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Data, mining & data mining

Big data is here to stay. Like in many sectors, the resources industry has accepted the use of big data as a necessity in an era where much is outside their control - including tighter margins, price fluctuations, slowing Chinese demand and a rise in geopolitical instability.

However, in many ways the relationship between the natural resources industry and big data is still in the courtship phase. In other sectors big data is already married into the industry. 

The fortunes of Google and Facebook are based on extracting value from big data. Data-based fintechs are upending accepted pecking orders all over banking. Amazon’s algorithms are coming to smash the Australian retail sector.  Where is resources in all of this?

"For mining, more data-driven change lurks just beyond the horizon and it’s crucial the sector is properly prepared for what’s next."

For mining, more data-driven change lurks just beyond the near horizon and it’s crucial the sector is properly prepared for what’s next. Moving into 2018 and beyond, natural resources firms will have to apply big data beyond the production process of their operations to flourish – and survive. 

Asset utilisation

Some firms have been slow to embrace new technologies, hampered by conservatism and a devotion to established ways of operating. But many are coming around to the potential of big data.

When the next commodities shakeout comes, it is tech-savvy resources firms who will be best placed to weather it.

Cloud-based storage, the Internet of things (IoT) and artificial intelligence are likely to play big roles in the resources sector over the next decade.

A number of industry CEOs have made leveraging data their main priority moving forward.

In 2015, Australian oil and gas producer Woodside set up a team to harvest and interpret the masses of data generated by its operations in order to improve efficiency, safety and profits at its LNG operation in northwest Australia.

Other companies are using big data analysis to review real time data feeds to both optimise maintenance scheduling and predict future plant and equipment breakdown -  a metric known as ‘Time To Failure’ or TTF.

The result of these big data analysis is resources companies can significantly enhance operational productivity, reduce unit operating costs, and potentially reduce sustaining capital intensity.

Then there are robots. Many diversified miners, like Rio Tinto, are using robot trucks or driverless long-distance railway systems – programmed according to big data-based inputs – to automate parts of the production process, achieving labour and cost savings. 

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The next frontier

In recent years, many natural resources companies have aggregated data to identify the quickest, safest, and most cost-effective ways to discover and extract minerals from the earth and pre-empt major hazard and risk events.

Some firms can now pinpoint and optimise their daily operating productivity, looking back at best daily performance up to five years ago. As one ANZ client put it - “we use data to ensure we play our best game every day.”

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Exploration

Big data has a big role to play in the exploration phase. Searching for undiscovered deposits requires acquisition and interpretation of massive multi-facetted sets of data, which provide critical insights into the mineral potential of the targeted area.

The stakes are high: in order to find a needle in a haystack, miners must rely on largely visual interpretation of these processed and complex digital data sets to decide if they will allocate scarce capital.

These interpretations are done by human eye and involve assessment of digital data which has been transformed onto maps showing rendered colour and grey toned imagery.

Developments in artificial intelligence will soon summarise innumerable layers of data in mere seconds with a very low margin of error – augmenting human talent and intuition in ways previously unimaginable.

We are seeing this already in companies like Woodside which has created and trained a cognitive assistant (Avatar) called Willow which is able to engage with different Woodside specialists to assist with the real time problem solving. Willow is able to access millions of current data files to support the user’s verbal and written requests.

The marketing of resources is another area ripe for big data applications. Soon firms will be able to better focus marketing efforts and more-efficiently identify the most appropriate customers based on data-driven analyses of the e commodities they are selling.

Data DNA

So what can natural resources companies do to take these crucial next steps in the area of big data application?

The first thing to do is to ensure a dedicated data strategy and chief data engineers are part and parcel of business development plans at the most senior level. Miners must foster company-wide awareness of data rather than simply pay lip service to trends.

Working with data-driven external partners who can help realise gains is also crucial, as excessive insularity can trap a company in a state of arrested development.

Moving forward, leading natural resources companies need to work with partners which provide bespoke – rather than generic – dashboards around capital risk and liquidity management, ensuring big data will become part of their DNA across entire business cycles from exploration to marketing. 

Aaron Ross, Global Head of Resources, Energy & Infrastructure at ANZ and Jonathan Bloch is Head of Strategic Banking, Resources Energy & Infrastructure at ANZ.

Additional contributors to this article included Richard Schroder, Head of Analytics & Platform – Client Insights, Institutional and International Banking at ANZ; and Frank Van Rooyen, Head of Natural Resources, Australia at ANZ

The views and opinions expressed in this communication are those of the author and may not necessarily state or reflect those of ANZ.

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