Has data become hottest non-commodity in the mining & metals sector?

Artificial Intelligence (AI) is rapidly transforming industries, and the mining and metals sector is no exception. With global demand for critical minerals surging as the world transitions toward clean energy, companies are under pressure to accelerate exploration and production. To meet this challenge, they are turning to AI for its ability to revolutionize how resources are discovered. However, for AI to function effectively in this context, it requires large amounts of quality data. As a result, data itself has emerged as a highly valuable asset in the sector, positioning it as the hottest non-commodity for miners and explorers alike.

Just as children learn by looking at images and being told the names of animals, AI systems require examples to identify geological anomalies. This analogy illustrates the importance of high-quality, extensive data for AI to operate effectively in mineral exploration. AI algorithms need to be trained with significant data sets so they can recognize patterns in geological structures, thereby enabling more accurate predictions and insights. For instance, detecting the presence of valuable minerals requires AI to sift through vast amounts of geophysical data. The better and more comprehensive this data is, the more efficiently AI can flag anomalies that warrant deeper investigation.

It becomes evident that data is key as technology startups and traditional mining giants alike recognize its growing importance. KoBold Metals, for example, has pioneered the use of AI to accelerate the search for battery metals crucial to the energy transition. The company utilizes a proprietary data set to guide its exploration activities, drastically reducing the time and financial cost typically associated with identifying new mineral deposits. AI-driven models at KoBold comb through existing geological data from various sources to pinpoint the most promising areas for further exploration. Such innovations underscore that data is the lifeblood of AI, and without it, these advanced technologies would be far less effective.

Established companies in the mining and metals industry are also reaping the benefits of AI-powered data analytics. Rio Tinto, one of the world’s leading mining corporations, has implemented AI to automate the logging of core samples, a critical step in understanding ore bodies. By feeding historical data into machine learning models, the company can assess mineral resources more accurately and at a faster pace. Similarly, BHP uses AI to integrate core sample data with geological information, enhancing its ability to optimize mining operations. These examples show that major industry players, sitting on decades of core samples and exploration data, have recognized the vast potential AI can unlock when paired with the right datasets.

As companies increasingly turn to AI, the value of geological data continues to rise. Data: The hottest non-commodity in the mining & metals sector? is demonstrated by transactions such as KoBold Metals’ acquisition of exploration data from Alaska Energy Metals Corporation. This deal, involving data near KoBold’s Skolai Project, highlights the premium placed on data as a critical asset in the sector. By acquiring access to rich geological datasets, KoBold can further enhance its AI-driven exploration processes, giving it a competitive edge in the hunt for minerals essential to electric vehicles and renewable energy storage.

Moreover, the growing recognition of data’s value has fostered new kinds of strategic partnerships. Traditional mining companies, with their wealth of historical data, are increasingly collaborating with technology-driven startups that bring expertise in data analysis and AI. These partnerships blend the strengths of both worlds—traditional expertise and cutting-edge technology. For instance, Ivanhoe Mines, through its subsidiary Computational Geosciences Inc., has used AI to process data collected by its proprietary Typhoon™ technology, generating 3D geological models that pinpoint drilling targets. By sharing data and leveraging advanced analytics, companies are unlocking untapped potential within their existing resources.

Yet, this transactional activity surrounding data is raising important questions within the industry. How exactly is the value of geological data determined? Often, the answer depends on the specific context of the transaction, with data being evaluated based on its uniqueness and relevance to the buyer’s objectives. Ownership of data is another complex issue. In cases of acquisition, does the seller retain any rights to use that data in the future? When it comes to partnerships, who owns the data that results from joint exploration efforts? These questions are becoming increasingly central to negotiations in the mining and metals sector, particularly as the role of data grows.

Governments are also starting to recognize the significance of data in the mining and metals industry. In many countries, natural resources are seen as national wealth, and the data generated from exploration activities is viewed in a similar light. This has led to regulations requiring companies to report their exploration data to government agencies. For example, Australia and Canada, two of the largest mining jurisdictions, mandate that companies share their data, which is then made publicly available after a set period. These policies raise the stakes for mining companies, as government control over data could impact how companies strategize their exploration efforts.

In some cases, governments are even becoming direct participants in data collection and sharing. A notable example is Zambia, where the government has launched a country-wide airborne geophysical survey to assess its mineral potential. Ivanhoe Mines has partnered with the Zambian government to share geological data, with the aim of co-developing mining projects. Such collaborations underscore the role of governments as key enablers in this new frontier of AI-driven exploration. By working closely with governments, companies can access broader datasets and benefit from public-private partnerships that enhance exploration capabilities.

Vast repositories of geological data, core samples, and exploration results are now seen as treasure troves waiting to be unlocked. Oil and gas companies, with their extensive reserves of data, could also play a significant role in the energy transition. Their historical data, originally collected for fossil fuel exploration, can be repurposed to identify critical minerals needed for renewable energy technologies. This cross-sectoral application of AI to old datasets represents a potential game-changer for both industries.

Also published on Medium.