HOW DATA ANALYTICS IS IMPACTING THE MINING INDUSTRY AND BRINGING REAL VALUE TO MINING COMPANIES

Data Analytics is provoking major changes within the business world. Companies are getting benefits from data analytics to positively impact their performances

We all know that Data Analytics is provoking major changes within the business world. Companies benefit from data analytics to impact their performances, growing revenues and efficiency positively.

Undoubtedly, one area that sees a lot of potential in Data Analytics is the mining industry.
Although Data Analytics is not a new thing in mining, the volume and, most importantly, the extent to which it’s being used in the sector has grown substantially over the past couple of years.

For an industry that makes billions and billions of dollars in business, Data Analytics should not be seen as a luxury or as something we must do in the future, but as a necessity.

More and more data is being collected daily, but neither operations, managers, nor corporate executives can consolidate and fully take advantage of it.

On the other hand, mine operators and mining executives are under tremendous pressure to meet profitability goals in this unfavourable market.

Faced with rising energy costs, scarcer high-grade ores, declining commodity prices, and tighter profit margins, it has never been more important for a mine to make the most of its data.
Companies certainly hope to see big dividends and lower their operational costs based on the many promises offered by Data Analytics.

The mining process is complicated and requires many different equipment, technologies, and sciences such as IT, engineering and geology.

It goes without saying, that these different moving parts are the differentiators that improve the chances of Data Analytics making a big impact in this industry.

One instance of this could be in predicting when mining equipment could fail. Not so long ago, I had a chance to meet up with IBM and see a demo on how they`re using Data Analytics and historical data to predict and anticipate when and if an equipment might fail or be likely to fail.

It is, for sure, a very challenging approach in terms of strategy and implementation.
Some of the key challenges the mining industry is facing right now when it comes to Data Analytics are:

Deciding which data should be collected and analysed. The mining sector, for sure, generates a huge amount of information along the mining chain. Identifying and selecting throughout this data what you need to make informed business decisions and positively impact the organisation’s bottom line is certainly the biggest challenge.

Consolidating data across several systems, vendors and platforms. This is a very difficult challenge in terms of creating a cohesive data system approach.

There are many other phases of the mining process where Data Analytics can be used, from the extraction of ore and processing to separating and concentrating the usable components. One of the areas that may be the most inefficient for mining companies is the logistics part of it. Much of the data for this transportation comes from using rail to move goods to the port. Many of the deficiencies reported by the companies deals with the automated processes of loading rail cars. Data Analytics can help identify the inefficiencies, alerting business leaders where improvements are needed.

In addition, with the proper utilization of the Internet of Things, equipment???s could be outfitted with sensors that send back data on its operations in real time, populating this huge database. By using Data Analytics to make these predictions, mining companies would increase overall machine reliability improve the efficiency of business operations and potentially save millions of dollars.

Another usage for Data Analytics may also be in providing safety for the miners. Many mining companies worldwide have installed automated ground control systems that are used underground or for pit mining. The sensitive systems capture data on vibrations in the ground and can determine the structural integrity of the mining operation. In the case of real, significant danger such as a tunnel collapse or ground slide, the monitoring system can warn the miners to evacuate before it is too late. The same data used from ground monitoring can also be applied to the development of safer drill and blasting procedures

The mining industry is only scratching the surface of all the potential of Data Analytics. With promises of better safety procedures, increased efficiency and productivity, and lower costs, any company not yet utilising Data Analytics will likely do so soon.