Mining data, not diamonds: iKVA on the power of AI 

The mining sector generates huge volumes of data on a daily basis; a single drill hole can produce up to 200GB of data, with data points being generated from thousands of sensors and instruments deployed in all areas of raw material exploration and extraction. 

However, this data is frequently stored on siloed legacy systems, leaving people unable to access it and unlock much-needed insight. Meanwhile, the industry faces increasing pressure to meet the growing demand for critical raw materials, such as graphite and cobalt, in the face of geological scarcity, rising energy costs and infrastructure challenges.  

In today’s globalised world, the complexities of fragmented digital ecosystems, and large teams located across the world, are driving the demand for improved knowledge management in many industries, including mining. Mining operations are complex and often decentralised, requiring the coordination of a number of contractors and subcontractors. 

In this landscape, business-critical decisions can often be made via non-traditional channels, such as instant messenger, webchats and electronic messages. Accessing this information is essential, but difficult. Businesses are also required to deliver an increasingly large number of documents, including tenders, reports and evaluations, to comply with industry regulation; and easy access to information is essential in order to do this efficiently. 

All of these data sources are stored in different repositories, and may not even have a search interface to enable easy access. The diverse computing systems used by global companies, combined with poor systems and software integration, can contribute to a further increase in business risk. 

Smart mining, or mining 4.0, is poised to deliver significant benefits across the entire industry, by integrating digital innovation into processes that will boost efficiencies and productivity in areas such as extraction, hauling and processing. 

McKinsey estimates that, by 2035, data analysis and technologies such as artificial intelligence (AI) will save between $290bn and $390bn annually for mineral producers. Harnessing AI, advanced machine learning and vector mapping technology will enable organisations to leverage unstructured data from multiple sources, to increase productivity, reduce wasted time and improve profitability. 

New solutions, such as those developed by iKVA, can be cloud-based for instant implementation and integration with existing workflow systems. This completely avoids costly upgrades to the legacy systems that are still frequently deployed by organisations in this industry. Industry giants, such as Rio Tinto, are already embracing AI technology and benefiting from the business advantages it delivers. 

From dwindling reserves of raw materials, to the escalating costs associated with exploration, prospecting is growing increasingly difficult. Advances in AI technology offer organisations an efficient and accurate way to identify financially viable ore deposits, by applying advanced machine learning to geoscience data to determine why deposits form in a geographical area, and to predict the potential volume of available material. 

AI technology can combine historic data, survey results, chip samples, soil samples and drill results to identify and visually map areas for exploration, enabling firms to dig deeper, explore further and improve the viability of mining. 

AI-based software processes data more than 1,000 times faster than humans can, with extreme consistency and accuracy, reducing labour and time intensive processes and optimising asset management. 

Mining firms invest considerable resource in producing complex documents, such as sustainability reports, permit requests and licensing agreements. Accurate information is vital for limiting business risk and AI technology can be effectively utilised to ensure that documentation is consistently aligned to the organisation’s existing internal standards and strategies, as well as complying with external regulations and certifications. 

iKVA’s Discover For Engineers proactively and intelligently suggests company information that is relevant to the document that an individual is working on, in real-time. This seamlessly integrates into a user’s workflow, eliminating the need to stop working to search for and discover information.

AI tools such as these can ingest data from almost any company source without the need for taxonomies, ontologies or meta-data, regardless of language, and guarantees that the resulting information is more accurate and relevant, which saves time and reduces business risk. 

With pressure to deliver cost efficiencies increasing in reaction to external market demands, preparing accurate tenders is crucial, but this process can be time consuming, costly and labour intensive. AI solutions such as those developed by iKVA can help procurement teams to identify, prioritise and respond to time-sensitive tender opportunities. 

They enable the review of incoming Request for Proposal (RFP) opportunities, alongside historic successful submissions, based on the organisation’s strategy for contract prioritisation. These are then ranked in order of potential success, which reduces the time wasted on unsuitable or unsuccessful proposals. 

This new way of managing RFPs provides bid managers with actionable insights to make faster business decisions, create stronger proposals and progress new projects rapidly. The same technology can proactively provide relevant information to bid writers, offering actionable recommendations based on the company’s previously defined goals. 

The mining sector faces increasing pressure to meet the demand for raw materials, which are essential to the world economy. Leveraging advances in AI technology is an opportunity for organisations to unlock insight to inform operational efficiencies, optimise tender outcomes and gain valuable business insight to remain competitive in an evolving global landscape.