Every sustainability claim a company makes, whether it is about carbon, nature, or labor conditions, ultimately rests on the same foundation: can you actually trace where something came from, and do you have reliable data about it? For most companies, the honest answer is “only partially.” That gap between what a company claims to know and what it can actually verify is quietly becoming one of the biggest risks in corporate sustainability.

It’s tempting to file traceability under ordinary data hygiene, but isn’t, for a few reasons:

• Most companies don’t own the data: The information you need — where a raw material was grown, which farm it came from, what conditions workers faced — sits with suppliers several tiers removed from you, who may have no obligation or incentive to share it.
• The chain gets murkier with distance: Tier 1 suppliers are usually visible. Tier 2 and beyond — the actual farms, mines, and raw material processors — are where visibility collapses, and that’s often exactly where the highest-risk activity happens.
• Verification is harder than collection: Getting a number from a supplier is one problem, but trusting that number is another. Data that hasn’t been validated can’t reliably support compliance claims or survive an audit.
• It’s a market access issue: With the EU Deforestation Regulation in active enforcement and CSRD reporting obligations expanding, a broken traceability system can mean a shipment gets held at the border or an order doesn’t arrive on time.

1. Inconsistent data formats across suppliers: Duplicate lot numbers, mismatched supplier codes, and manual data entry errors mean that even when data exists, it doesn’t line up cleanly enough to trace a product end-to-end.

2. Field-level data capture gaps: Where data originates on the ground — a farm visit, a factory floor — matters enormously. Tools that require constant connectivity or aren’t available in local languages see far higher rates of skipped or incomplete data entry than offline-first, localized alternatives.

3. Partial adoption: A traceability system only works if every tier actually uses it. A platform that Tier 1 suppliers adopt but Tier 2 and 3 ignore just moves the blind spot one level down the chain.

4. No agreed metrics: Even when data is collected consistently, suppliers, regions, and industries often aren’t measuring the same thing the same way — different units, different definitions of a “batch” or “impact,” different baselines. Without a shared metric standard, data can be complete and still incomparable across the chain.

The good news: none of this requires solving every problem at once. A few concrete moves make the biggest difference. Design matters as much as technology: traceability tools live or die on adoption in the field, not in head office, and offline-first, mobile-first, multilingual data capture tools consistently outperform connectivity-dependent, single-language systems when it comes to how much data actually gets captured accurately at the source. Collection alone isn’t enough, either — confirming accuracy is where most programs fall short, so building in spot-checks, cross-referencing, or third-party verification for your highest-risk suppliers and commodities matters more than assuming self-reported data is reliable by default.

The remaining moves are about focus rather than more effort. Rather than building a system that flags everything, define a narrow set of exceptions that genuinely warrant follow-up. Finally, treat tier 2+ visibility as the actual goal — since tier 1 visibility is usually already reasonably solid, the real return on effort is pushing traceability one or two tiers further back, toward the raw material sources where risk (and the current data gap) is greatest.