Poor product data has a measurable cost. In 2024, U.S. consumers returned $890 billion worth of products. Of those returns, 31% were attributed to misdescribed items. A separate analysis found that mid-market companies managing 10,000 to 100,000 SKUs lose an average of 23% of potential revenue to bad product data, traced directly to catalog inconsistencies rather than competitive or pricing factors.
Product data management (PDM) is the process of collecting, organizing, and maintaining accurate product information across your business. These product data management best practices focus on what actually moves the needle, based on where companies most often get this wrong.
1. Establish a Single Source of Truth
When product data lives in spreadsheets, ERP exports, shared drives, and someone's inbox, conflicting versions of the same product are inevitable. A price gets updated in one system but not the other. A product ships with outdated specs. The return arrives two weeks later.
The fix is structural: one place where all product data lives and all updates flow through. For most manufacturers and distributors, that means a Product Information Management (PIM) system. Tools like AtroPIM are built specifically for this, centralizing product records, assets, and relationships so every downstream channel pulls from a single, current version.
If a dedicated PIM is not feasible yet, a well-governed shared database can work as an interim step. The goal is to eliminate competing versions, not achieve a perfect setup from day one.
2. Define a Clear Data Governance Framework
Data governance sounds bureaucratic, but it comes down to three questions: who owns this data, who can change it, and what happens when something goes wrong.
Without clear answers, the same product gets updated differently by two teams, and nobody knows which version to trust. In projects we implemented for manufacturers with large catalogs, this was the most common root cause of data quality problems. Not bad tooling, but unclear ownership.
Assign a data owner per product category. That person is accountable for accuracy. Then define the minimum: naming conventions, required fields, and an approval step before anything goes live. AtroPIM supports granular role-based permissions scoped by role, product category, and workflow stage. Content editors can update descriptions without touching pricing, and reviewers can approve records without direct edit access.
A one-page policy your team actually reads beats a detailed document nobody opens.
3. Prioritize Data Quality and Completeness
A product record with missing attributes is harder to find and harder to sell. Search engines need structured data to determine relevance. Customers need enough detail to make a decision. When that detail is absent, they leave, and many do not come back.
Focus on three dimensions:
- Completeness: Are all required fields filled in?
- Accuracy: Is the information correct and current?
- Consistency: Does it match across all channels?
Set mandatory fields per product type, such as title, description, price, category, and at least one image, and enforce them with validation rules before anything goes live. Supplier data deserves extra scrutiny before import. It is one of the most common sources of errors in multi-brand catalogs.
Our customers in the industrial equipment sector regularly came to us with catalogs where 30 to 40 percent of products were missing key technical attributes. Those gaps showed up directly in search performance and quote request volumes. In one project, a manufacturer with around 8,000 active SKUs completed a structured enrichment workflow in AtroPIM over three months. Organic product page traffic increased by roughly a third, and the sales team stopped manually fielding requests for spec sheets that should have been on the product page.
4. Standardize and Structure Your Data Model
Take a catalog that lists "Blue Running Shoe," "azure sneaker," and "running shoe blue" as separate entries for the same product. The filters are broken. Search results are polluted. Customers bounce because they cannot find what they are looking for, even when it is there.
A consistent data model gives every product a defined place in your taxonomy: category, subcategory, and type-specific attributes. A cable assembly needs different fields than a lubricant or a safety harness. Building attribute templates per product type means teams always know what is required, and new products get structured correctly from the start.
This is worth investing in early. Restructuring a taxonomy of 50,000 products is significantly more painful than designing it correctly at 500.
5. Integrate Systems and Automate Workflows
Manual data entry is where errors accumulate. Every time someone copies a price from an ERP into a spreadsheet, or manually reformats a supplier file before importing it, there is a chance for something to go wrong. Research from Netguru puts the cost of data quality problems for companies at $12.9 to $15 million per year, with employees spending 20 to 27 percent of their time correcting errors.
Your PDM system should connect directly to your ERP, e-commerce platform, and any other system that produces or consumes product data. When a product record is updated in the PIM, changes flow downstream automatically based on rules you define. No manual re-entry, no version drift.
For supplier data specifically, build an automated ingestion process: pull the file, map it to your format, validate it against your quality rules, and flag anything that does not meet the threshold before it enters your catalog.
6. Enable Multi-Channel Data Distribution
Each sales channel has its own format requirements. A B2B portal needs detailed technical specs. A marketplace listing needs a character-limited title and specific attribute fields. A print catalog needs high-resolution assets formatted at print dimensions.
Maintaining separate product files per channel seems manageable at 200 products. At 5,000, every update requires multiple edits in multiple places, and something always gets missed. A single product description change can mean eight separate edits if you maintain files per channel manually. That overhead compounds with every new channel you add.
AtroPIM handles this natively. You define channel-specific output profiles, and the system formats and distributes product data accordingly. This matters most for manufacturers selling across direct, wholesale, and marketplace channels simultaneously, where format requirements diverge significantly.
7. Conduct Regular Data Audits and Maintenance
Product data degrades without maintenance. Prices change, specs get revised, and discontinued products linger in the catalog as active. Without scheduled audits, small inaccuracies accumulate until they surface as visible problems: wrong prices going live, or obsolete products appearing in customer searches.
Schedule quarterly reviews and check for:
- Incomplete product records
- Duplicate entries
- Outdated pricing or specifications
- Discontinued products still marked as active
Track two metrics over time: your completeness rate (share of products with all required fields filled) and your error rate (issues flagged per audit cycle). These tell you whether your data health is improving or slipping. If completeness is trending down despite a stable team, a process is probably being bypassed. Worth investigating before it compounds.
8. Train Teams and Build a Data-First Culture
Tools only work when people use them correctly, and data quality is as much a people problem as a technology one.
Train everyone who creates or edits product records: procurement, marketing, operations. The training that sticks focuses on consequences, not procedures. A product manager who understands that a missing technical attribute keeps a product out of filtered search results on a B2B platform will fill it in. One who sees it as an abstract compliance requirement will skip it.
One practice that works well in onboarding is pairing new team members with a short catalog review task before they add anything. They find records with missing fields, trace back why those gaps exist, and fix them. It takes an hour and makes the cost of incomplete data concrete in a way that no policy document does.
Keep processes simple and well-documented. Complexity produces shortcuts, and shortcuts produce bad data. If your data entry workflow has more than a few steps per product type, look for what can be automated or removed before adding more training. A workflow that gets bypassed is worse than no workflow, because it creates the appearance of governance without the substance. Audit logs in AtroPIM make bypassed steps visible: if records are going live without passing through the approval stage, that shows up, and you can address it before it becomes a pattern.
9. Plan for Scalability from the Start
What works for a catalog of 300 products often breaks at 30,000. Data structures that were pragmatic at a small scale become rigid constraints when product volume grows, new categories are added, or the business expands into markets with different language and currency requirements.
When choosing a PDM system, look past your current state. It needs to handle significantly larger product volumes without performance degradation, support multiple locales for international expansion, and accommodate product categories with attribute structures that do not yet exist in your catalog.
The same applies to your data model. Avoid hardcoding taxonomies or attribute structures that cannot be extended. Building in flexibility early is far cheaper than restructuring after the catalog has grown.
Governance scales too, or fails to. A policy that works when five people touch product data does not automatically work when fifty do. Build approval workflows and role structures that can expand alongside your team, not ones that require renegotiating every time you add a channel or category.
10. Protect Your Data with Access Controls and Security
Product data often contains commercially sensitive information: supplier pricing, cost structures, unreleased product specs, and planned launch dates. Treating it purely as an operational asset underestimates the exposure.
Start with role-based access. Content teams need to edit descriptions; they do not need visibility into supplier costs. Product managers need to publish records; they do not need to modify approval workflows. Permissions scoped to role and task reduce both accidental errors and deliberate misuse.
Keep an audit trail. Modern PIM systems log who changed what and when. When something goes wrong, you can trace it to its source in minutes rather than spending hours reconstructing what happened.
AtroPIM includes configurable role-based permissions and a full change history per product record, which makes governance enforcement and error recovery significantly faster.
Verify that integrations with third-party platforms use secure data transfer practices and that your systems comply with applicable data regulations. A leak of unreleased product specifications or confidential pricing to a marketplace partner creates real commercial damage.
Where to Start with Product Data Management
If none of this is in place yet, the highest-leverage starting point is a single source of truth with clear ownership. Get that right, and the other product data management best practices build on top of it more easily. If some are already in place but inconsistently followed, the bottleneck is usually governance. Unclear ownership leads to inconsistent execution regardless of how capable the tooling is.
Product data management is an ongoing discipline. The companies that treat it as one ship faster, make fewer mistakes, and spend less time on problems that should not have existed.