SDG Goals

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To fully implement and monitor progress on the SDGs, decision makers need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. Data availability and quality have steadily improved over the years. However, statistical capacity still needs strengthening and data literacy must be enhanced at all levels of decision-making. This will require coordinated efforts on the part of data producers and users from multiple data systems. It will also demand innovative ways to produce and apply data and statistics in addressing the multifaceted challenges of sustainable development.

New approaches to capacity development for better data

The scope of traditional statistical capacitybuilding is widening to nurture collaboration and synergies across increasingly complex data systems. The goal now is to mainstream data innovations into official statistical production processes and focus more sharply on users’ needs. Efforts are also being made to ensure that statistical capacity-building initiatives are home-grown, long-term, and generated and managed collectively by those who benefit from them.
Many countries are making strides in this direction. For example, following the launch of its second National Strategy for the Development of Statistics in June 2016, Equatorial Guinea adopted an Advanced Data Planning Tool (ADAPT). Its purpose is to facilitate the budgeting and monitoring of development activities assigned to key department heads, allowing for direct ownership and reporting of progress. Similarly, in 2016, the heads of national statistical offices in 24 African countries participated in leadership training that emphasized the importance of active leadership and change management.

Innovations and synergies across data ecosystems

Increasingly, public-private partnerships are enabling the use of big data and other non-traditional data sources in policymaking by mainstreaming their use in official statistics. This is made possible through various institutional arrangements, including in-house production of statistics by data providers, direct transfer of private data to end users, the transfer of private data to a trusted third party and the outsourcing of certain functions. It is crucial that national statistical offices, supported by international organizations, continue to advance the design and implementation of incentives and business models that encourage effective partnerships for improving the availability and quality of data for sustainable development.
Data innovation projects are being implemented by a broad range of public and private actors in many parts of the world, including Africa, Asia and Latin America. The results are promising. For instance, crowdsourcing exercises are being employed for the collection and analysis of data for disaster risk management and data on climate change. That said, the use of innovative technologies and new data sources for the public good is not without risk. It also presents institutional challenges: merging new data sources with traditional ones requires the modernization of data governance and quality frameworks to ensure national ownership and the establishment of transparent mechanisms. Such mechanisms allow partners from the private sector, academia and civil society to contribute their data, expertise and technology to achieving the SDGs.

Leave no one behind

National averages, even city averages, often mask wide disparities among population groups. The identification of people suffering from deprivation therefore requires sufficiently detailed data across multiple dimensions, including age, sex, geography and disability status, among others. Any global or national statistical system must ensure that the coverage and level of data disaggregation for the follow-up and review of the 2030 Agenda leaves no one behind.
Towards this end, national statistical systems need to invest in the technology and skills necessary to collect and integrate data from multiple sources, including integration of geospatial information with statistics and other data. This means making better use of traditional statistical surveys, censuses and administrative records. It also means harnessing the power of technology to leverage new sources of data, such as from cell phone records, Earth observations, other sensors and social media. More citizen-generated data are also being used to monitor the needs and progress of vulnerable groups. However, new methodologies need to be developed to ensure the quality and reliability of such data.

Understanding the world through data

When properly designed, visual information facilitates data exploration and processing for evidence-based decision-making and advocacy. Data visualization and storytelling can connect users with data on sustainable development, enabling them to discover, understand, and communicate patterns and interrelationships in the wealth of data and statistics that are now available.
Today, there is an explosion of commercial and open-source frameworks and tools for data visualization. To take advantage of them, national statistical systems need to engage with diverse communities of data scientists and analysts ready to put their expertise to the service of sustainable development.
Data users also need help in making sense of the overwhelming volume of data and information they are presented with every day. This can be accomplished by increasing collaboration across sectors and enhancing users’ skills. In addition, policymakers and the public in general must improve their data literacy. The National Institute of Statistics of Rwanda and the Partnership in Statistics for Development in the 21st Century (PARIS21), for example, are partnering to provide training to journalists from local radio, television, print and online media along with the Executive Secretary of the Media High Council of Rwanda.

Data principles and governance

Data standards and best practices need to evolve in parallel with available technology and users' needs. Data developers and users must therefore engage on an ongoing basis, recognizing the policy context for the SDG data ecosystem. For instance, new open data management frameworks are needed to foster innovation, while providing continuity and facilitating interoperability among data providers, managers and users.
In recent years, a number of initiatives have reviewed ways to possibly expand existing data principles and standards to non-traditional data sources, such as big data. The focus has been on new data standards that would build upon existing ones to facilitate their adoption and rapid scale up among stakeholders from both public and private spheres.

The way forward, starting in Cape Town

The first United Nations World Data Forum, held in January 2017 in Cape Town, South Africa, has led to the launch of a number of related initiatives. It also provided a platform for the presentation and review of the Cape Town Global Action Plan for Sustainable Development Data. The plan was developed by members of the official statistical system and other data communities, including civil society, the private sector and academia; it was later adopted by the United Nations Statistical Commission at its forty-eighth session. The plan provides strategic guidance for the design and implementation of country-led statistical capacity-building needed to achieve the 2030 Agenda, and identifies six strategic areas for action listed below.