Using Data to Advance the 2030 Agenda

(MainsGS2:Effect of policies and politics of developed and developing countries on India’s interests, Indian diaspora.)

Context:

• Data has emerged as a powerful tool for driving sustainable development and achieving the 2030 Agenda.
• However, the data landscape across the G20 remains highly uneven, with some countries being able to effectively harness data for development (D4D), while others experience significant challenges in doing so. data for development (D4D)

Variation in access to data:

• The data landscape within the G20 presents a complex picture, with significant variations in the quality and availability of and access to data. 
• While some G20 countries have robust data ecosystems and well-established statistical systems, others lag behind with respect to their data collection and analysis capacities. 
• This results in a wide range of gaps, including the absence of disaggregated development data, and the invisibility of data pertaining to marginalized communities, and could also create skewed representations of on-ground challenges that hinder the formulation of responsive policies and interventions.

Strengthen D4D ecosystems:

• Strategic investments in data collection, reporting and analysis systems, particularly in countries with limited technical capacity, are a prerequisite for the success of D4D programmes. 
• As such, G20 member states, particularly low- and middle-income countries (LMICs), need to identify areas where additional resources and technical support are required, and how weaknesses in the monitoring and evaluation of D4D initiatives and evidence-based interventions might be addressed. 
• Corrective measures, including the smart allocation of resources to strengthen D4D ecosystems, could significantly improve prospects for socio-economic growth and betterment.

Bridge the data divide:

• G20 countries must support internal capacity development related to data collection, focusing on areas with conspicuous data gaps and marginalised populations. 
• Within their sovereign space, countries can foster partnerships between sub-national governments, civil society, and private sector entities to strengthen and expand data collection networks. 
• There is a particular need to train stakeholders to disaggregate and anonymise the data collected, so that the possible needs of the data subjects can be clearly identified on the one hand, and their identities remain secure and confidential on the other.

Coordination and cooperation required:

• Data partnerships need to be established between countries with advanced data capabilities and those that have traditionally experienced D4D-related challenges. 
• New South-South and North-South cooperation models must be devised to exchange knowledge and promote collaboration. 
• Establishing mechanisms for sharing data between G20 countries would improve access to D4D. 
• There is a marked need to promote the use of open data, and to build open-access repositories through which development datasets can be made publicly available. 

Conclusion:

• A comprehensive data privacy and security framework must be put in place to ensure confidentiality and build trust, while also creating the groundwork for harmonized standards, approaches and protocols that allow specific types of development data to be shared across borders.