The solution is a structured update process, not periodic heroics:
- Schedule regular reviews of descriptions, specs, and images — quarterly at minimum for fast-moving categories, annually for stable ones
- Update pricing and promotional content in real time, not in batches
- Systematically mine customer feedback: product reviews, support tickets, return reasons — for evidence that your data is missing something customers need
- Treat data maintenance as an ongoing operational function, not a project to be completed
The most actionable signal is often the simplest: recurring customer questions. If your support team keeps answering the same question about a product, that question should be answered in the product description, not in an email thread.
10. Track Progress With Data Quality Metrics
Improvement without measurement is guesswork. To manage product data quality seriously, you need a defined set of metrics and a regular review cadence.
The most important is completeness — the percentage of mandatory fields that are actually populated, segmented by product category. It's the most direct measure of whether your standards are being met in practice. Alongside completeness, track:
- Accuracy — are values correct and traceable to a verified source?
- Consistency — does the same attribute look the same across every channel where it appears?
- Timeliness — how long does it take for a change to propagate everywhere it needs to go?
Set a baseline before you start improving, then review on a fixed schedule — monthly for active improvement phases, quarterly for maintenance. Share the numbers with the people responsible for acting on them. A simple dashboard makes data quality visible in a way that a spreadsheet buried in someone's inbox never will.
Over time, these metrics also help you build the business case for further investment. A documented reduction in return rates, or an improvement in search ranking correlated with data completeness, is a concrete argument for resources.
11. Implement a PIM System
Everything described above can be approached manually at small scale. But past a few hundred products, or with more than one or two sales channels, the process becomes unmanageable without dedicated tooling.
A Product Information Management (PIM) system is purpose-built for this. It provides a central repository for all product data, enforces validation rules, manages channel-specific content variants, supports approval workflows, and tracks quality metrics — all in one platform. It's the technical infrastructure that makes the organisational practices in this article sustainable at scale.
When evaluating PIM tools, the key variables are catalog size, number and type of distribution channels, integration requirements (ERP, DAM, e-commerce platforms, marketplaces), and the degree of automation you need for quality enforcement and content syndication.
One option worth looking at is AtroPIM — an open-source PIM that covers all of what's described in this article out of the box. It's a solid starting point if you need something flexible and don't want to pay enterprise licensing fees to get there.