Mastering data applications: People's livelihood service industries continue to expand
The public service industry, which is closely related to our daily lives and covers fields such as clothing, food, housing, transportation, medical care, education, and retail, is undergoing a profound digital transformation globally with the rapid development of data-driven technologies.
The widespread application of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and cloud computing is redefining service models and business logic, and creating new market opportunities and economic value. According to a report by the international research firm Gartner, the global smart public service market will reach US$420 billion in 2024, with a compound annual growth rate (CAGR) exceeding 12%. This trend not only brings new opportunities for international enterprises but also provides impetus for the accelerated digital transformation of Taiwan's public service industry.
(Section Title) Data Application is King, Driving Production Value Up
A closer look at the data application and technology trends in the global public service industry reveals the following 4 key points:
1. Data-driven Personalization and Precision of Services
2. Smart Retail and Seamless Payments
Smart retail is one of the core trends in global public services, embodying the deep integration of AI, IoT, and mobile payments. Amazon's cashierless store, Amazon Go, and China's JD.com cashierless supermarket are leading this trend, offering seamless payment experiences and real-time inventory management. According to data from the analytics company Statista, the global smart retail market reached US$120 billion in 2023 and is expected to grow to US$300 billion by 2028.
3. Smart Medical and Health Services
Medical and health care is one of the important areas for technological application in public services. The application of AI in disease prediction, remote medical care, and health management is also becoming increasingly popular. For example, Livongo Health, a US health technology company, provides personalized health advice to diabetes patients through its data platform and AI technology, and its innovative business model significantly reduces users' medical costs. A report by market research firm Grand View Research indicates that the global smart healthcare market will reach US$780 billion in 2024.
4. Smart Cities and Data Application in Life Services
The rise of the smart city concept promotes data integration and application in the public service sector. For example, Singapore's Smart Nation initiative uses IoT sensors and AI technology to provide citizens with more efficient transportation, housing, and energy management. According to a report by market research firm Markets and Markets, the smart city market will grow to US$820 billion by 2025.
(Section Title) Data Silos Emerge, Limiting Cross-Enterprise Collaboration
Looking back at the current development and challenges of data application in Taiwan's public service industry, retail businesses use data analysis technology for member management and promotional activity planning, which to some extent improves operational efficiency. However, most enterprises still have obvious shortcomings in the depth and breadth of data application. For example, small and medium-sized enterprises mostly focus on basic levels of data usage, such as member consumption records, and fail to further apply it to machine learning or AI for in-depth analysis. In addition, the data sharing mechanism among enterprises is not yet sound, creating the so-called "data silo" problem, which limits the possibility of cross-enterprise collaboration.
Furthermore, a convenience store in Taiwan uses consumer data for personalized promotional activities, significantly increasing the average spending of members. However, due to the fragmented nature of its supply chain and data platform, it fails to achieve real-time inventory allocation, leading to frequent shortages of popular promotional items.
The problem of data silos has become a major bottleneck restricting industrial development. Enterprises lack standardized data sharing protocols, making it difficult to integrate resources. Enterprises also have limited investment in data analysis and lack high-level data application capabilities, especially in applying AI technology for precise analysis and prediction. This not only affects the commercial value of data but also hinders the progress of the industry's digital transformation.