In this blog post, we examine the vast potential of big data alongside the challenges South Korea must address to effectively utilize it, including personal data protection, data ethics, software competitiveness, and the development of specialized talent.
In director Steven Spielberg’s film ‘Minority Report’, the Precrime system prevents crimes in advance based on prophecies made by precogs. The precogs predict the location, time, and even the perpetrator of a crime in advance, and the police arrest suspects before the crime occurs based on those prophecies. While the Precrime system in the movie predicts the future through supernatural abilities, in reality, attempts to predict the future are centered not on prophecy but on vast amounts of data and the technology to analyze it. Whereas big data was previously recognized primarily as a technology for probabilistically predicting specific phenomena by analyzing vast amounts of data, today it is utilized as a key resource—combined with artificial intelligence—to enhance the accuracy of decision-making, uncover new insights, and analyze complex, multivariate phenomena.
Traditionally, big data referred to data sets so large and complex that they were difficult to process using conventional database management and analysis tools alone. Initially, the “3Vs” concept—focused on volume, variety, and velocity—was widely used; later, “veracity,” referring to the reliability or accuracy of data, was added to form the “4Vs.” However, today, big data does not simply refer to the size of the data. It is more appropriate to understand it as a concept that encompasses a series of technologies and utilization systems designed to rapidly collect, store, and process not only structured data but also various forms of data—such as text, images, audio, video, sensor data, location data, and online behavioral data—and to analyze this data to generate valuable insights.
In South Korea, too, big data is no longer a technology at the stage of debating whether to adopt it. Since the previous administration began expanding the use of public and private data by establishing initiatives such as the “Big Data Master Plan for Realizing a Smart Nation,” the data industry has continued to grow in conjunction with artificial intelligence, cloud computing, and digital transformation. Currently, the Korea Data Industry Promotion Agency operates various initiatives—including data industry support, data vouchers, data safe zones, and MyData—while the government is also implementing policies to foster a secure environment for data utilization. Therefore, the question South Korea must now address is not simply “Should we adopt big data?” but rather “How can we utilize data safely, responsibly, and competitively?”
Of course, it is difficult to deny that big data and data analytics technologies are key drivers of future industries and society. As social and economic systems become increasingly complex and the types and volumes of data generated by humans surge rapidly, the ability to effectively analyze vast amounts of data is becoming crucial not only for businesses and the government but also across various fields such as healthcare, transportation, finance, manufacturing, logistics, and scientific research. However, the use of data carries as many risks and challenges as it does boundless possibilities. In particular, considering the characteristics that have emerged during South Korea’s development process, it is just as important to determine under what principles and systems new technologies are utilized as it is to adopt them quickly.
The concepts of “compressed modernization” or “compressed modernity” are often used to describe South Korea’s rapid economic growth and industrialization. Compressed modernization refers to the phenomenon in which industrialization, urbanization, economic growth, and changes in social structure—processes that typically unfold over a long period in other societies—occur intensively within a relatively short timeframe. South Korea achieved industrialization, urbanization, and informatization in a short period, attaining economic growth that garnered global attention. However, various problems—including industrial accidents, environmental pollution, and social inequality—also emerged during this rapid growth process. Although legal and institutional mechanisms to address these issues have been continuously strengthened, there remains a possibility that the societal tendency to prioritize quick results and efficiency will influence the process of adopting new technologies.
These characteristics are linked to the social and ethical issues that may arise during the utilization of big data and artificial intelligence. One of the key features of big data is the ability to uncover new insights by combining and analyzing everyday information that was previously considered insignificant. Individual data points—such as personal preferences, places visited, purchase histories, search histories, travel routes, health-related information, and online behavior—may seem insignificant when viewed in isolation. However, when this information is accumulated over a long period and combined with other data, it becomes possible to infer an individual’s lifestyle habits, interests, consumption tendencies, health status, and behavioral patterns to a significant degree.
It is precisely at this point that the potential and risks of big data emerge simultaneously. While securing more data can enable more sophisticated analysis, this does not mean that all data can be collected and utilized without restriction. We must also be wary of “data absolutism”—the tendency to view people merely as “sources of data” or to judge human behavior and characteristics based solely on data. We must also consider issues such as data preserving past records for extended periods, thereby limiting individuals’ opportunities to move beyond their past actions or records; the inference of sensitive information that data subjects did not anticipate through data combination; and algorithms repeatedly producing outcomes that disadvantage specific groups.
In the past, it was argued that a legal framework was needed to more clearly define consent for data collection and retention periods in order to address these issues. Today, South Korea’s personal information protection system has advanced significantly since then. According to the Personal Information Protection Commission, the “Personal Information Protection Act” includes a system for pseudonymized information, allowing personal information to be pseudonymized and utilized under certain conditions for purposes such as compiling statistics, conducting scientific research, and preserving records in the public interest. Even during the pseudonymization process, the principles of minimal use and collection of personal information must be upheld, and technical, administrative, and physical security measures must be applied while assessing risks such as re-identification. Recently, guidelines and support systems for processing pseudonymized information have been continuously refined to reflect the rapid advancements in artificial intelligence and the rapidly changing data utilization environment.
Therefore, the current key challenge goes beyond the simple requirement of “obtaining an individual’s consent.” Throughout the entire data lifecycle, it is necessary to examine what information is being collected and for what purpose; whether only the minimum information necessary to achieve the purpose is being used; how long it is retained; what risks arise when combining or sharing data with third parties; and how the possibility of re-identification is managed. Rather than viewing data utilization and personal information protection as mutually exclusive values, we must establish a system that realizes the social and economic value of data while prioritizing personal information protection.
The South Korean government is also expanding the foundation for data utilization in this direction. It has designated and operates “Data Safe Zones” to enable the secure analysis of high-value data in environments equipped with technical, physical, and administrative security measures, and has established a support system to reduce the legal and administrative burdens associated with the use of pseudonymized information. This approach moves away from the traditional view—which framed the expansion of data utilization and personal information protection as a simple conflict—toward one that seeks to enhance the potential for data utilization while ensuring appropriate safeguards are in place.
Meanwhile, when discussing the use of big data in South Korea in the past, the asymmetrical development structure of the IT industry was often cited as a key issue. At the time, many assessments noted that while South Korean companies possessed strong competitiveness in hardware sectors such as smartphones, semiconductors, and displays, the international competitiveness of the software industry was relatively weak. In fact, while South Korean manufacturers once held a high market share in the global smartphone market, Google’s Android and Apple’s iOS effectively dominated the smartphone operating system market. Samsung Electronics’ proprietary “Bada” operating system also failed to gain significant market influence and subsequently faded into obscurity.
However, it is inappropriate to use this example as evidence that South Korea’s current software industry remains in the same state as in the past. Today’s technological environment, which leverages big data, has changed significantly from the era when software competitiveness was assessed solely based on market share in smartphone operating systems. The scope of the data industry itself has expanded significantly as various fields—including cloud computing, artificial intelligence, databases, data analytics platforms, SaaS (Software as a Service), data centers, and AI semiconductors—have converged. In South Korea as well, policies supporting the data and AI industries and cloud-based digital transformation initiatives are ongoing, and projects are being promoted to support domestic software companies’ entry into global cloud marketplaces and their expansion into overseas markets.
Therefore, current issues should not be simplified by claiming that “South Korea is strong in hardware but weak in software, making it difficult to properly utilize big data.” Rather, competitiveness in data utilization is determined not only by high-performance computing infrastructure and the cloud but also by data quality, analytics software, AI models, security technologies, privacy protection technologies, industry-specific expertise, and the ability to integrate and operate these elements cohesively. In particular, the integration of big data and AI has become so close recently that it is difficult to distinguish them as separate domains. The very ability to collect and refine large-scale data for use in AI training and inference has become a critical component of industrial competitiveness.
The cloud has also established itself as a core foundation for big data utilization. Whereas in the past it was common for companies to build and operate their own servers and storage devices, today they can access computing resources, storage space, databases, analytics tools, and AI services from the cloud as needed. In South Korea, policies are being implemented to support the adoption of cloud services by small and medium-sized enterprises (SMEs) and to promote cloud-based digital transformation. Therefore, it is not necessary for South Korean companies to independently develop all the operating systems and software required for big data utilization. What matters is securing the necessary technologies reliably, maintaining control over core data and services, and fostering an ecosystem where domestic companies can build their own competitive edge in data and software.
From this perspective, continued investment in South Korea’s data industry remains crucial. However, the goals of such investment must also change from what they were in the past. Rather than merely creating big data processing programs, we must enhance competitiveness across the entire data value chain—including data collection and refinement, databases, cloud computing, artificial intelligence, personal information protection technologies, cybersecurity, and data analysis services. In particular, it is crucial to create an environment where domestically developed SaaS and data/AI solutions can expand into overseas markets rather than remaining confined to the South Korean market.
Another key challenge is the continuous training of specialized personnel capable of effectively utilizing big data and artificial intelligence. In the past, a lack of technical and analytical know-how, as well as a shortage of specialized personnel, were identified as major factors hindering the effective use of big data. This problem persists today, albeit in different forms. Simply having a large volume of data does not automatically yield valuable results. We need people who can define the problems to be solved, select the necessary data, review data quality and bias, apply appropriate analytical methods, and interpret the results. Furthermore, with the rapid advancement of artificial intelligence, the importance of interdisciplinary talent—who can understand data engineering, machine learning, statistics, privacy protection, cybersecurity, and industry-specific expertise—is growing even more.
Above all, big data analysis requires a proactive attitude—the ability to identify problems and formulate questions independently. The process of identifying patterns within vast and diverse datasets and connecting them to real-world problem-solving is by no means simple. One of the examples frequently cited in the past to illustrate the potential of big data was a study by researchers at the Graduate School of Industrial Science and Technology in Japan, who attempted to identify drivers by analyzing the body contours and pressure distribution of people seated in car seats. This is an interesting case because even seat pressure data—which may seem insignificant at first glance—can be linked to driver identification or vehicle safety technologies when approached with a specific problem-solving mindset and analytical methods.
The key takeaway from this example is not the sheer volume of data itself, but the perspective from which the data is viewed. What matters is the ability to discover meaning in seemingly meaningless data and determine how it can be used to solve specific problems. No matter how vast the dataset, it is difficult to obtain valuable results if one does not know what questions to ask. Conversely, if one can ask the right questions while understanding the characteristics and limitations of the data, new value can be discovered even in seemingly ordinary data. Therefore, we must actively cultivate not only talent accustomed to solving predefined problems but also talent capable of defining problems independently and posing questions to the data.
In everyday life, people produce enormous amounts of data every day. Data is constantly generated not only through smartphone use, online searches, e-commerce, financial transactions, transportation, location-based services, and social media activity but also from various sensors and Internet of Things (IoT) devices. Utilizing big data involves the process of identifying necessary information within this vast amount of data, connecting and analyzing disparate data sets, and deriving meaningful results. Unstructured data—such as images, audio, video, and natural language—which was difficult to store or analyze in the past, can now be utilized much more extensively thanks to advancements in cloud computing and artificial intelligence.
However, the fact that data can be analyzed technically does not necessarily mean it should be analyzed. Issues such as privacy violations, data breaches, hacking, misuse and abuse of data, discrimination and bias caused by algorithms, and excessive surveillance and profiling become increasingly critical as the use of data expands. Particularly in an era where artificial intelligence analyzes and predicts human behavior and preferences based on big data, we must examine not only the data collection stage but also how the results of analysis and inference affect individuals and society.
Therefore, what South Korea needs to effectively utilize big data is neither the unconditional expansion of its adoption nor, conversely, excessive restrictions on data use based solely on perceived risks. We must continuously refine legal and ethical standards to protect personal information and fundamental rights, develop technologies and systems for the secure use of data, and simultaneously strengthen our competitiveness in the fields of software, cloud computing, artificial intelligence, and data analysis. In addition, we must consistently cultivate specialized professionals who understand the meaning and limitations of data and can identify new problems—rather than merely technicians who process data.
The potential of big data has grown far greater than in the past. With the advancement of new technologies, including generative artificial intelligence, data has established itself as a key resource that determines not only industrial competitiveness but also a nation’s scientific and technological capabilities and the quality of public services. Therefore, what matters is not how much data is collected, but how responsibly it is gathered, how securely it is managed, how accurately it is analyzed, and how effectively it is utilized. For South Korea to fully harness the potential of big data and artificial intelligence, it must not only rapidly adopt new technologies but also continuously strengthen its internal foundations—including personal data protection, data ethics, the software ecosystem, security, and a skilled workforce. The original text’s core argument—that establishing the social and industrial foundations necessary to properly utilize these technologies takes precedence over the speed of their adoption—remains highly relevant today.
