In recent years, environmental, social, and governance (ESG) has gained prominence among an increasingly socially and environmentally conscious consumer base. Viewed from a public policy perspective, this shift towards a more socially and environmentally conscious private sector creates opportunities for alignment between business and government. For instance, ESG metrics and political targets such as the Sustainable Development Goals (SDGs) center on people, the planet, and prosperity as the three pillars of sustainable development.
One area of alignment is in the field of data and digital transformation. Whether framed as ESG, the SDGs, or corporate social responsibility (CSR), companies worldwide are supporting public sector institutions in ways that help improve their capacity for evidence-based decision-making. Activities –or data actions– being taken range from the transfer of actionable data to the public sector to providing tools or services that help improve the public sector’s ability to capture and utilize insights from data repositories.
To gain insight into how these trends are playing out in Asia, we at LIRNEasia conducted a mapping study to understand the private sector collaborations related to data to achieve the SDGs across the continent. This is part of a Global South mapping study that we are leading with Cepei in Colombia and with support from the International Development Research Centre. The study was based on desk research using internet searchers between March and May 2022. The search results comprised various documents, including reports, web pages, and occasional newspaper articles.
Our mapping study revealed significant information about data and the value of data actions. We were quite flexible when defining “data” but limited the study to digital data, which varied from personal to remote sensing data. At one end, we gained insights by looking at the content of social media posts (even with the user data anonymized). There were also transaction-generated data where the content was irrelevant, such as the data from call-detail-records (CDRs) generated on mobile telecom networks. At the other end, there are data from satellites that provide significant insights.
We were quite specific when defining actions for those data. The mapping study categorized data actions into 1) Capacity building and skill sharing, 2) Data analysis, 3) Data collection, 4) Data governance, 5) Data infrastructure, 6) Data mapping, 7) Data migration, 8) Data monitoring, 9) Data for impact assessments and measurement, and 10) Data sharing. There can be many further actions under data actions, but it is worth examining data sharing since it is common in Asia.
The term “data sharing” involves two or more entities. For this mapping study, we defined data sharing as a collection of practices, technologies, cultural elements, and legal frameworks that are relevant to any kind of information digital transactions. Data sharing is a joint use of resources. It can be a business transaction or an in-kind transaction with open access to data.
Data sharing was the most common data action in the Asia region. The mapping study identified 52 such instances in 14 countries. Interestingly, all data-sharing actions in the region were linked to the SDGs, the most common being climate action (SDG 13) and decent work and economic growth (SDG 8).
Our study found good examples of data-sharing actions implemented to mitigate the impact of natural disasters. The Gorkha earthquake in Nepal caused over 8,000 deaths and injured over 22,000 people in 2015. Large parts of the country became isolated due to landslides and avalanches along the Kathmandu Valley. The Flowminder Foundation, which focuses on improving the well-being of vulnerable populations in low and middle-income countries, provided a rapid analysis of human movement after the disaster, including patterns of return to affected areas. The Foundation had partnered with a mobile phone operator in Nepal and used their data to support response efforts to the humanitarian disaster. The population patterns revealed using these data would have been almost impossible to find using other data. In this data-sharing action, we see how partnerships helped to achieve the climate action targets.
Decent work and economic growth were other SDGs mentioned frequently in data-sharing actions. This SDG focuses on sustained and inclusive economic growth by creating decent jobs for all and improving living standards. The mapping study revealed a data-sharing action to prove the private sector’s contribution to achieving this SDG in Bangladesh. The data collection and analysis were a collaborative action of the Bangladesh Garment Manufacturers and Exporters Association, UNDP Bangladesh, and the Global Reporting Initiative. The 47 garment manufacturing factories involved make business decisions based on their impact on the environment and people, among other priorities. The factories self-reported business sustainability data, which helped communicate their contribution to national priority indicators and the SDGs.
While the Asian regional priorities appear to be climate action and decent work and economic growth, other regions (Africa, Latin America, the Middle East, and North Africa) prioritize good health and wellbeing, sustainable cities and communities, and gender equality through their data-sharing actions. This may imply that SDG priorities are not the same throughout the Global South. These differences are worth exploring in future studies.
The study revealed that sharing data is not just providing access to information. It creates trustworthy relationships among entities and all parties who work together to improve the community. We also find that most initiatives are working on sharing data and other data actions but are not publicly drawing the connections between their partnership actions and the SDGs. We had to examine the initiatives/partnerships in detail to understand which SDGs the initiatives contribute to. In catalyzing more private sector firms to come to the table, it might be worthwhile making these connections explicit.
The findings of the mapping study can be found in the study report, while the underlying data collected through the study (data actions) can be viewed and interacted with through the data visualization tool.
Based on the mapping study, two case studies were carried out on two organisations to get a deeper look at some of the challenges and opportunities in public-private data partnerships. Watch this space for more.
This blog post originally appeared on the Cepei website
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