Agenda 2030 and NSDS

The United Nations  Member States at the Sustainable Development Summit in New York adopted the 2030 Agenda for Sustainable Development (henceforth called the Agenda 2030) on 25 September 2015. The Agenda lists 17 sustainable development goals  (SDGs), 169 targets and 232 indicators that reiterate recognition of poverty as the greatest global challenge and advocate for the principles of “leaving no one behind” and “reach first those who are furthest behind”. 

The Agenda 2030 also recognizes and utilizes data and statistics as a means to implement and monitor the SDGs (Global indicator framework for the SDGs), and by extension, a tool for governments to design, implement, and monitor national development goals incorporating the SDGs. The Agenda 2030 has significant implications for statistical systems and may have started to influence the design of strategies for and support to the development of statistics at the international, regional and national levels.  

Statistical development is deemed vital in the SDGs that the Agenda 2030 included specific targets to address fundamental challenges to statistics. The overarching goal for statistics is to generate “high quality, timely, and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability and geographic location”. With 232 indicators to measure and monitor 17 goals and 169 targets (adopted by the UN General Assembly on 6 July 2017), the goal is supported by the commitment to address systemic issues, including capacity and methodology gaps, in data, monitoring, and accountability.

To address this goal, the High Level Panel, appointed by UN Secretary-General Ban Ki-moon to advise on the global development agenda after the 2015 millennium development goals (MDGs) expressed the need for a “data revolution”. The data revolution sparked many ideas and initiatives that include big data, citizen-generated data, and building data-ecosystems  . It has also reiterated calls to modernize national statistical systems   to consider non-traditional sources of data and use of data science and advanced technology to adapt to new data demand and emerging environment.

Are national statistical systems prepared and up to the challenge? Can national statistical systems at their current state meet new and for most, additional data demand? Are NSOs ready to lead and coordinate the NSS and the data ecosystem at large to deliver the data?  

What may considered a response to these questions, the “Cape Town Global Action Plan for Sustainable Development Data” provides a global vision for better data, calls for a commitment by governments, policy leaders and the international community to undertake key actions in six strategic areas, including:

  • Coordination and strategic leadership on data for sustainable development;
  • Innovation and modernization of national statistical systems;
  • Strengthening of basic statistical activities and programmes, with particular focus on addressing the monitoring needs of the Agenda 2030;
  • Dissemination and use of sustainable development data;
  • Multi-stakeholder partnerships for sustainable development data; and
  • Mobilise resources and coordinate efforts for statistical capacity building.

The SDGs data demand will require substantive reforms in the NSS, which may need to be radical for some countries. These reforms will entail a new NSDS or an updated one to integrate the Agenda 2030. There are at least three (3) requisites to ensure that NSDS can effectively support the SDGs: 
 

  • Concrete national policy framework integrating the SDGs, which requires direct coordination with the national development agency;
  • Sustainable framework for financing, which will need stronger advocacy for change among and mobilization of resources by all stakeholders, the governments in particular;
  • More focused, targeted, and balanced capacity development across all stakeholders, producers and users alike.


Concrete actions

  • Clarify the national development policy framework, ensuring integration of the SDGs. Step 1.1
  • Clarify the national policy on statistics. Step 1.1
    • Prepare a policy document on the need to develop statistics in support of the national development agenda including the SDGs.
    • Highlight international/regional commitments made by the government relating to data support to the SDGs.
    • Consult with the highest level political authority for official recognition and endorsement of the policy document on statistics that allows SDGs monitoring.
  • Prepare the NSS roadmap and develop the advocacy programme and toolkit to SDGs indicators domestication. Step 1.2 | Step 1.3
    • Conduct dialogue with the national development agency, the national budget agency, and all program ministries/departments and other relevant agencies.
    • Highlight international/regional commitments made by the government relating to data support to the SDGs and the role of the ministries/agencies.
  • Assess the NSS across all sectors and/or subject-matter areas  , to ensure the domestication and integration of the SDGs indicators. Step 3.1 | Step 3.2 | Step 3.3 | Step 3.4
    • Identify the most relevant and priority users of SDG data and their needs.
    • Map the SDG indicators with the national development indicators and analyze data quality gaps, including SDGs domestication. ADAPT
    • Examine existing relevant legal frameworks and policies, resources, and capacities of key ministries/agencies/other data ecosystem.
    • Calculate cost of resources to address gaps. 
  • Identify strategic goals and key outputs toward improvement of SDG statistics. Step 4.2 | Step 4.3
    • Prioritize improvement of administrative data   and other non-traditional sources in priority sectors and subject-matter areas (including SDGs) to produce subnational level data, gender, and other more granular details of data.
    • Explore cross-cutting strategies and inter-sectoral linkages (agriculture and environment, health and environment, infrastructure, etc.)
    • Consider partnerships with big data sources such as data science firms, telecommunication companies, and other service providers to share data and/or help develop applications to extract data. 
    • Assess and consider existing systems of the private sector, academic and research community, and civil society organizations to produce specific SDG data.
  • Identify specific actions and corresponding costs, as well as key risk factors and mitigating measures. Step 5.1 | Step 5.2 | Step 5.3
  • Mobilize resources and implement actions. Step 6.2 | Step 6.3
    • Establish institutional partnerships between NSO and the relevant ministries/agencies /other data ecosystem mandated to implement actions on SDG data development/production.
  • Monitor results of strategies and actions. Step 6.4
    • Design the SDG data accountability monitoring mechanism. Use the Voluntary National Review (VNR) mechanism where possible.
    • Analyze key results and milestones by SDG and target and accountability centers (e.g., by ministry/agency).