Case Study: How a Sporting Goods Wholesaler Achieved 99.7% Data Accuracy Before Migration

A sporting goods wholesaler distributing bikes, fitness equipment, and outdoor gear to approximately 280 retail accounts was preparing to migrate from a legacy ERP-based catalog system to a modern ecommerce stack with B2B portal functionality. A pre-migration data audit found that 14.2% of their 8,400 active product records contained at least one incorrect or missing required attribute. Migrating with that error rate would have pushed bad data directly into a new system that their retail customers would use to place orders.

The Challenge

The catalog had been built over 11 years across three different ERP versions. Each system migration had brought over the product data without comprehensive validation, and data quality issues had accumulated over time. The types of errors identified in the initial audit fell into several distinct categories:

Weight and dimension errors (highest frequency): Products where the recorded weight did not match the actual product weight. This was especially prevalent in the bike accessories category, where products had been re-packaged by suppliers with different packaging weights and the catalog records had not been updated. Of 1,240 bike accessory SKUs audited, 187 had weight discrepancies of more than 15%.

Missing safety certifications (compliance risk): Helmets, protective gear, and certain fitness equipment required CE certification numbers and test standard references to be recorded in the catalog for both B2B ordering compliance and for downstream label generation. 94 products in the safety equipment category were missing certification data entirely. Some of these had never had the data entered; others had data in a legacy field that had not migrated correctly across the ERP versions.

Incorrect dimensions: Fitness equipment items where the assembled dimensions were recorded in place of the packaged shipping dimensions, or vice versa. This caused warehouse picking errors and shipping cost calculation failures. The error rate in this category was 9.1% across 2,200 fitness equipment SKUs.

Category miscategorization: 312 products assigned to incorrect categories — primarily in the outdoor gear range where the legacy ERP used a flat category structure and products had been assigned to the closest available category rather than the correct one. In the new ecommerce system, category assignment would drive navigation, search facets, and attribute requirements. Wrong categories would mean wrong attributes displayed and wrong search results.

Duplicate records with conflicting data: 68 products had duplicate entries in the system — typically created when a re-ordered product was entered as a new item rather than linked to the existing record. In some cases, the two records had different weights, different images, or different supplier codes.

The pre-migration audit was conducted by the IT and operations teams jointly over three days, using a combination of manual sampling and exports to Excel. The estimate from the audit: cleaning the data manually to a publishable standard would take 6-8 weeks of dedicated work from the catalog team, pushing the migration timeline back by two months and increasing project cost significantly.

[SCREENSHOT: MicroPIM data audit dashboard showing error distribution by category — a bar chart with error counts per error type (wrong weight, missing certification, wrong dimensions, wrong category, duplicate records) against the total SKU count per category]

What They Tried First

The initial plan had been to clean the data using Excel and then migrate. The IT project manager assigned a catalog analyst to begin working through the weight and dimension errors first, as these were the most numerous. After one week, the analyst had reviewed and corrected 420 products. Extrapolating that rate, full data cleanup would take 28 weeks at that pace — well outside the project budget.

The team then considered a different approach: migrate the data in its current state, mark erroneous products as draft or unpublished, and clean them in the new system post-migration. The operations director rejected this plan for two reasons. First, approximately 40% of the products with data errors were active top-sellers that could not be taken offline during the migration period without significant revenue impact. Second, the logic for the new B2B portal included attribute-driven product filtering and automatic shipping cost calculation — both features that depended on correct dimensional data. Migrating with wrong dimensions would generate incorrect shipping quotes that the warehouse would then need to manually override on every affected order.

A third approach considered was hiring a specialist data cleaning agency. Two agencies were approached; quotes came in at €28,000-35,000 for a project of this scope. The timeline estimates from both agencies were 6-10 weeks, which still delayed the migration.

The Solution

The team imported the full 8,400-product catalog into MicroPIM and used its data validation pipeline to systematically identify, categorize, correct, and re-validate the product data accuracy issues before migration day.

Step 1: Structured Data Audit

MicroPIM’s audit tool ran a rule-based scan against the imported catalog. The audit rules were configured to reflect the attribute requirements of the target ecommerce platform:

• Weight must be present and within a plausible range per category (e.g., a bike accessory should not weigh more than 25kg).
• Shipping dimensions (L x W x H) must all be populated and internally consistent (no single dimension larger than the category maximum).
• CE certification number must be present for all products in the safety equipment category.
• Category assignment must be one of the valid terminal nodes in the new platform’s taxonomy.
• No duplicate EAN values across the catalog.

The audit ran in approximately 8 minutes and produced a structured report: 1,194 products with at least one failing rule, organized by error type and category. This was the 14.2% error rate figure. The report provided enough detail to allocate cleanup work by type and priority.

Step 2: Bulk Fix Workflows by Error Type

Rather than correcting errors product by product, the team used MicroPIM’s bulk edit tools to address errors by type across the entire affected population.

Weight and dimension errors were handled using the supplier data import. The two main bike accessory suppliers provided updated product data files when requested — weight and dimension data is standard in their B2B data sheets. Importing the supplier files and mapping the weight and dimension fields to the existing product records corrected 312 of the 187 bike accessory weight errors in a single import operation. The remaining cases required manual lookup from physical product specifications.

Missing safety certification data was partially resolved by locating the certification data in the legacy ERP’s archived field that had not migrated correctly. A bulk update from the archived export populated 61 of the 94 missing certification records. The remaining 33 required contacting suppliers directly for documentation. This took approximately two weeks running in parallel with the other cleanup work.

Dimension category errors were corrected through a conditional bulk edit: find all fitness equipment products where assembled dimensions exceed shipping dimensions on at least one axis, and flag for review. Of the 200 products flagged, 171 had their dimension fields in the wrong order (assembled and shipping swapped). A bulk field swap operation corrected these in a single action. The remaining 29 were individually reviewed.

Category miscategorization was handled by building a mapping table: old category code to new category node. For 298 of the 312 miscategorized products, the correct category was deterministic from the product name and supplier code. A bulk reclassification applied the mapping table across the affected products. The 14 edge cases required manual review.

Duplicate records were merged using EAN as the matching key. For each pair of duplicates, MicroPIM’s merge tool presented both records side-by-side with field-level comparison, allowing the analyst to select the correct value for each discrepant field. 68 merges were completed in approximately 3 hours.

[SCREENSHOT: Bulk edit interface in MicroPIM showing the conditional filter “fitness equipment + assembled dimension > shipping dimension on any axis” with 200 results selected, and the bulk swap action applied to the L/W/H shipping dimension fields]

Step 3: Re-Validation and Accuracy Measurement

After the bulk fix workflows were completed, the team ran the full audit again. The second audit identified 25 remaining product data accuracy issues — down from 1,194. Of these, 14 were the pending safety certification cases awaiting supplier response, and 11 were miscellaneous individual errors that required product-specific investigation.

The 14 pending certifications were resolved over the following week as supplier documentation arrived. The 11 individual cases were corrected by the catalog analyst.

Final audit, one week before migration: 25 products with at least one error, representing 0.3% of the 8,400-product catalog. Measured product data accuracy: 99.7%.

The 25 remaining products were all in non-critical categories with no active orders from retail accounts in the preceding 90 days. The project team decided to migrate them in their current state with a post-migration correction task, rather than delaying the entire migration further.

The Results

The data quality project was completed in 5.5 weeks — less than the 6-8 week estimate for manual cleanup and significantly less than the 6-10 week agency timeline:

• Product data accuracy improved from 85.8% to 99.7% across 8,400 active SKUs.
• 1,169 products corrected through bulk workflows — weight updates from supplier files, dimension field swaps, category remapping, and EAN-based duplicate merges.
• 5.5 weeks to completion versus the 28-week estimate for manual product-by-product correction at the initial analyst pace.
• Migration proceeded on schedule. No timeline delay from the data quality project.
• Shipping cost calculation accuracy improved immediately after migration. The warehouse reported a 91% reduction in manual quote overrides in the first month of the new B2B portal operation, directly attributable to correct dimensional data.
• No safety certification gaps in the migrated catalog. All 94 products that were missing certification data either had it populated or were flagged for post-migration supplier follow-up.
• Data cleanup cost: MicroPIM subscription + approximately 40 hours of catalog analyst time — significantly below the €28,000-35,000 agency quote.

Key Takeaways

• Product data accuracy degrades silently over time, especially in catalogs that have existed across multiple system migrations. A 14% error rate is not unusual for a catalog of this age and history; it is simply invisible until an audit makes it visible.
• Structured audits with quantified error types are more useful than impressionistic data quality assessments. Knowing that 187 products have weight discrepancies over 15% and 94 products are missing certification data makes prioritization and bulk correction planning possible.
• Bulk correction by error type is orders of magnitude faster than product-by-product manual review. The most impactful single action in this case study was the supplier data import that corrected 312 weight errors in one operation.
• Pre-migration data quality work is significantly cheaper than post-migration correction. Data errors in a live customer-facing system generate operational costs (manual overrides, customer service contacts, compliance risks) that accumulate over the full lifetime of the error.
• Master data accuracy should be measured and tracked as a catalog metric, not treated as a one-time project. The 99.7% endpoint is not a permanent state — it requires ongoing validation rules to maintain.

If you are preparing for a platform migration or concerned about the accuracy of your current catalog, MicroPIM’s audit and validation tools can give you a quantified picture of your data quality in hours rather than weeks. Start with a free account and run an audit against a sample of your catalog at app.micropim.net/register.

Related Reading

• Audit Your Product Data
• Case Study: Home Goods Wrong Attributes Eliminated
• Case Study: Office Supplies Reduced Returns

Frequently Asked Questions

How does MicroPIM determine what counts as an “error” during a data audit?

Audit rules are configured by the user based on the data requirements of the target system or business. Common rule types include required field presence checks (this field must be populated), range validation (weight must be between X and Y for this category), format validation (EAN must be 13 digits), referential integrity (category must be a valid node in the taxonomy), and cross-field consistency (all three dimension fields must be populated if any one is). The rules reflect your quality standard, not a generic standard — which is what makes the audit useful for pre-migration work.

Can MicroPIM connect directly to the legacy ERP to pull the source data, or does the catalog need to be exported manually first?

MicroPIM connects to common ERP systems including SAP, Microsoft Dynamics, Oracle NetSuite, and Odoo through native integrations. For legacy systems without a standard integration, the most common approach is a full catalog export to CSV or Excel, which is then imported into MicroPIM. The export-import path does not compromise data quality — the audit and correction work happens after import, before the data goes anywhere else.

What happens to products that fail validation but cannot be corrected before migration — for example, if a supplier has not responded with certification data?

Products that fail validation rules can be assigned a workflow status in MicroPIM — for example, “pending supplier documentation” — and excluded from the initial migration batch while still being tracked. This allows the migration to proceed with the clean portion of the catalog while the remaining items are in a defined correction workflow. The pending items migrate when their data is complete, rather than being forgotten.

How long does a full catalog audit take for a catalog of 8,000-10,000 products?

The audit scan itself runs in minutes — processing time scales with catalog size but is typically under 15 minutes for catalogs up to 50,000 products. The time investment is in configuring the audit rules to reflect your target quality standard (typically 2-4 hours for a thorough rule set) and in reviewing and acting on the output. The audit produces a structured report that can be prioritized and distributed to team members for parallel correction workflows.