ISSN 0718-3291 Versión Impresa

ISSN 0718-3305 Versión en línea

Volumen 28 N° 1, Enero - Marzo 2020

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Datos, Ciencia e Ingeniería

Data, Science and Engineering

Ingeniare. Revista chilena de ingeniería

On-line version ISSN 0718-3305

Ingeniare. Rev. chil. ing. vol.28 no.1 Arica Mar. 2020

http://dx.doi.org/10.4067/S0718-33052020000100002 

Articles

Data, Science and Engineering

Dr Juan Vega Vargas1 

1 Departamento de Ingeniería Industrial y de Sistemas Universidad de Tarapacá Arica, Chile E-mail: Juan_vega@hotmail.com

Nowadays, the volume of data available in companies and organizations is constantly increasing. Daily large amounts of data are generated in the normal operation of the systems. For example, more and more devices, manufacturing tools, and plants are equipped with sensors that collect immense amounts of data concerning themselves and their environment. Added to this data collection, the use of web pages, emails, scanned administrative documents, records of smart devices such as mobile phones or smart devices (internet of things), bank transactions, satellite images, GPS routes, network data social, and many others.

This immense amount of available data has involved the development of new scientific and engineering methods in having systems and procedures capable of storing, processing, and analyzing such data, thus generating information and knowledge.

Big Data is an English term whose translation would be equivalent to "massive data" or "macro-data". The term can be defined as the set of data whose size considerably exceeds the management and analysis capacity of conventional software. However, this concept refers not only to the information (volume), but also to the variety of content and speed with which the data is generated, stored, and analyzed. Additionally, to establish the veracity or reliability and the value that the data finally gives to the organization. These dimensions are the so-called "5V" of Big Data, that is, volume, velocity, variety, veracity, and value of the information.

All this data can deliver valuable information on various fields, for example, process management and optimization, equipment failures, maintenance cycles, purchasing patterns, market trends, and others. In short, they can be used to improve decision making, improve results and performance, save costs, or generate public policies that positively impact society.

In parallel to the growth of Big Data, a new area of knowledge has evolved that responds to their unique exploitation demands. This area of knowledge is known globally as data science. Data science is today a fundamental tool for knowledge generation. Among the objectives pursued is the search for models that describe patterns and behaviors from the data to understand the systems or make predictions. It is an area that has experienced tremendous growth due, among other things, the development in the capacity of computers.

Data science is an interdisciplinary field that combines topics such as data mining, mathematical and statistical modeling, programming, and artificial intelligence. It uses techniques such as fuzzy logic, neural networks, machine learning, and deep learning, among others. Through the use of high-capacity technology, this technology has applications in a wide variety of engineering fields. For this reason, it is of great importance for an engineer to know and master these techniques.

However, there are currently a large number of companies that do not take advantage of the potential of their data. A report by the Chilean-American Chamber of Commerce showed that 78% of the institutions analyzed in Chile do not incorporate this type of technique in their processes, products, or services. Of that number, 14% are in insufficient degrees of use, and only 8% would do so in a generalized way.

In practice, this means that a significant number of companies in the country do not use data for decision-making, nor do they have a protocol for handling sensitive data, nor do they have the infrastructure, specialists, or the appropriate software to incorporate predictive analytics in their operations. According to this report, 8 out of 10 companies are not incorporating artificial intelligence technologies, which is very negative because they should already be investigating how to incorporate it into their work since it is one of the factors that they will need to remain competitive in the market.

The report concludes that the lack of agility of the companies to incorporate this type of tool, which allows higher productivity in the processes can become a stepping stone to achieve sustainable economic growth for the country in the coming years. Therefore, specialized training of professionals in the use of techniques associated with data science is essential. However, there is currently a lack of such professionals. This lack is due to its newness in companies that have yet to focus on the formation of talent to cover this area. In the opinion of Joyanes (2013), professionals who relate to the handling and analysis of data will be strongly demanded in the coming years.

In this sense, the engineer is vital when it comes to the performance of all that stored data. The engineer is who asks the right questions and who gives value to that data in decision making. However, you do not just have to know how to use new technologies. The mere use of technology does not guarantee the delivery of value. It is only justified in a specific context. The differentiating element is in making a correct analysis process, having the ability to clearly understand the problem, being creative in the generation of variables, choosing the appropriate models and technology, and, above all, being able to communicate the results found effectively. These are some of the necessary skills that allow moving from data to objective knowledge that helps creating innovative solutions. The world is entering a new era of data-driven engineering, and it must be prepared.

 


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Desarrollado por: Cristian Díaz Fonseca - cfonseca@matiasluke.cl