MMForum: How Data Analytics Is Important to Mining Industry
As we are living in the 21st century where technology is advancing and changing the rules of business, many companies are starting to adopt enterprise resource planning (ERP) systems, e-commerce, and other internet-based systems which helped them in capturing organisational data digitally.
Currently, social media is on the rise, increasing of people using smartphones or tablets, and the use of cloud computing, these “internet of things” have increased the large volume of data at a quick pace and which is available to business. Gartner research show that by 2020, about 230 billion devices will be connected to the internet which has continually sending out huge amounts of data that need to be stored and analyze. These information indeed will be a great “helps” in gaining competitive advantages and to improve the organization productivity and efficiency.
Big data will affect the mining industry companies and information such as voice recordings, pricing data, social media posts, images, and geo-location information collected are critical assets for the companies to gain new insights into business performance, opportunities and risks. Recently, Singapore government, transportation companies, and IT companies are trying to gather passenger data, which is also a form of big data, for analysis in order to gain new insights to improve commuter experience in boarding the trains to ease transportation woes.
For mining industry, big data can help them in like predicting whether their mining equipment is going to fail by using real-time analytics from both equipment sensors and operational data. These can be derived from the use of predictive analytics which helps to extract real-time data on a variety of operational parameters such as equipment settings and readings from the company’s production control systems. In the case of using the equipment sensors from the “internet of things”, “internet of things” has helped to solve the huge financial burden for mining industry companies. Thus, this can increase the efficiency in business.
Additionally, mining industry companies can apply data analytics in ensuring health, safety, and environmental protection. This is to help the companies in investigating and improving processes and forecasts on management that are related to the above protection issues. Also, the companies need to strengthen their safety efforts by using innovative approaches that extend the safety by providing training and procedures.
With the application of analytics and optimization capabilities to the data, this helps mining industry companies to make better production decisions that ultimately extend the life of mines, improve production, yields, and reduce environmental risks. Thisallows the companies to impress their clients with the best portfolio of contracts. For predictive analytics capabilities, it helps to fill in the gap between data and action by helping management produce reliable conclusions about the current conditions and future events in such areas as asset management and it helps to analyze which mines produce the most profitable output. Lastly, optimization will go through modelling of changes that lead to actionable insights where this is the part that the visualization and modelling starts to come in with the production of dashboard which helps to implement better decision. Now, “internet of things” has indeed helped mining industry increase in efficiency in business and indeed changed the working environment as technology always change the rules of business.