Data-Driven and Business Model Innovation: Key Strategies to Lead the New Generation of People's Livelihood Service Industries
The public service industry encompasses areas closely related to people's daily lives, including clothing, food, housing, transportation, medical care, education, and retail. With the rapid development of data-driven technology, the global public service 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, and creating new market opportunities and economic value. According to a Gartner report, 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 Taiwan's public service industry to accelerate its digital transformation.
Global Public Service Industry's Data Application and Technology Trends
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 points out that personalized data applications can increase corporate revenue by 10% to 15%, becoming a key growth engine for the retail and other public service industries.
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 Go, Amazon's unmanned 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 technology company, provides personalized health advice to diabetes patients through its data platform and AI technology. The innovation in its 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 in Life Services: The rise of the smart city concept has promoted 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 Markets and Markets report, the smart city market will grow to US$820 billion by 2025.
Analysis of Taiwan's Public Service Industry Status
Current Status and Challenges of Data Application
Taiwan's retail industry has begun to apply data analysis technology to membership management and promotional activity planning, which has, to some extent, improved operational efficiency. However, most companies still have significant shortcomings in the depth and breadth of data application. For example, small and medium-sized enterprises (SMEs) primarily focus on basic data usage, such as member consumption records, and fail 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 silo" problems 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 personalized shopping experiences for consumers 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 major aspects: personalized marketing and supply chain optimization. In terms of personalized marketing, Tesco uses AI-driven data analysis models to analyze member shopping history and preferences, customizing and pushing exclusive discount coupons and promotional activities, successfully enhancing user loyalty and shopping frequency. In 2024, Tesco's personalized marketing strategy increased the average spending of Clubcard users by 18%. In terms of supply chain optimization, Tesco established a real-time data-based predictive model to accurately calculate commodity demand in various regions, reducing waste and stock shortages caused by oversupply or undersupply. For example, during the 2024 holiday sales period, Tesco effectively planned inventory allocation for food and beverages by analyzing data on local climate, holiday customs, and consumption patterns, 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 cleaning 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 and continued to leverage the commercial value of data while protecting user privacy.
To address these challenges, Tesco adopted the following strategies:
Data Lake Construction: By deploying a unified data lake platform, data from different sources is 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 regulations.
Cross-departmental Collaboration: Establishing data collaboration centers to promote data sharing and joint decision-making among departments, enhancing data utility.
In Taiwan, a convenience store utilized consumption data for personalized promotional activities, significantly increasing the average spending of members. However, due to the fragmentation of its supply chain and data platforms, it failed to achieve real-time inventory adjustments, leading to frequent shortages of popular promotional items.
Challenge Analysis: Data silos have become a major bottleneck restricting industrial development. The lack of standardized data sharing protocols among enterprises makes resource integration difficult. Secondly, enterprises have limited investment in data analysis and lack advanced 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 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 improving 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 an AI shopping guide system 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 functions, allowing customers to complete purchases directly in the app and then pick up items at designated areas in the store, without going through the traditional checkout process. This innovation not only improved shopping efficiency but also enhanced customer experience, leading to a 15% increase in sales in relevant IKEA stores in the first half of 2024. In Taiwan, a hypermarket 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, especially for small and medium-sized retailers with limited funds, which is a major obstacle. In addition, current AI and IoT systems still need further optimization in terms of stability, and consumers' usage habits and acceptance are also important factors in promoting the widespread adoption of smart retail.
Localized Application of Smart Healthcare
Significant progress has been made in the development of smart healthcare in Taiwan, particularly in early cancer diagnosis and chronic disease management. 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, issues of medical data standardization and privacy protection 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, especially 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 behaviors and real-time physiological data. In 2024, Livongo Health introduced an "active intervention model," which not only passively tracks patient data but also proactively issues health risk warnings to patients based on AI analysis results. For example, if a patient's blood glucose data shows abnormal fluctuations, the platform will immediately send automated suggestions and notify the medical team for further intervention. This function has reduced patient hospitalization rates by 35%, further reducing total medical expenditures, far exceeding the previous 30% savings rate. In addition, Livongo Health deepened its cooperation with major US health insurance companies and medical 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 technology company launched a health management service based on national health insurance data, aiming to help chronic disease patients with health risk prediction. However, due to unresolved issues of national health insurance data standardization, 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 improve practicality.
Taiwan's public service industry is at a critical stage of transformation and upgrading. Innovation in data applications, smart retail, and smart healthcare will be the core drivers of future development. However, only by addressing challenges such as data silos, technological costs, and privacy protection can the value of these technologies be maximized in the local market.
Future Development Trends of Taiwan's Public Service Industry
Cross-platform Data Integration and Sharing
Taiwan's public service industry can leverage a nationwide data sharing platform to achieve unified integration of retail, medical, and transportation data, providing technical support for diverse and personalized public services. This platform can not only deepen data application but also promote cross-industry collaboration and drive industrial upgrading.
Insights from International Experience: Estonia's X-Road data exchange platform in Europe is a representative case of cross-platform data integration and sharing in 2024, demonstrating how a unified data architecture can enable seamless data interoperability between different institutions and improve the efficiency of public services. Through secure data exchange protocols, it connects government departments, private organizations, and citizens. The platform supports the integration of various data sources, including population, health, transportation, and financial data, and ensures data privacy and security. In 2024, Estonia further achieved cross-border data sharing through X-Road, establishing cross-national data exchange cooperation with countries such as Finland and Latvia. For example, in the medical field, Finnish patients can access Estonian medical data through the X-Road platform, achieving seamless cross-national health services and shortening patient waiting times. The highlights of the model are as follows:
Data Standardization and Interoperability: X-Road uses unified data standards and protocols to ensure data compatibility and efficient transmission between different institutions.
Privacy Protection and Data Security: The platform adopts encryption technology and distributed architecture to ensure the security of data during exchange and comply with GDPR regulations.
Implementation of Cross-border Cooperation: In 2024, X-Road has become an important infrastructure for promoting data sharing and public service integration in multiple European countries.
According to a 2024 report, the implementation of X-Road has helped the Estonian government save over 100 million euros in administrative costs annually and improved public service efficiency by 80%. At the same time, the pilot program for cross-national medical data sharing has shortened patient treatment times and increased satisfaction by 25%. Taiwan can learn from its experience and establish a national-level data sharing infrastructure to unify and integrate transportation, medical, and retail data, further enhancing the precision and efficiency of public services and gaining an advantageous position in international data cooperation.
Recommendations for Taiwan's Development: The government can promote the establishment of a "Taiwan Public Service Data Sharing Alliance" to integrate data resources from retailers, hospitals, and transportation departments, and introduce blockchain technology to enhance data security and transparency. This cross-domain data integration is expected to support applications such as smart transportation planning, personalized medical care, and consumer behavior analysis.
Business Model Innovation and Technology Integration
Taiwanese enterprises need to explore innovative business models based on AIoT and 5G, leveraging technology integration to enhance service value. For example, smart home solutions can integrate home security, energy management, and health tracking, providing consumers with high-value on-demand subscription services.
Case Recommendations:
Insights from International Experience: In the Southeast Asian market, Grab's business model innovation and technology integration in 2024 are considered exemplary. Grab is not only a pioneer in the sharing economy but has also successfully expanded its service scope through data and technology, becoming a leader in smart living solutions in Southeast Asia. Grab's smart service ecosystem combines AI, IoT, and big data technologies to create integrated cross-domain services. For example, Grab's "Smart Subscription Platform," launched in 2024, integrates food delivery, transportation, and digital payment services, allowing users to choose monthly packages on demand and enjoy discounted prices. This not only enhanced user service stickiness but also helped the company significantly increase revenue streams. Grab also uses AI-driven recommendation engines to provide personalized service suggestions based on user behavior data, such as optimized traffic routes during peak hours and recommended food delivery options suitable for individual tastes. The highlights of the model are as follows:
Diversification of Subscription Models: Combining services with smart technology to create differentiated subscription plans, such as "Smart Home Package" integrating daily transportation and grocery delivery needs.
Technology Integration and Data Application: Adopting IoT devices for real-time delivery tracking and improving logistics efficiency through AI analysis.
Extension of the Ecosystem: Grab collaborates with local businesses and service providers to form cross-domain alliances, further expanding the influence of the ecosystem.
According to the latest report in 2024, Grab's subscription service users increased by 35%, with subscribed users spending an average of 50% more per month than non-subscribed users. In addition, the application of its smart recommendation system increased service satisfaction by 20% and helped its partner merchants increase sales by 15%. Grab's success story demonstrates the potential of business model innovation and technology integration. By building a data-driven ecosystem, Grab not only enhanced user experience but also set a benchmark for smart living innovation in Southeast Asia.
Recommendations for Taiwan's Development: Local telecom operators can combine 5G technology to launch smart home services featuring remote monitoring and energy consumption analysis, and provide flexible subscription models to attract more users. At the same time, they should actively cooperate with technology companies to develop new functions to meet diversified consumer demands.
Improvement of Data Privacy and Security Regulations
With the accelerating development of data applications, improving data privacy protection and security regulations will become fundamental work for upgrading Taiwan's public service industry. Ensuring the legality and transparency of data usage can not only enhance user trust but also bring long-term stable development momentum to the industry.
Insights from International Experience: In 2024, the EU further promoted the improvement of data privacy and security regulations by strengthening the supervision of AI technology through the "Artificial Intelligence Act (AIA)," establishing an advanced protection framework based on GDPR, setting a new benchmark for global data privacy and security management. It specifically formulated strict regulations for high-risk AI applications (such as medical diagnosis, autonomous driving, smart surveillance), including data transparency, risk assessment, and compliance certification. The implementation of AIA significantly enhanced the trust and competitiveness of European enterprises in the field of data applications:
95% of EU companies believe that transparent data usage regulations enhance customer trust.
In the first quarter of 2024, the compliance service market for AI-related industries in the EU grew by 22%, demonstrating the positive market benefits brought by regulations.
Several tech giants such as SAP and Siemens have successfully entered the high-risk AI application market through compliance certification.
Taiwan can refer to the successful experience of the EU AIA to improve local data privacy and security regulations, especially in sensitive areas of AI technology applications (such as medical care, smart cities) by formulating corresponding risk assessment and compliance processes. In addition, establishing third-party certification bodies to increase transparency, ensure data usage compliance and security, and enhance user trust in technology applications.
Recommendations for Taiwan's Development: The government should prioritize piloting data privacy and compliance frameworks in smart healthcare and smart retail, and establish a third-party data governance agency to ensure data security and transparency of use. At the same time, technologies such as "post-quantum cryptography" should be adopted to ensure the security of data transmission and storage.
Talent Cultivation and International Cooperation
To stand out in global competition, Taiwan needs to accelerate the cultivation of data science and AI talents and establish international cooperation platforms to attract cutting-edge technology and professional knowledge into the local market.
Insights from International Experience: The Estonian government, in cooperation with the EU, established a digital skills training center in Tallinn through the "Digital Europe Program," focusing on cultivating high-end talents in AI, data analysis, and digital security. This program has successfully trained over 1,200 AI and data professionals, 70% of whom entered local or international technology companies after completing the courses:
International Certification Courses: Collaborating with Google and Microsoft to offer data science and AI application courses, where students can obtain globally recognized professional certifications.
Corporate Internship Mechanism: Providing internship opportunities in cooperation with local technology companies and international companies such as Skype and Nortal, allowing trainees to practice their skills in real business scenarios.
Estonia further joined forces with Finland to launch the "AI Arctic Circle Alliance," focusing on smart healthcare and smart city applications. The alliance attracted companies such as Google DeepMind and Nokia to participate, jointly promoting data sharing and AI application technology standardization:
Smart Healthcare: Bilateral cooperation to develop AI models for health monitoring of chronic diseases, improving the efficiency of remote health management for patients.
Smart Cities: Deploying jointly developed smart traffic management systems in major cities in Estonia and Finland, reducing traffic congestion by 25%.
In 2024, Estonia attracted over 200 million euros in international investment in AI and smart application fields. According to the EU evaluation report, the country's brain drain of data and AI talents decreased by 15%, contributing to the long-term development of the local technology ecosystem.
Recommendations for Taiwan's Development: The government can cooperate with international enterprises such as Google AI Center to establish innovation centers in Hsinchu Science Park, attracting international researchers and local experts to jointly develop technologies and promote the application of AI in retail, healthcare, and transportation. At the same time, relevant academic courses and corporate internship opportunities should be added to further expand the AI talent reserve.
Through data integration, business model innovation, data security regulations, and international talent cooperation, Taiwan's public service industry can achieve the transformation from traditional service models to smart upgrades, providing consumers with more efficient and convenient service experiences, and enhancing Taiwan's competitiveness in the international market.
Conclusion
Taiwan's public service industry stands at the crossroads of digital transformation, where global market competition and technological innovation present unprecedented opportunities and challenges. In this rapidly changing era, how to integrate data resources, promote innovative business models, improve privacy protection regulations, and cultivate technical talents will be key directions for Taiwan's industrial upgrading.
Through cross-platform data integration, Taiwan can break down data silos, promote inter-industry collaboration, and improve service efficiency. By drawing on international experience, Taiwan can design innovative solutions that meet local market demands, promoting the widespread adoption of smart retail, smart healthcare, and smart homes. In addition, comprehensive data security regulations and international technical cooperation can not only enhance consumer trust but also attract more foreign investment and cutting-edge technology companies to Taiwan.
Looking ahead, Taiwan needs to leverage the global market, utilize emerging technologies such as AI, IoT, and big data, to improve the quality of public services while creating greater economic value and social impact. Only through close cooperation between the government and enterprises, actively promoting data-driven smart transformation, can Taiwan continue to leverage its unique competitive advantages on the international stage, create a smarter and more convenient living environment for its people, and become an important participant and leader in the global public service industry.