| | MARCH 20199CIOReviewthe ability to easily track and analyze trends and detect patterns. Operators can spend entire shift rotations without ever receiving feedback, positive or negative, about their performance. Supervisors at most mines spend an hour before and after the shift finishes, doing paperwork, entering data into a variety of systems, and even during the shift, are expected to drive around the pit to get a visual perspective of how the mine is performing. This low-feedback, paper-work intensive work has remained unchanged for decades. However, the apps would reduce the time it takes to complete forms and completely eliminate the need for data entry. Operators can gain feedback on their own performance and that of their team, through engaging user experiences, similar to how multiplayer online games present highly engaging feedback at the end of every match, in this case, shift. Supervisors would also have a far clearer perspective and can even spend time in the pit when and where it counts because all the data is at their fingertips. However, the sheer volume of data that these sensor platforms (tablets) can generate is staggering. For example, a deployment of our tablet-based fleet management system at a coalmine, where every tablet calculates proximity to one another through Bluetooth signals, frequent GPS heartbeats, operator inputs, digital forms, and automatically triggered records such as cycle sequences, a single week produced 7 gigabytes of data (not including photos), this was for only 14 machines. However, a relatively new approach to storing and processing data, generically referred to as "Big Data" will help solve the volume-issue.The Big Data revolution, that is transforming other businesses, is based on the high volumes of data drawn from web, sensor, and other data sources, that are mostly in the form of unstructured data. Other than data collected from next-gen mining apps and IoT, most data in mining is the more common structured (SQL) data and time series data. These data sets could be still processed using more traditional techniques, but executive pressure to join the "Big Data" bandwagon will undoubtedly force CIOs to investigate the technology's applicability to their own operations.Processing the data through complex algorithms is actually the least time consuming step in an analytics project. The most time consuming step is the data preparation phase, where data, typically from disparate sources is integrated into a common contextual model using domain expertise. Any CIO seriously contemplating engaging in big data efforts, beyond one-off projects, should be prepared to create an integrated contextualized integrated data infrastructure. The benefits of such an infrastructure will help not only facilitate Big Data analytics, but greatly enhance basic analyses because most analytics at mine sites is currently undertaken using spreadsheets. By far the most challenging step in a big data project is to transform workflow, in other words, to gain action from data.The most immediate transformation in mining in the next few years will be the introduction of tablet apps and IoT as data collection and control systems, mostly at small and medium sized mines. As the current generation of fleet management systems that were recently purchased reach the endof their life cycle, it too will be replaced by these lower-cost systems. Big Data will continue to excite executives due to the media attention it receives, and so perhaps, with such high-level corporate attention, big data projects could result in the reengineering of workflows in mines where other data-driven technology has fallen-short.Although mobile fleet automation undoubtedly gets the most interest, the level of control, cost, isolation, inflexibility, and complexity will prohibit its use to only the very largest and most sophisticated mines. Any CIO seriously contemplating engaging in big data efforts, beyond one-off projects, should be prepared to create an integrated contextualized integrated data infrastructure
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