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A New Profession Is Keeping Up With the Fourth Industrial Revolution Trend : Big Data Professional
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  • 등록 2021-03-06 00:15:05
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 In order to understand the current fourth industrial revolution, we first need to understand the past, how everything has proceeded to connect and evolve into a more intelligent society. The first industrial revolution in the 18th century was a steam engine-based mechanization revolution. Based on the steam engine, machines started being used for manufacturing and transportation. The second industrial revolution, which took place in the 19th and early 20th centuries, was an electrical energy-based mass production revolution. The conveyor belt is one of the examples of technology at this time. The Third Industrial Revolution was a computer and Internet-based intellectual information revolution in the 20th century. At this time, people utilized computers and Internet. Lastly, the 21st century is the fourth industrial revolution. Many TV shows and news sources have said that the fourth industrial revolution is taking place, so we should be prepared. So what is the Fourth Industrial Revolution? The current industrial structure is a hyper-connected revolution based on information technology, with IoT, big data, 5G, and AI. It means a future society in which everything is connected and intelligent. As a key point, AI technologies are used in production processes, distribution, and consumption of goods and services. IoT, robots, AI, 3D printing, and AR/VR are new technologies that led to the Fourth Industrial Revolution. It is predicted that data will become money and a core source of competitiveness in the future. So why don’t we make money by utilizing the massive amount of available data?

 

 Preferentially, we need to know the concept of big data. Big data can be summarized with three V’s. They are Volume, Velocity and Variety. The point here is speed. According to the development of technology, data processing speeds are getting faster. Therefore, this has allowed us to process larger amounts of data faster than ever before and analyze data that could not be analyzed before. In other words, unlike previous traditional data, massive amounts of data can be processed and manufactured at a rapid pace. The biggest difference from the past is the methods of data acquisition. The characteristic of traditional data is itemization from the beginning, for example, surveys and sales information data. It also included a planned subject and the data could be entered by hand. However, this is an era in which all kinds of data can be collected with the development of communication technology, good computing power, and valuable sensors. It is far beyond the range of hand written quantities. Therefore, big data is unstructured data, which is collected, analyzed, and used to derive patterns. The point is to pattern it.

 

 As the use of big data has increased, the career fields related to big data have expanded. The first is engineering. One job of engineers that is close to computer science involves how to stack and process data. It is largely divided into data engineers developing back-end platforms, engineers making machines for deep learning, and engineers focusing on data engineering. Data engineers collect and process data from the web or apps and store it for data analysts to use easily. They manage the flow of data in generating data pipelines. They also develop dashboards for data analysts to use. ML/DL engineers develop the required model in their domain and apply it to production. The main task is to monitor the performance of the model or to improve the performance and to develop a better model. The second is data analysts and data scientists. In other words, it is one of the occupations that collects data stacked in the database and then identifies patterns within the data based on statistical knowledge, finally finding meaningful data through the pattern. When an engineer saves the database, the data analyst performs data analysis based on the data. They create and manage dashboards for data that needs to be monitored or for specific data. They establish key performance indicators, monitors KPI, and also analyze the causal relationship from numerical changes. Data scientists usually conduct thesis research in R&D organizations. They also model statistics or do what machine learning engineers do. Third are planners and growth hacker. That is, the profession that links meaningful data found through patterns in order to meet business objectives to inform meaningful data essential to business decision making. Service/product planners use third-party tracking tools such as Google Analytics, Tune, and dashboards in the company. To make better products and services, they plan to analyze data and improve function using Python or R for sending SQL queries directly from the database or for simple data manipulation. They are very closely related to data analyst jobs. Growth hacker is an occupational group that combines marketing and engineering analyzing data growth. Like planners, they are responsible for viewing data by sending queries or using dashboard analytics and focusing on business growth rather than product development.

 

 There are good cases that make better use of big data, such as Amazon. Amazon analyzed consumers’ consumption patterns and predicted who would buy which products. Therefore, they have developed a system that allows consumers to click the purchase button before they press the delivery request to prepare for delivery. This played a major role in reducing time and budget. Another example is ZARA. Their marketing strategy is growing into a variety of small-scale production. They produced more than twice as many kinds of products as the typical fashion brand. ZARA developed it so that orders, production, and entry can take place within six weeks. The reason why this was possible was because they developed an inventory management system that can identify real time demand forecasting, inventory calculation by store, price determination by product, and transportation. Lastly, Google lets big data analyze the search results. For example, people generally recognize that when they catch a cold, they search for words such as flu or cold symptoms before going to a hospital or pharmacy. Consequently, since 2008, they have developed a service that informs the spread of the cold virus in the United States based on search information and location. These services contribute a lot to improving people’s quality of life.

 

 The world we belong to is only a small part of it, and it is possible that it is fake when we look at the whole thing with the facts we know. The fact is the world is much bigger and much less aware than we think. Therefore, we constantly measure and test the response of the world based on data. This is why data analysis is important. When you are contemplating or wondering about your career path and studying the field, it would certainly be helpful to consider a career in big data. Students have to know that there are various occupational groups. This article is to provide help to students who are thinking about their career path toward big data and to tell them not to worry about it. It is not true that students who belong to the liberal arts cannot go into the IT profession. The IT field is open to all students.

 


75th Cub ReporterCHOI HYUN JEONGchj010627@naver.com

75th Cub ReporterHAN SONG HEEshhan0509@hanmail.net


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