Introduction The coronavirus disease (COVID-19) pandemic has highlighted the importance of big data in sharpening crisis response. A study from the Asian Development Bank (ADB) shows big data and related technologies could also help expedite post-pandemic recovery. In Southeast Asia, it can create more than $100 billion worth of opportunities in education, health care, and social welfare and protection by making the delivery of public services more effective and efficient. Governments, however, need to lay the strategic and technical groundwork for big data to maximize the opportunities and mitigate its risks. ADB studied the impact of the health crisis on Cambodia, Indonesia, Myanmar, the Philippines, and Thailand and how big data can help their governments analyze challenges and design policies. The study recommends ways to optimize the benefits of big data, including developing digital roadmaps, improving technical infrastructure, and training a workforce that will lead digital transformation. Big Data Applications in Education Big data applications that can make remote learning and online job matching more effective could deliver a $77.1 billion boost to the region's gross domestic product (GDP) annually by 2030. These could help Southeast Asian countries deal with the surge in unemployment caused by COVID-19 and get affected populations, particularly the youth, informal workers, and migrant workers, back into the workforce. In Thailand, for example, data from job portals were leveraged to improve the responsiveness of the education system to skills gap during the COVID-19 pandemic. The Ministry of Education funded an initiative to examine the labor market situation and build a database of skills needed using data from 12 online job portals. This enabled policy makers to identify skills gap and develop programs to re-train the workforce, particularly in digital skills required to operate during the pandemic. Big data can help (i) identify skills gaps, (ii) increase graduation rates and prevent dropouts, and (iii) provide a personalized learning experience to cater to the unique needs of students. Identify skills mismatch. Digital skills, such as using information and communication technology (ICT) tools and digital platforms and analyzing digital information, are increasingly becoming essential across sectors, but many workers still lack these skills. Big data can help through better skills gap identification, career advice, tailored learning, and better job matching as well as by developing a responsive education system in the long term. For example, data from online job portals can be analyzed to understand employment and skills trends and develop suitable training courses based on industry demands. In Malaysia, proprietary assessment system Nurturing Expert Talent (NEXT) helps individuals identify their strengths, passion, and the career choices that are most suited to their skill sets. Increase graduation rates and prevent dropouts. Leveraging big data analytics, schools can analyze student records to identify early warning signs and provide remedial measures and targeted support to those in need. Georgia State University in the United States has effectively used big data to spot students who are at risk of dropping out and provide them with timely academic and financial support. Provide a personalized learning experience. Big data can help make remote learning more effective as it enables educators to customize teaching methods and curricula based on students’ learning styles, areas of interest, abilities, and progress. Schools can better understand the abilities and learning styles by collecting data on students' interaction with the virtual learning environment, the online sources they use for research, their participation in chats and forums, the areas that they struggle with, and the way they present information. In Arizona State University in the United States, the general-level mathematics course required students to sit at their computers and work through course content in their own time, with the help of tutors. Each student was placed at the appropriate starting point based on their abilities and then continually assessed as they progressed through the course. The class success rate increased to 85% from about 65%. Big Data Applications in Health Care Data-driven technologies in health care could bring an estimated $25 billion worth of annual benefits to Southeast Asian countries by 2030–$9.4 billion through application of remote monitoring systems and $15.5 billion through use of analytics to direct highly targeted health interventions for at-risk populations. Big data can (i) improve the monitoring of infectious disease outbreaks, (ii) enhance the prevention and detection of noncommunicable diseases, and (iii) help improve treatment capacity through remote patient monitoring and digital collection of patient information. Initiatives in health care include the eHealth Strategy (2017–2026) of Thailand’s Ministry of Public Health and Indonesia’s analysis of social media data to provide real-time insights on public perceptions on immunization. Improve the monitoring of infectious disease outbreaks. Big data can be used for preventative health surveillance by monitoring health conditions of populations to detect the risk of an outbreak and for pandemic response. Studies have found that influenza could be predicted through data from smartphone-connected thermometers that can track real-time influenza activity, as well as through search engines and social media, such as Google searches and Twitter messages, that produced precise estimates of influenza development. During the COVID-19 crisis, data from mobile phones, transport systems, and social media have also been used to support disease tracking and provide early warning for populations at risk. Singapore’s TraceTogether app collects proximity data based on exchanges of Bluetooth signals to identify people who have prolonged proximity with infected cases. Big data can further identify priority populations for vaccination programs. Enhance the prevention and detection of noncommunicable diseases. Big data can be employed to monitor risk factors associated with noncommunicable diseases. The United Nations Global Pulse and the World Health Organization tapped into big data to monitor risk factors associated with noncommunicable diseases (e.g., tobacco and alcohol use, diet, and physical activity) and found that indices for risk factors could be built and tracked over time on social media, such as Twitter. Improve treatment capacity through remote patient monitoring. Big data can be leveraged to improve treatment capacity through remote patient monitoring, such as devices that monitor heart conditions and blood-sugar levels. This can improve productivity, reduce patient in-hospital bed days, and cut emergency department visits. Vital signs trackers piloted by the Singapore General Hospital in 2019 reported significant productivity improvements compared to in-person checks, saving about 9 minutes when remotely monitoring a patient hourly for 6 hours and up to 22 minutes when monitored hourly across 12 hours. The McKinsey Global Institute estimates savings of 10%–20% for health care systems from applying remote patient monitoring systems. This could reduce costs by $9.4 billion annually by 2030 in Southeast Asia. Uses of Big Data in Social Welfare and Protection Most of the initiatives in the area of social welfare and protection are still at the exploratory or early phase. A pilot study in the Philippines and Thailand that explored the use of satellite imagery in mapping poverty levels has shown the potential of using innovative data sources to complement traditional poverty statistics. In Indonesia, a study in 2019 that looked into the use of mobile data to map migration patterns provided a high level of granularity that allowed the government to identify migrant source communities and destination cities. Cambodia has started developing the infrastructure required to enable big data applications in social welfare and protection through its digital identification system called IDPoor. The digital database has been used to identify more than 560,000 poor households for the Cash Transfer Program for Poor and Vulnerable Households during the COVID-19 pandemic. In the social welfare and protection sector, big data can be used to help (i) identify beneficiaries, (ii) improve program delivery and detect fraud, and (iii) assess program effectiveness. Identify beneficiaries. Alternative data sources (e.g., cell phone data, satellite imagery) can complement official statistics by providing more granular and updated insights into vulnerable populations. A World Bank project in Guatemala analyzed cell phone call records to assess users’ socioeconomic behavior, including consumption, mobility, and social patterns, to produce poverty estimates that were more cost-effective and updated than traditional survey data. A study by the UN Global Pulse and World Food Program on the Tabasco flood in Mexico also found that real-time information derived from mobile phone usage patterns can help authorities and humanitarian agencies accurately and quickly pinpoint vulnerable populations. Improve program delivery and fraud detection. Big data can improve transparency and reduce risks of errors and fraud in identifying beneficiaries by using algorithms to sift through data from various sources to detect inconsistencies. For example, rule-based algorithms can flag suspicious correlations, such as a person receiving unemployment benefits while filing for a work-related accident. Other potential data sources, including bank statements or digital wallets, can also support real-time adjustments in assistance for beneficiaries. Assess program effectiveness. Big data can be used to better evaluate the impact and success of social assistance programs. For example, household behavior shifts after receiving cash transfers and how money was spent can be analyzed through anonymized financial account data from banks or digital wallet providers. Similar approaches have been used in Australia where accounting software has helped analyze the impact of tax cuts on small business spending. Policy Reforms Unlocking the full potential of big data in public service delivery requires seven policy enablers. Strategic governance Governments should create a clear plan, road map, or national strategy to foster the digital transformation of public services and promote the use of big data applications in public service delivery. Availability and quality of data Adopting open data policies, improving data collection processes, creating an integrated data platform to facilitate data sharing between government agencies, as well as improving collaboration mechanisms for private sector engagement can broaden access to data and improve the quality of data to capture the full potential of big data applications. Risk mechanisms Mechanisms should be put in place to allow data sharing while minimizing the risk of unintended consequences, such as data privacy infringements, security violations, and unethical usage of data. Governments also need to balance data protection with the need to facilitate data sharing during crises to support crisis management. Human capital for big data Governments need to implement initiatives in education and training to increase the pipeline of graduates with the right skills who can join the civil service. In particular, it is crucial to expand the supply of talents with advanced digital skills, such as data science and machine learning. Access to relevant technologies Government agencies need access to relevant technologies to store, process, and analyze big data. Access to relevant technologies Countries need to instill a culture of making policy decisions based on rigorous evidence. A paradigm shift at the highest level of government may be required to promote a data-driven culture and increase awareness of applications and technologies used in managing and analyzing data. ICT infrastructure to support big data The application of big data requires a strong ICT infrastructure that is capable of collecting, storing, transferring, and processing large amounts of data at extremely faster rates as compared to traditional data systems. It is necessary to invest in ICT infrastructure, particularly improving cloud computing capabilities in government to provide a cost-effective and scalable way to store big data and enable efficient cloud-based big data analytics. Recommendations The study recommends policies for the short to long term to optimize the benefits of big data and mitigate its risks, including data breach, fraud, and cyberattack. Short-term – Begin implementation within the next 12 months. Establish strategic governance mechanisms, such as designating a digital transformation champion in government and establishing a national multistakeholder task force to drive big data adoption. Medium-term – Begin implementation over the next 1–2 years. Create integrated data platforms and forums to crowd-source data from the private sector, develop data protection frameworks and collaborate with the international community on common standards and approaches, and provide targeted training and incentives for civil servants to acquire relevant skills. Long-term – Implement over the next 3–5 years. Develop cloud-first policies, provide incentive schemes for data-driven decision-making in government, and establish mechanisms to crowd-source innovations and technologies. While such actions are expected to result in significant long-term impact, they require a strong political will and a whole-of-government approach to be effective.  Ministry of Public Health. 2017. eHealth Strategy (2017–2026). Nonthaburi: Information and Communication Technology Center.  The study used commercially available data from Kinsa Smart Thermometers, which record and store temperature measurements using the Kinsa smartphone application. When recording temperatures, users can assign readings to profiles by age and sex, allowing readings from multiple users within a household to be distinguished. Readings are geocoded using Global Positioning System location (for enabled devices) or by Internet Protocol address. Resources Asian Development Bank (ADB). 2022. Harnessing the Potential of Big Data in Post-Pandemic Southeast Asia. Manila. ADB. 2020. Mapping Poverty through Data Integration and Artificial Intelligence: A Special Supplement of the Key Indicators for Asia and the Pacific. Manila. Epitome. Improving Employment Outcomes for Graduates in Malaysia. H. Else. 2017. How Do Universities Use Big Data? Times Higher Education. 13 April. J. Ruiz-Palmero et al. 2020. Big Data in Education: Perception of Training Advisors on Its Use in the Educational System. Social Sciences. 9 (4). p. 53. L. Samaras et al. 2020. Comparing Social Media and Google to Detect and Predict Severe Epidemics.Scientific Reports. 10 (1). pp. 1–11. M. Hernandez et al. 2017. Estimating Poverty Using Cell Phone Data: Evidence from Guatemala. Policy Research Working Paper. No. 7969. Washington, DC: World Bank. Ministry of Public Health. 2017. eHealth Strategy (2017–2026). Nonthaburi: Information and Communication Technology Center. Pulse Lab Jakarta. 2019. Annual Report. R.C. Nielsen. 2015. Guest Post: Mobile Phone Data Analysis for Disaster Management. UN Global Pulse. 11 May. SingHealth. 2020. Why Remote Monitoring Will Shape Tomorrow’s Medicine. 16 September. TraceTogether application. United Nations Global Pulse. 2014. Understanding Public Perceptions of Immunisation Using Social Media. W. Theanvanichpant. 2020. Big Data to Keep Thai Workers Afloat. Thailand Development Research Institute. 26 August. Xero and AlphaBeta. 2018. Do Company Tax Cuts Boost Jobs, Wages and Investment? Sydney. Ask the Experts Dulce Zara Senior Regional Cooperation Officer, Southeast Asia Department, Asian Development Bank Dulce Zara is part of the Southeast Asia Department of ADB. She oversees the preparation of country partnership strategies and country operational reports. She is a member of the economics unit that monitors developments, prepares reports, and organizes knowledge-sharing events on various issues that impact Southeast Asia. Prior to joining ADB, she has gained experience as a technical adviser in a Philippine government bank and as a manager in the treasury department of a private bank. James Villafuerte Regional Lead Economist, Asian Development Bank James Villafuerte heads the ASEAN Policy Network and conducts research on a range of economic and policy issues, including economic surveillance, international trade and global value chain, technology and digitalization, and infrastructure financing. He was also a Team Leader of ADB's Asia Regional Integration Center. Prior to joining ADB, he was a senior economist at the Department of Treasury and Finance in Victoria, Australia; and an economist at the World Bank Office in Manila. He is assigned to the Southeast Asia Department. Georginia Nepomuceno RCI Expert, PACER TA Consultant, Southeast Asia Department, Asian Development Bank Georginia Nepomuceno is a regional cooperation and development specialist with wide experience in managing and implementing regional cooperation initiatives in Southeast Asia. She has worked extensively on the Greater Mekong Subregion Program, including formulating strategies and action plans and providing technical program development and knowledge management support. She led ADB initiatives for the Indonesia-Malaysia-Thailand Growth Triangle. At the ASEAN Secretariat, she implemented the Initiative for ASEAN Integration work program. Asian Development Bank (ADB) The Asian Development Bank is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members—49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. Follow Asian Development Bank (ADB) on Leave your question or comment in the section below: View the discussion thread.