A footprint result depends on the quality of the data behind it. Important factors include whether material data is exact, whether supplier-specific information is available, whether defaults are used, and whether assumptions match the actual product and supply chain.

Data quality is not all-or-nothing. Companies can start with available data and improve the result over time. The key is to make assumptions visible and focus improvement effort where better data will materially affect the result.

Replacing a default production location with supplier-specific data may improve the result more than spending time on a tiny low-impact accessory.

Pickler helps teams structure product data, identify data gaps and improve representativeness while keeping results usable across a portfolio.