In a data-dominated world, the mining industry remains a labour-intensive enterprise and one that relies on long-term planning which, when inaccurate, can massively impact bottom lines.
But the scale and complexity the data generated in the process of mining may in fact create more value than any other technological innovation in the industry’s history.

According to Magda Honey, mining executive for IBM South Africa, increasing efficiency, reducing overheads, informing decision-making, improving safety and empowering mines to be proactive and agile rather than reactive and cumbersome all come as a result of better insight into data.

“Decision makers are understandably reluctant to make drastic changes, relying instead on inference and history. But these same times demand innovative approaches if mines are to respond to the range of new pressures. Big data analytics makes it possible to identify the necessary changes and make them without the risk they usually entail by providing research to support arguments for change.”

Traditionally, IBM’s role in mining has been limited to hardware supply and information systems, but big data is helping to change that. IBM has invested heavily to develop a range solutions to make sense of big data and use it to drive innovation, identify potential pitfalls and stimulate growth. Added to this is IBM’s global expertise in the resources sector which has now been extended to South Africa.

This year IBM will bring to market a number of uniquely South African big-data offerings through partnerships with companies like MineRP. By integrating geospatial, temporal mining technical data with ERP Solutions like SAP, through patented methods including GeoFinance and GeoInventory, MineRP and IBM are able to store and analyse both structured and unstructured data and turn it into useable information and valuable insight. This allows for end-to-end simulations across the mining value chain, replacing guesswork with information-based strategy.

MineRP and IBM can provide mines with an off-site Integrated Operations Centre that delivers normalised and standardised data and information to and from decision support systems. This positions mines to improve production and reduce costs across a range of mining activities and makes it possible to undertake coordinated decision-making that is based on current, reliable information.

Not only does this provide an unprecedentedly comprehensive overview of operations, but it also creates an early warning system that monitors key risk indicators and allows mines to be proactive rather than reactive and to act upon information in real-time.

By properly harnessing big data across disparate operations, IBM and MineRP’s solutions can expose risk, identify growth opportunities, improve and refine planning processes, and ensure that production and output are maximised while minimising costs.

Information analysis does more than streamline processes; it can be used to identify potential social upheaval. In the age of permanent connectivity, social media and unprecedented mobility, preventative, predictive analytics make it possible to conduct social sentiment analysis and identify issues before they become crises.

Instinct and experience still have their place, but they can stymie progress. Data, properly utilised, can help change that. IBM’s work on supercomputers like Watson and other cognitive computing systems makes it the perfect partner for the mining sector. Rather than relying on guesswork and estimation, properly managing and processing mining data could make it possible to more accurately forecast demand and respond to it.

Africa’s mineral resources are one of her biggest assets and managing these resources properly is key to ensuring success on the continent. By developing partnership with educational institutions, innovative companies and the mining industry itself – IBM is intent on playing a pivotal role in helping mining companies increase efficiency, reduce overheads, improve safety and empower mines a more proactive and agile industry using big data and analytics.