Future Skills Requirements

Paulo Martins and AH Soofastaei

Advanced-Data Analytics Company Profile – Operating Model

The Analytics of big data has been one of the main topics in engineering for many years, and no one can deny that the application of Advanced Analytics (AA) tools is transforming the way many companies do business and opening opportunities to competitive advantage. There are some related questions in this field. For example,

  • • what are the formula or critical success factors for exploiting data analytics? and
  • • how can a company be transformed to take advantage of data and advanced analytics tools?

Behind all efforts for successfully deploying AA, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), companies need to rethink their data culture and focus their strategies to emphasize data and analytics. Recently completed research by McKinsey shows that there is a gap between laggards and leaders in adopting AA. These days, we see some companies are doing wonderful tasks; many are still trying with the basics; and many are feeling undeniably overwhelmed, with managers and members of the rank and file questioning the return on information initiatives [1].

To achieve this goal, many companies have established hybrid organizations, which include centers of excellence, analytics sandboxes, or innovation labs to derive benefits more rapidly from their data investments [2]. For example, ВНР set up its Innovation Center in 2017 to develop products and services that integrate data and devices to make BHP's work more accurate than ever [3]. Another mining company that has intensified the data analytics investment is Vale, which inaugurated the AI Centre in 2019 as part of the evolution strategy that aims to influence the implementation of innovative and disruptive technologies in all sections of the mining business, always focusing on the results through data analysis and process improvements [4].

Further examples of mining companies leading the data analytics race are Rio Tinto [5], Barrick Gold [6], and Anglo American [7].

In a digital analytics strategy, mining companies can explore three sources of value levers: value creation (encompass projects that improve production or product quality), value protection (encompass projects enhance safety or deal with environment or community challenges), or value definition (projects that involve the definition of ore reserves) [8].

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