Big data and analytics, improving efficiency in mining and agriculture
Hernán Orellana, general manager of Telefónica's R&D center in Santiago, Chile
There's ever more information on individuals available online, while the volume of data grows exponentially. Application and content providers such as Google and Facebook have been able to exploit this enormous amount of information, developing new and complex services for their clients, giving them more value and benefits.
In industries, productive and business processes are also generating infinite amounts of information and useful data. Traditionally they have been used to update statistics, monitor processes or even for business intelligence. In today's environment of technological development, it's possible to extract, process and arrange large amounts of data in a useful way for business purposes.
On the one hand, the devices and sensors that allow us to extract useful information from processes, machines and productive elements are simpler, smaller and cheaper than ever. On the other, the capacity to process large amounts of information is also cheaper and more efficient.
This is where analytics and data science come into play. It involves the generation of algorithms and models that, based on large amounts of data, allow them to be correlated to predict events in order to find efficiency and optimization points. But in practice, how does the above apply to business?
With this in mind, Telefónica created an R&D center Chile with the purpose of developing state-of-the-art technological solutions to increase productivity and efficiency in Chilean industry, and improve quality of life for ordinary Chileans. The Telefónica R&D center has developed solutions for the mining and agricultural sectors based on analytics and big data.
For example, a web access platform called GEO was developed for the mining industry. It integrates the production and energy consumption data of a mining plant process in real time and, through a mathematical analytical model, allows the identification of regular behaviors to optimize energy consumption. After 12 months of testing at the Matta plant of ENAMI, productivity improved by 31%.
For the agricultural sector, the center has developed an irrigation management application called Optimal Crop, which integrates large amounts of data extracted by sensors in the fields for the application of a mathematical model of prediction. This model of irrigation prediction through real-time measurement of soil moisture, and its correlation with current and predicted climate variables, leads to a recommendation of when and how much to irrigate. In Agrícola Rivarola, after a month of use of the application, it was possible to reduce the consumption of water and energy for irrigation by 20%.
The above are just some examples of what analytics, data science, big data and knowledge management can do to improve the productivity and competitiveness of industries and ultimately of the Chilean economy. You just have to dare to invest in these technologies that will make the competitive difference of tomorrow's businesses.
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Hernán Orellana will be one of the speakers at the SingularityU Chile Summit to be held on March 14 and 15 in Santiago. BNamericas subscribers can obtain a 10% discount to attend the event by completing the following form .
The content is entirely the responsibility of the author and does not necessarily reflect the views of BNamericas. We encourage guest columns, and those interested in submitting one for possible publication should contact the editor at telecom@bnamericas.com.
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