MINING INDABA • the perspective of the employer , it has become increasingly difficult to find and retain highly desirable mining talent . The recruitment has even glossed over mining into computer and data science . The skills gap not only reduces the number of candidates available in the market , but also decreases the common ground between existing management and willing talents ( e . g ., an understanding of suitable project and staff management models ). It is even more difficult to create a functional group of , for instance , geodata scientists or automation engineers . This is not surprising , as take for example an artificial intelligence or data science doctorate , because of the highly trans-disciplinary nature of their skills , they can succeed in practically any modern industry that can manage the high-risk nature of their work . What would be their objectives in the choice of a career ? For many , maximising financial returns is an obvious choice . For others , finding sustainable and interesting work is another . While financial competitiveness will be decided by the market , the sustainable and interesting work aspect can be addressed through the education system .
To foster attainable interest , it is imperative that mining graduates are trained , as early as possible , in the key prerequisites of transdisciplinary techniques : mathematics , statistics , and computer science . Once these requirements are met , it would be much easier to train a mining professional in selected topics in machine learning , and data science , including robotics at the graduate level . This lowers the barrier to entry of mining into trans-disciplinary domains . To create sustainable work , it is imperative that some mining professionals are trained in sensor and data engineering , such that sustainable and system-type data gradually replaces static or expensive data , and therefore sustains adoption of transdisciplinary techniques in the industry .
At the highest level , it is important to recognise that as mining becomes more data-driven , it is important that skills be categorised into a modern and formalised pipeline or model ( Fig . 2 ). For example , in the simplest configuration , a data pipeline consists of data generation , data management and data usage . In this model , it is obvious where various mining professionals fit in – those who are primarily specialised in observations , sensors and data engineering fit into data generation and their skill progression and talent management must cater to their role as data generators . Those who are interested in databases , hardware and software , data specifications and standards easily fit into the data management segment .
Those who are interested in using data as analysts , scientists and engineers fit into the data usage role . Once this pipeline is built , all other beneficiary activities become practical , because implementation becomes plannable and requirements obvious . For example , automation , digital twins , remote control and robotics all rely on high quality data and analytics . The explicit delineation along the data pipeline and other similar modern operating models ( e . g ., the modern digital operating model ) is a requirement because many activities in the mining industry that used to be implicit have become more complex and detailed .
Hence specialisation is a natural evolutionary progression . For instance , it no longer makes sense to generally train observational scientists and engineers to become experts in machine learning ,
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Figure 2 . Future division of skills in the mining industry .
African Mining Publication because : ( 1 ) isolated observations do not accumulate to sufficient data to sustain machine learning ; ( 2 ) those who are interested in outdoor or field work will not have as much time to spend indoors ; and ( 3 ) machine learning and observations require a dichotomous set of pre-requisite skills .
For those willing to take an active role in shaping the future of mining , participation in the education and training of young mining professionals is important for the advancement the entire minerals industry . Judicious and decisive recognition of current skills gaps and remediation of mining education in a timely manner could make mining : safer through automation ; more environmentally friendly through better monitoring and control ; more sustainable through more selective extraction , processing , and waste reuse ; and much more human-oriented through the use of machines and remote control for tasks that are not human friendly ( e . g ., repetitive or hazardous ). •
Acknowledgement : The author is grateful to his colleagues for their comments and input on this article : Professor Cuthbert Musingwini , Head of the School of Mining Engineering at the University of the Witwatersrand and SAIMM Past President ; Dr Steven Zhang , Visiting Senior Lecturer at the Wits Mining Institute ; and Dr Sihesenkosi Nhleko , Lecturer at the School of Mining Engineering at the University of the Witwatersrand .
African Mining
Professor Glen Nwaila , director of the Wits Mining Institute at the University of the Witwatersrand .
Trans-disciplinary education is probably the most difficult to accomplish after the tertiary level .
African Mining • January 2023 • 37
Supplied Professor Glen Nwaila Wits Mining Institute at the University of the Witwatersrand