How the Mining and Energy Industries Benefit from Data Analytics

Perhaps it’s a given that real-time analysis of big data is a mechanism that’s been adapted by sectors like retail, transportation, and disaster response. Big data solutions have enabled fast, easy, and efficient movement of data between disparate systems and within complex environments—all while minimizing latency or the lag time that occurs between these transfers.

The ability to move such high volumes of data, at significant speeds and with 24-hour real-time accessibility, translates to notable increases in business efficiency and profit. This proves especially useful at a time that people’s dependence on technology has increased, and at a time that they expect high-tech solutions when purchasing, commuting, or transacting with big companies and government offices.

But the application of real-time analytics is even wider than most people know. Some of these processes also stand to impact the energy and mining industries. Let’s take a look at how big data has made its mark on energy and mining, and the benefits that it’s affording these two industries.

How Big Data Impacts the Energy Industry

Issues like climate change, the increasing cost of commodities, and price fluctuations in electricity and fossil fuels have changed how we think about energy usage and production. With the rate that we are emitting carbon dioxide and widening our eco-footprints, the way we consume energy is increasingly less sustainable for our environment.

Luckily, the energy industry has technology on its side. Big data and computing add an information layer for power companies to work with, and these drive more sustainable technologies such as smart grids. Smart grids are named as such because they are equipped with smart meters and sensors, enabling real-time monitoring of power consumption levels in specific areas.

This means that energy usage data of high diversity can be received at high volumes. The concerned players can then monitor consumption by the hour, or per electrical appliance used. Access to this kind of data helps spearhead energy flexibility and, eventually, more cost-efficient and environmentally friendly ways of utilizing our power resources.

Real-Time Analytics and Improvements in the Mining Industry

Complex data sets are also part and parcel of mining, but this is an industry that’s only started to benefit from analytical technology in recent years.

However, this family of technologies can be particularly helpful to mining, such as in the cases of artificial intelligence and machine learning (AI/ML) and real-time analytics for predictive maintenance, safety compliance, and optimization of mining processes.

Concrete examples of how real-time analytics can be involved in mining are: monitoring mining activities like the rate of ore extractions and separations; acquiring real-time data from the environment (gas concentration, coal dust, oxygen levels) via sensors; and assessing the viability of mining instruments (power, operating pressure). Of great importance is how real-time analytics can help predict machine failure; for example, on the railway cars that transport miners to and from the tracks, therefore ensuring their safety in the workplace.

Thus, real-time analysis of big data in the mining industry can aid in operational efficiency, scalable mining technologies, better workforce management, and greater safety compliance. It’s a technology that can help miners, operators, and manufacturers all work together for safe, profitable, and efficient mining practices.

Conclusion

In summary, big data can do a lot of things: increase productivity, enable smarter decision making, and maximize available resources. But for how much real-time analytics can increase business dividends while lowering the cost of operations, the technology has yet to become the norm. Several reasons might play into that circumstance—that it’s too expensive to invest in, and that adapting this kind of system might be too big and risky a change.

Regardless, it’s exciting to count the possibilities. We’re hoping that big data will expand even further in these industries, and way beyond.