Dirty data is the most common reason PIM projects fail
You can have the right platform, the right data model, and the right integrations. But if your product data is incomplete or wrong, you’ll still deliver a broken customer experience.
The thing with product data problems is that they compound over time. Attributes get added without constraints or governance. Different teams maintain different spreadsheets with overlapping data that varies across departments. Specs conflict among your ERP, legacy PIM, and ecommerce platform. Things can get so bad that the scope feels overwhelming.
A structured product data audit stops the guessing. It helps you replace assumptions with a clear remediation plan.
How Ntara approaches product data audit and remediation
Our framework identifies what’s wrong with your product data. It also prioritizes what to fix and shows what can be automated versus what requires human judgment.
Phase 1: Discovery audit
We analyze your product data across sources such as ERP systems, legacy PIM systems, spreadsheets, and ecommerce feeds. Then, we map what exists against what your channels require. The output is a gap analysis that includes all missing attributes, inconsistent values, formatting violations, and duplicate records.
Phase 2: Remediation plan
We categorize every gap in one of two ways. First, what is fixable through automation, i.e., bulk transformations, format normalization, and rule-based enrichment. Second, what requires input from copywriters, product managers, or category teams. That way, you know exactly what the effort looks like before work begins.
Phase 3: Remediation execution
Our team works in tandem with yours. For fixes that are eligible for automation, we build and run the transformation scripts. For fixes that require your team’s input, we manage the workflow to route tasks to the right owners and track completion.
Phase 4: Pre-launch validation
Before data enters PIM or syndicates to any channel, we validate against completeness rules and channel-specific requirements. This sets you up to scale PIM on a foundation that won’t backfire at launch.
Why this matters beyond ecommerce
Customers across all industries find products through more channels than ever, from distributor portals to marketplaces to AI-powered search. Incomplete or inconsistent data doesn’t just hurt your PDPs. It reduces visibility everywhere your products need to appear.
Structured, enriched product data is what lets you syndicate reliably without manual reconciliation at every step. That includes everywhere your data needs to go, from retailers to distributors to digital channels and beyond.
What this looks like in practice
When a global tool manufacturer came to Ntara with low PIM adoption and unreliable data, we found the source. They had a sprawling data model that had accumulated 12,000 attributes with poor governance around what was required, optional, or redundant.
We restructured those attributes down to 2,500. That’s a 78.3% reduction in fields. We rebuilt the taxonomy, eliminating unnecessary hierarchy and introducing completeness rules tied to product launch dates. As a result, they recovered 416+ hours per year through automation. And today, they have a data model that their PIM users actually trust.
Then there was Visual Comfort, a global manufacturer of premium home fixtures. When we did their PIM and DAM integration, we found a data quality problem that had quietly been limiting their ecommerce velocity. We put a new data model in place and built a bidirectional integration between their PIM and DAM. Now, corrections that used to take days appear on-site in under a minute.
What you get
- Documented data gap analysis across all source systems
- Prioritized, categorized remediation plan (automated vs. manual)
- Clean, validated product data ready for PIM ingestion
- Completeness rules documentation aligned to your channel requirements
Ready to get your product data right before it costs you?
Let’s start with a data audit scoping call.
