Different business models from overseas in integrating or processing freely disclosed data

There is a saying that big data is a "lottery draw." If you draw well, you hit the jackpot; otherwise, you miss the mark.

Looking at the U.S. data industry, there are many examples of "jackpot" successes. Yodel, a platform company, is one such example. This company scrapes homepages of data companies and provides them tailored to desired clients, and was subsequently sold to another company. Remarkably, for approximately 700 billion won. This demonstrates how lucrative the data platform business can be.

This is because the United States has the Data Marketing Association (DMA), which regulates and manages disputes. It implements a post-accountability system that establishes guidelines and manages data usage, including personal information, data collection, consumer notification, and responsible marketing. Although concerns about potential harm to individuals emerged in the United States, two years after the trial run of voluntary data regulation, conclusions indicated no major issues arose, and the system has been well maintained since then.


Building on such institutional foundations, the U.S. big data industry continues to thrive. Because data management and processing are well-executed, programming becomes easier. A prime example is Airbnb, which within eight years of entering the market surpassed the number of rooms of the global hotel chain Hilton. Airbnb did not build rooms but rather "connected" them. This was possible precisely because existing data was well connected. There are also companies that perform data processing and refinement, tasks difficult for individual companies to undertake. CrowdFlower generates revenue by building artificial intelligence necessary for business.

Of course, domestically there are platform companies such as Cozaza, an accommodation platform, and Ankus, an IoT data platform. Ankus, also called an open-source community, implements machine learning by aggregating sensor data held internally, other owned data, and publicly disclosed public data from the private sector. Such results are provided to companies seeking to develop secondary works. Small and medium-sized enterprises can reduce process steps through analyzed data and achieve cost savings by reducing total required costs. Additionally, they lower total production costs through data reuse.

The government also recognizes the importance of big data. Data analysis underpins efforts to prevent disease outbreaks and operate late-night bus services. By understanding data about where epidemics originate and how they spread, and calculating citizen travel times, late-night buses have been deployed accordingly. Additionally, public institutions are also disclosing information.

"While public institutions are disclosing information, they fail to satisfy users' needs, and private institutions holding data cannot disclose it due to various constraints"

However, South Korea's big data industry still has a long way to go. Rather than deliberating what data to "draw," often there is simply no data to "draw" from in the first place. There is a lack of usable data, and even when data exists, there are restrictions on how freely it can be used.

This is why Lee Jae-jin, director of the Distribution Business Division at the Korea Data Agency, stated at a policy discussion forum on promoting big data industry activation that "while public institutions are disclosing information, they fail to satisfy users' needs, and private institutions holding data cannot disclose it due to various constraints." This is because data must undergo processing before it becomes the necessary information.

This difference stems from a clear contrast with overseas practices. Overseas data businesses involve taking freely disclosed data and integrating or processing it into necessary formats for use, whereas domestically the typical approach involves taking information distributed by data holders and using it, often without connecting to processing stages.

According to research by a Singapore research institute, South Korea's data regulations are at higher levels than those of Singapore, the United States, the EU, and OECD averages. Regulations designed to prevent personal information protection issues and disputes between companies restrict not only data distribution but also storage, collection, and processing. Referencing discussion articles from the "Digital Transformation Forum" covered by this publication in January, reasons cited for the lack of activation of domestic platform companies include vertical corporate culture and reluctance toward personal information utilization, which lag behind the accelerating pace of digitalization.

AT Kearney partner Shim Tae-ho, who participated in the discussion forum, pointed out that "there is no IT-based platform and no original technology. Since we must adopt and utilize data but lack compelling business offerings, there is a risk of becoming dependent on global platforms." While one hesitates to repeatedly mention the need for regulatory improvements, substantial government investment, and specialized workforce, tracing the root causes of the problem inevitably leads to this same conclusion. One can only hope that the advice offered at this discussion forum held in the National Assembly does not become a hollow echo.