Rio Tinto scales up its big data ambitions

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Rio Tinto scales up its big data ambitions

Dangles data carrot in front of 700 Aussie juniors.

Rio Tinto is opening its “vast troves” of exploration data to junior explorers in the hopes they will help sift for opportunities and supply ideas and labour.

The miner’s head of exploration Stephen McIntosh said he believed there was “no logical or geological reason” why a new era of mining exploration – aided by technology and big data – would not turn up significant new ore body discoveries.

But with mineral prices under pressure, McIntosh indicated the miner was keen to strike up partnerships with some of the “more than 700 junior explorers in Australia” to spread the risk of new projects rather than go it alone.

“Our industry has reached a new maturity that calls for ecosystems in which risks and rewards are shared and the specialists do what they do best,” McIntosh said in a lengthy opinion published by professional minerals body AusIMM.

“In the case of junior explorers, it is both new ideas as well as ‘boots on the ground’ work.

“In the case of majors like Rio Tinto it is using our decades of experience, vast troves of data, and modern technologies to sort the interesting ideas from the rare tier 1 opportunity.”

McIntosh said Rio Tinto wants "juniors to knock on our door”, and is hoping Rio Tinto’s vast exploration big data set would act as a significant drawcard.

This data set effectively contains information drawn from “decades of mineral samples across Australia and the world that we can use to compare and contrast, or sort and select", he said.

“[It’s] a data set that is almost impossible to replicate, and of enormous value for project identification and target generation,” McIntosh said.

“A data set that we can build upon, and work with junior explorers to sift and prioritise opportunities with preferred mineralogy and geochemical signatures.

“So we firmly believe the new technologies will bring forward new discoveries, we just need the boots on the ground [from the juniors].”

McIntosh said he expected the combination of big data and other mineral exploration technologies would eventually lead to the discovery of major new deposits.

He disputed the idea that Australia had hit a kind of “peak discovery”.

“I think new technologies have only just begun to peel back the surface cover of new deposits,” McIntosh said.

Similarly, while exploration budgets were lower, that technology could help close the gap, he said.

“[One] myth we need to address is that lower exploration spending cannot also be higher quality spending,” he said.

“Technology and productivity means we can do more with less; we can be smarter with our dollar both in the lab and in the field.”

New technology front opened

Rio Tinto is no stranger to big data, and has used it on several occasions on its own mine projects over the past several years.

It built a 3D visualisation system intended to generate value worth in excess of half a million dollars a year.

It has a data analytics centre in India it uses "to improve global equipment productivity through the use of big data", and a similar facility in Brisbane.

And a project attaching big data analytics to 900 haul trucks allowed it to defer around $248,000 in maintenance costs.

However, Rio Tinto’s new strategy to establish itself as a big data broker is likely to draw plenty of attention, particularly from resources rivals.

Some of those rivals, such as BHP Billiton and Roy Hill, have moved on similar technology trajectories as Rio Tinto in the wake of the latter's successful Mine of the Future automation program.

However, as Mine of the Future has progressed – and amid a sustained depression of the resources sector – it has until now been unclear where Rio Tinto saw the next technology-driven opportunity to be.

While Rio Tinto hopes Australia’s juniors will help it find value in its big data troves, other resources firms are wanting to automate the querying process.

Woodside, for example, is hoping to deploy IBM’s Watson cognitive compute engine to search across historical data.

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