Jan 03, 2022 to Dec 29, 2022
Funding Agency:
Philippine Institute for Development Studies
Focus Area(s):
New Data Generation Methods and Sources

With the advent of digital transformation, ICT innovations have also led to a “data revolution” wherein more data is being captured, produced, stored, accessed, analyzed, archived, and re- analyzed, and at an exponential pace (Independent Expert Advisory Group on a Data Revolution for Sustainable Development 2014). New data sources, including big data and crowd-sourced data, can complement traditional sources of statistics (such as censuses, sample surveys and administrative data) to monitor and analyze the public sector’s development targets such as national development plans and the SDGs. New statistical methods and tools are also being developed side by side with the availability of the tsunami of data. New data requirements, especially more granular data, are also being demanded by data users. While the Philippines has its national development plans and committed to the SDGs by 2030, data gaps persist for monitoring development plans and the Global Goals even amid the emerging data revolution that has led new data sources, such as big data. Many governments have long recognized the need for data and statistics to inform policy and effect development outcomes. Faced with a growing demand to produce evidence-based policy research, PIDS can be part of the data ecosystem, and take advantage of new, innovative data sources to provide decision-makers with near real time information in formulating development policies and programs. Institutions in both the public and private sectors are accumulating data at an exponential rate, sometimes as a by-product of an administrative function, and in other cases, through data collection systems designed specifically for generating statistics to inform decision makers. Data is growing specially amid the growing use of technology, including the internet and digital platforms. Yet little data analytics are being performed on data holdings. This study aims to use the “learning” from the various PIDS data as proof of concept for examination of various new data sources, especially for addressing selected data gaps on development issues.

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