Big Data Analytics: A Smarter Way To Mine

Indranil Som

The mining industry is sitting on a virtual goldmine of very large and complex data sets. But the lack of investment in analytical technology prevents the industry from unlocking its true value.

Today, it is slowly waking up to the fact that Big Data is not only a game changer – it is a necessity with gains in operational efficiencies, from automation and real-time planning to total workforce management and safety, and much more. The key is to analyze this data, identify where it can be most useful, and use it for competitive advantage.

Advantages of Big Data to the mining industry 

The whole process of mining is extremely complicated and involves many types of equipment, technology, and science (IT, engineering and geology). Moreover, companies need to ensure the safety of miners and proactively manage compliance. Data analytics helps improve processes and reduce operational costs and losses.

Data analytics can be used in every stage of the mining process.

  • Improve efficiency: With automated and agile processes at the operations level, ore extraction, processing, separating, and concentrating the best components is faster
  • Improve logistics: Transportation is core to the mining industry, and automating the process can greatly benefit it. Data analytics can help identify areas of inefficiency and areas for improvement
  • Smart collaboration: A single source of data across the organization improves collaboration between various departments that build newer collaboration models with OEMs, operators, and service providers, for monitoring via cloud or networks
  • Business intelligence: BI helps identify the areas that are real cost drivers, enabling faster decision-making and improved performance
  • Safety and security: By capturing operational, people, and sensor data, Big Data analytics provides actionable insights based on real-time monitoring of people in mines (location, heart rate, temperature), environment (gas concentration, CO, coal dust, wind speed) and equipment (power, operating pressure, speed). The analysis can help identify risks such as a tunnel collapse or incidents of near misses, thus ensuring safer mining operations
  • Smarter procurement: A data-driven system makes it easier to manage and monitor current and future needs of spares and services, and optimizes inventory of spare parts. Big Data makes price negotiations and spend analytics speedy and efficient while reducing overall procurement costs

Translate insights-driven actions into measurable outcomes with data analytics

Mining companies can speed analytics, aggregate and evaluate data, and crunch volumes of internal and external data to identify trends and make better predictions, improving business performance.

In summary, harnessing Big Data presents mining industry with incredible opportunities to effectively leverage the enormous amounts of data to respond to new market expectations, to build safe, sustainable, and profitable mine operations, and improve the lives of their employees and the environment.

For more insight on data analytics in the mining industry, see Data-Driven Mining: The Role Of AI And Machine Learning.


Indranil Som

About Indranil Som

Indranil Som is the Digital Leader for Energy and Natural Resources industry at SAP India, engaged in consulting with C-level executives to enable organizations unlock business value through technology driven business transformations. He has had over 16 years of management consulting experience with a combination of strategy and technology engagements, encompassing scoping, planning and execution, with leading international firms.