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

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 its being used in the sector has grown substantially over the past couple 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 each day, but neither operations nor managers, or 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 on 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 hopes to see big dividends and lower their operational costs based on the many promises offered by Data Analytics.

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

It goes without saying, that these different moving parts are the differentiators that improves the chances of Data Analytics onto 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. Being able to identify and select throughout this data what do you need to make informed business decisions and make a positive impact on the bottom line of the organisation 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 definitely many other phases of the mining process where Data Analytics can be put to use. From the extraction of ore, processing to separating and concentrating the usable components. One of the areas that may be the most inefficient for mining companies is in the logistics part of it. Much of the data for this transportation comes from the use of rail to move goods to the port. Many of the deficiencies reported by the company’s 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 not only increase overall machine reliability but also improve the efficiency of business operations and end up potentially saving millions of dollars.

Another usage for Data Analytics may also be in providing safety of the miners. Many mining companies around the world 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 send out an warning to the miners to evacuate before its 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 on all the potential stemming from Data Analytics. With promises of better safety procedures, increased efficiency and productivity, and lower costs, any company not yet utilizing Data Analytics will likely do so soon.