Mining for data
Mining giant Rio Tinto believes it has already saved $90 million through better data processing. Its new Processing Excellence Centre is staffed by 12 mineral experts who continually scrutinise processing data from five coal sites in Australia, as well as operations in Mongolia and the US. Around 30GB of data is streamed in real time every day with a lag of only 100ms and then examined by 20 different analytic systems. Through spotting anomalies, it has enhanced a range of processes and procedures, including increasing the recovery of copper and gold at Rio Tinto’s Oyu Tolgoi mine in Mongolia.
Map to success
Mapping technology is driving a raft of fascinating efficiency and productivity improvements using big data. One company reaping the benefits is Australian energy company Ergon Energy, which uses mapping technology, specifically a product called Google Maps Engine, to map the growth of trees near and around it’s entire 150,000km of poles and wires across Queensland. The technology uses sensors to capture images and data that are then turned into 3D models of vegetation growth, which guide the company’s pruning program. Ergon Energy estimates it may save close to $60 million dollars over the next five years, because it can now respond with the right work at the right time.
Continuing the forest theme, Global Forest Watch estimates forest usage, change and tree cover by using satellite imagery and Google technologies, and reveals how many millions of hectares of forest are lost every year. The aim is to help people and governments all over the world manage forests better, increasing conservation. It reveals Australia lost 5.9 million hectares of forest from 2000-2012, only gaining 1.4 million new hectares in the same period.
Billions of dollars are wasted in productivity due to undereducated citizens. To improve education, the UK has developed the most comprehensive databases of school children in the world. Tracking 600,000 children from 3000 schools, it now has 10 years of data including exams, socioeconomic status, geography, transport, free meals and behaviour issues. Analysis already suggests that socioeconomic factors are less important than originally thought, but school performance and responsiveness, as well as science and technology courses, are critical.
Aimed at urban foragers, Falling Fruit highlights over 700 types of edible plants in over half a million urban areas worldwide. From blackberries and kumquats to macadamia nuts and garden parsley, hundreds of locations are identified around the world. Falling Fruit is built on public data as well as contributions by individual foragers and demonstrates the power of crowd-sourcing data.
What’s interesting from all these projects is the continued role of human input in both collecting and analysing data. For any business planning a big data project, getting timely and accurate reporting to build data sets is critical. We’re still some way off from a fully sensored, fully automated world.