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Geochemical surveys are a cornerstone for data generation in geosciences, facilitating resource exploration. Geochemical data is essential for identifying mineralized areas, understanding local geology and assessing environmental impacts. This paper aims to: (1) review the process of geochemical data generation to gain an appreciation of the characteristics of traditional geochemical data; (2) review recent developments in the usage of geochemical data, particularly the disruption brought forth by those in data science and artificial intelligence; and (3) envision a future specification of geochemical data that would be fit-for-purpose for modern and emerging data users. This review reveals that benefits brought by advancements in automation, analytical technique and computing capability have unlocked unprecedented insights from geochemical data. However, the sustainability of re-purposing small geochemical data for big data methods is intrinsically questionable. The mismatch stems from rapidly changing data requirements in geochemistry, which is brought forth by: (1) a pivot away from scientific reduction through the adoption of system-level methods; (2) developments in geometallurgy; (3) skill gaps in geoscientific education; and (4) a growing demand of raw materials. While traditional methods will likely continue to serve many scientific needs, new strategies and techniques must be developed and implemented to cost effectively and efficiently generate bigger geochemical data. Bigger geochemical data must emerge in response to the already changing landscape of geochemical data usage. Solutions are multidimensional, from evolving geoscientific education, leveraging modern technology, explicating and differentiating data user and generator roles and responsibilities, to modernizing data management.
Bourdeau et al. (Tue,) studied this question.