Why Primary Data Strategy Now Determines LCA Outcomes

Life cycle assessment is increasingly used to support decisions with regulatory, financial, and strategic consequences. In this context, the limiting factor is no longer the choice of impact method, but whether the data represents how products and supply chains actually operate. This white paper explains why organisations can meet formal LCA requirements while still relying on assumptions in the most decision-critical parts of their models. It shows how data choices - not calculation techniques, ultimately determine confidence, comparability, and risk exposure.

Generic and modelled datasets are essential for scale, but they are designed to represent typical systems, not real ones. By smoothing variability, they often suppress the very differences that matter when comparing suppliers, technologies, or sourcing routes. Using lifecycle assessment to guide decisions requires moving beyond averages. This section demonstrates how increasing data specificity at material processes shifts hotspots, changes results, and reveals risks and opportunities that static models fail to capture.

Primary data does not improve outcomes everywhere - and treating it as a blanket requirement often adds cost without leverage. Across most product systems, a small number of upstream or energy-intensive processes dominate total impact. The paper introduces a hotspot-first approach that uses secondary and modelled data to guide focus, then applies primary data selectively where decisions change. This ensures effort is targeted where it reduces uncertainty, rather than where it simply increases coverage.

Stable LCA results are often mistaken for robust ones. In practice, unchanged outputs frequently reflect unchanged assumptions rather than improved understanding of reality. This section explains why data quality is about confidence, not perfection. It introduces practical ways to make progress visible - including Primary Data Share, helping teams distinguish between evidenced performance and assumption-driven results without oversimplifying complex models.

Primary data only creates value when it can be exchanged, reused, and governed across supply chains. Manual questionnaires, spreadsheets, and one-off studies rarely scale and often create fatigue for suppliers. The white paper outlines what effective supplier data operating models look like in practice - focusing on minimal viable data asks, clear governance, and incentives that make participation worthwhile without requiring full disclosure of sensitive information.

As organisations embed LCA into procurement, compliance, and reporting, it must function as infrastructure rather than one-off analysis. Infrastructure demands consistency, explainability, and the ability to evolve as data improves. This final section explores how tools, interoperability, and controlled transparency enable organisations to turn primary data exchange into reusable, decision-grade LCA, reducing regulatory risk while improving strategic flexibility.