Data-Driven and Business Model Innovation: Key Strategies to Lead a New Generation of Public Service Industries

The public services industry encompasses areas closely related to people's daily lives, including clothing, food, housing, transportation, healthcare, education, and retail. With the rapid development of data-driven technologies, the global public services industry is undergoing a profound digital transformation. The widespread application of technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and cloud computing is redefining service models and business logic, creating new market opportunities and economic value. According to a Gartner report, the global smart public services market is projected to 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 services industry.

Global Trends in Data Application and Technology in the Public Services Industry

Personalization and Precision of Data-Driven Services: Leading global companies such as Amazon and Alibaba are leveraging AI and big data technologies to achieve personalized services. For example, Amazon uses its AI algorithms to provide personalized shopping recommendations based on consumers' historical behavior and preferences, significantly enhancing user experience and conversion rates. McKinsey notes that personalized data applications can increase corporate revenue by 10% to 15%, becoming a key growth engine for the retail and other public services industries.

Smart Retail and Seamless Payments: Smart retail is one of the core trends in global public services, reflecting the deep integration of artificial intelligence, IoT, and mobile payments. Amazon Go, Amazon's cashierless store, and JD.com's unmanned supermarkets in China are leading this trend, offering seamless payment experiences and real-time inventory management. According to Statista data, the global smart retail market reached US$120 billion in 2023 and is expected to grow to US$300 billion by 2028.

Smart Healthcare and Health Services: Healthcare is one of the important areas of technology application in public services. The application of AI in disease prediction, telemedicine, and health management is becoming increasingly common. For example, Livongo Health, a US health tech company, provides personalized health advice to diabetes patients through its data platform and AI technology. Its innovative business model has significantly reduced users' medical costs. A Grand View Research report indicates that the global smart healthcare market will reach US$780 billion in 2024.

Smart Cities and Data Applications for Lifestyle Services: The rise of the smart city concept has promoted data integration and application in the public services 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 Markets and Markets report, the smart city market will grow to US$820 billion by 2025.

Analysis of Taiwan's Public Services Industry Landscape

Current Status and Challenges of Data Application

Taiwan's retail industry has begun to apply data analytics technologies to membership management and promotion planning, which has, to some extent, improved operational efficiency. However, most enterprises still show significant shortcomings in the depth and breadth of data application. For instance, SMEs largely focus on basic data usage, such as member consumption records, failing to further utilize machine learning or AI for in-depth analysis. Furthermore, inter-enterprise data sharing mechanisms are not yet well-established, leading to "data silos" that limit the possibility of cross-enterprise collaboration.

Case Study: In 2024, UK retail giant Tesco's Clubcard data analysis system, combining sales data and member behavior data, provided consumers with personalized shopping experiences and improved supply chain efficiency. However, as data application deepened, Tesco also faced numerous challenges in data integration and privacy protection. Tesco's data application covers two main aspects: personalized marketing and supply chain optimization. For personalized marketing, Tesco used AI-driven data analysis models to analyze members' shopping history and preferences, customizing and pushing exclusive discount coupons and promotional activities, successfully enhancing user loyalty and shopping frequency. In 2024, Tesco increased the average spending of Clubcard users by 18% through personalized marketing strategies. For supply chain optimization, Tesco established real-time data-based predictive models to accurately calculate product demand in various regions, reducing waste and stockouts caused by oversupply or undersupply. For example, during the 2024 holiday sales period, by analyzing data on local climate, holiday customs, and consumption patterns, Tesco effectively planned inventory distribution for food and beverages, increasing inventory turnover efficiency by 12%. However, the success of data application is not without challenges. First, Tesco faced the problem of data silos. As data came from different departments and supplier systems, the cost of integration and cleansing was high, affecting analysis efficiency. Second, data privacy became the core challenge. With the implementation of the General Data Protection Regulation (GDPR), Tesco had to ensure that data usage complied with regulatory requirements while continuing to extract business value from data while safeguarding user privacy.

To address these challenges, Tesco adopted the following strategies:

Data Lake Construction: By deploying a unified data lake platform, data from various sources was centrally stored and structured, improving data integration efficiency.

Application of Privacy-Enhancing Technologies (PETs): Employing post-quantum cryptography and federated learning technologies to protect user privacy during data analysis and ensure compliance with regulatory requirements.

Cross-departmental Collaboration: Establishing a data collaboration center to promote data sharing and joint decision-making among departments, enhancing the practical utility of data.

In Taiwan, a convenience store utilized consumption data for personalized promotional activities, significantly increasing the average spending of its members. However, due to the fragmented nature of its supply chain and data platform, it was unable to achieve real-time inventory adjustments, leading to frequent shortages of popular promotional items.

Challenge Analysis: The data silo problem has become a major bottleneck restricting industry development. The lack of standardized data sharing protocols among enterprises makes resource integration difficult. Furthermore, enterprises' limited investment in data analysis and lack of advanced data application capabilities, especially in applying AI technology for precise analysis and prediction, not only affects the business value of data but also hinders the industry's digital transformation process.

Smart Retail and Payment Innovation

Smart retail has achieved initial results in Taiwan's retail industry, particularly in the promotion of unmanned stores and electronic payment systems. Some convenience stores have introduced AI checkout systems, significantly reducing consumer waiting times and enhancing user experience. However, due to the high cost of smart retail equipment, small and medium-sized retailers face barriers to technology adoption, and some consumers' acceptance of unmanned services still needs time to improve.

Case Study: IKEA in Europe launched a new smart shopping experience in 2024. IKEA introduced AI shopping assistant systems in some of its stores. Through a mobile app, customers can scan products, obtain detailed information, and receive personalized product recommendations. In addition, IKEA integrated mobile payment functionality, allowing customers to complete purchases directly in the app and then pick up items at designated areas in the store, bypassing the traditional checkout process. This innovation not only improved shopping efficiency but also enhanced the customer experience, leading to a 15% increase in sales in relevant IKEA stores in the first half of 2024. In Taiwan, a supermarket successfully reduced labor costs by approximately 20% by introducing an unmanned checkout system. However, due to the high installation cost of IoT equipment, this model has not yet been fully implemented nationwide and can only be piloted in high-traffic urban core areas.

Challenge Analysis: The promotion of smart retail is constrained by high equipment costs, which is a major barrier for small and medium-sized retailers with limited capital. Furthermore, current AI and IoT systems still need further optimization in terms of stability, and consumer usage habits and acceptance are also important factors in promoting the widespread adoption of smart retail.

Localized Application of Smart Healthcare

Smart healthcare in Taiwan has made significant progress, especially in early cancer diagnosis and chronic disease management, achieving considerable results. For example, a study by Taipei Veterans General Hospital showed that its AI model can improve the accuracy of early colorectal cancer diagnosis to 90%, greatly enhancing diagnostic and treatment efficiency. However, the standardization of medical data and privacy protection issues remain two major challenges facing the industry.

Case Study: Livongo Health is a classic and continuously evolving international case in the field of smart healthcare. Since 2024, the platform has continued to upgrade its AI algorithms, specifically providing more precise and real-time health management services for patients with diabetes, chronic diseases, and multiple conditions. These advancements are mainly due to the integration of larger datasets and high-performance AI models, enabling the platform to extract deeper health insights from patients' daily behavior and real-time physiological data. In 2024, Livongo Health introduced a "proactive intervention model." This model not only passively tracks patient data but also, based on AI analysis results, issues early health risk warnings to patients. For example, if a patient's blood sugar data shows abnormal fluctuations, the platform will immediately send automated recommendations and notify the medical team for further intervention. This function reduced patient hospitalization rates by 35%, further lowering overall medical expenses, significantly exceeding the previous 30% savings rate. Furthermore, Livongo Health also deepened its cooperation with major US health insurance companies and healthcare systems in 2024, such as Kaiser Permanente and Anthem Health, achieving closed-loop data sharing among patients, insurance companies, and healthcare providers. Through this collaboration, the platform successfully expanded to more chronic disease areas, including cardiovascular disease and kidney disease management, and achieved a high satisfaction rate of 92% in user satisfaction surveys. In Taiwan, a health tech company launched a health management service based on National Health Insurance data, aiming to assist chronic disease patients in predicting health risks. However, due to unresolved issues with the standardization of NHI data, the service still faces challenges in data integration and application efficiency.

Challenge Analysis: Data application in the smart healthcare sector needs to strike a balance between individual privacy protection and data sharing efficiency. In addition, medical AI systems need to be seamlessly integrated with diverse hospital information systems (HIS) to enhance practicality.

Taiwan's public services industry is at a critical stage of transformation and upgrading. Innovation in data application, smart retail, and smart healthcare will be the core drivers of future development. However, only by addressing challenges such as data silos, technology costs, and privacy protection can the value of these technologies be maximized in the local market.

Future Development Trends of Taiwan's Public Services Industry

Cross-Platform Data Integration and Sharing