DATA MINING biological characteristics are necessary for the mineral appearance . It is then possible to predict what minerals might be missing , as well as where to go to find new sources .
Big data is also helping the physical processes of mining too . Jason Knuth , Product Manager of Analytics & JoySmart Solutions at construction and mining group Komatsu , explains : “ Every machine is unique in terms of the challenges they face . This includes tough operating conditions and variety of materials . It ’ s why we collect so much data off of our machines .
“ In order to accurately predict machine behaviour , you have to understand what normal looks like . You have to build customised models that are unique to each machine and the conditions they ’ re in . Then as the conditions change , so do the models and predictions ,” he says .
“ We focus on looking at abnormalities in the data from typical operations and highlight abnormal behaviours . You can ’ t prevent what you haven ’ t seen . But the more data we have and the more data we process , the more we understand and the more we can help prevent issues for our customers .”
For Komatsu , this sheer quantity of data is a good thing . “ For our engineers and data scientists , there ’ s never enough data ,” Knuth says . “ More data means more visibility , more clarity , increased modelling , and increased information . All of that leads to better response times for our customers and a better understanding of equipment needs for future development .
“ Data storage is expensive and that can be a concern of course ,” he adds , “ but Komatsu has a data management strategy that allows us to archive the information at a lower cost , prioritising the most relevant and recent information while still allowing access to archived data .
“ Because we are the original equipment manufacturer , we can do increasingly more computing and analytics on our own equipment , and less in the cloud . Getting that computing power closer to the machines helps with data storage and speeds up the flow of information and processing . Ultimately , that is our focus , turning the raw data into useful information and actionable items , and getting that into the right person ’ s hands in time to improve processes and prevent issues .”
19