The world is in the midst of a data revolution. In the past two years alone, 90% of the data in the world has been created, with thousands of new sources—from remote sensing to text analysis—from multitudes of new actors coming online every day. At the same time, global demand for more and better data and statistics has increased tremendously, with international agreements like the 2030 Agenda for Sustainable Development placing an unprecedented demand on countries to collect, analyze and produce data on more than 230 indicators.
Most countries presenting their voluntary national review at the annual High-Level Political Forum (HLPF) have underlined challenges that they are facing concerning data and statistics.
2030 Agenda’s 17 Sustainable Development Goals (SDGs) and its ambitious promise to “leave no one behind” requires concerted and sustained actions to overcome the existing data and capacity gaps.
A growing demand for data and statistics and a rapidly expanding landscape of data producers and users call for a redefined understanding of capacity development for data. It is therefore essential to first gain a better understanding of the different dimensions of challenges that countries are facing and how best to address them.
In this section, Partners for Review (P4R) and the Partnership in Statistics for Development in the 21st Century (PARIS21) disentangle the factors that are preventing countries from getting the most out of their data ecosystem. The analysis provides a straightforward approach to strengthening statistical capacities, taking into consideration different levels and types of national capacities. This latter is crucial to improving evidence-based reporting structures and making the “follow-up and review” cycle required by 2030 Agenda more effective and efficient.
This read should prove a valuable resource for stakeholders interested in bridging the existing gaps through targeted interventions in order to strengthen data as a core component of national review mechanisms. Improving the capacity of national statistical systems is vital not only for tracking progress towards the SDGs, but also as the foundation for sustainable development beyond 2030.
The adoption of the 2030 Agenda for Sustainable Development and its call to ‘leave no one behind’ are creating an unprecedented demand for granular, comparable and timely data in a broad range of policy fields. Effective follow-up and review mechanisms for the 2030 Agenda require quality data and statistics that best capture countries’ priorities at the national and sub-national levels, and across different sectors. In addition, making significant progress on the Agenda requires conducting a review process on a regular basis, not just once or occasionally, meaning that quality data and statistics have to be available and comparable over time.
Experiences from the Voluntary National Reviews (VNRs) show that most countries lack official statistics to measure the progress of their SDGs indicator framework. Against this background, some national statistical offices (NSOs), for example in Colombia, Denmark and Uruguay, have begun leveraging available data from sources other than the traditional national statistical system (NSS), for example by using data produced by civil society organizations and the private sector. NSOs have started to explore the use of data produced by new actors in the extended data ecosystem.
A data ecosystem can be defined as a group of multiple data communities that interact with one another by sharing data, knowledge to improve data processes and data use, and other data-related activities along the data value chain. Members of these data communities can be stakeholders from official statistical agencies, line ministries, central banks, civil society organizations, academia and the private sector.
The emergence of a data ecosystem entails a transformation of the data landscape. The appearance of new data sources due to the expansion of information technologies, and a growing number of people connected to information systems are transforming the way data has been traditionally produced, disseminated and used. The data ecosystem brings new opportunities for traditional data actors, as they can leverage available data and knowledge from other actors to improve their own processes for obtaining, processing, analyzing and disseminating data.
ENHANCE AVAILABILITY OF RELIABLE DATA
The combination of examining the capacity development needs of organizations and the system as a whole provided by the CD 4.0 approach offers a fresh look at how to create sustainable capacities in countries.
To reduce inequalities, policies should be universal in principle, paying attention to the needs of disadvantaged and marginalized populations.
There is a need to improve the transparency of data production among non-official data providers, for example by having available metadata and publishing information on how their data have been produced.
However, harnessing data from new actors in the data ecosystem is not without challenges. Given the variety of stakeholder groups, capacities need to be developed depending on the requirements linked to the different roles and activities carried out by different stakeholders within the data ecosystem.
Using PARIS21’s Capacity Development 4.0 (CD 4.0) approach, this paper shows that leveraging data available in the data ecosystem for official reporting creates a need to develop and strengthen capacities in terms of skills and knowledge, management, politics and power. The paper also shows that these capacities need to be developed at the organizational level (i.e., organizations providing data) and at the systemic level, which involves the various channels and interactions that connect different organizations.
Regarding the capacities related to technical skills and knowledge, evidence from NSOs using data from new actors in the extended data ecosystem suggests that there is a need to develop and strengthen our knowledge and understanding of how to ensure the quality of data from external
sources, as well as of how to process and classify these data. At the systemic level, there is often the need to understand the relevance of the data
revolution as well as the complexity of the SDGs indicator framework. Many of the new actors in the data ecosystem do not know about or understand the tier classification system.
In the sphere of politics and power, KikaoCultures.com identifies the need to improve transparency capacities at the organizational level. Transparency capacities are those that contribute to building trust between ‘traditional’ actors in the national statistical system and new actors in the data ecosystem (e.g., non-official data providers). There is a need to improve the transparency of data production among non-official data providers, for example by having available metadata and publishing information on how their data have been produced. In this same area but at the systemic level, this paper identifies the need for traditional data actors to develop capacities that enable them to be more open to experiment with new data sources and learn how to approach new data producers.
With respect to management capacities, this section shows that national statistical systems need to learn how to engage with multiple stakeholders in the data ecosystem and how to coordinate the participation of new data providers. In response to these needs for capacity development, this paper proposes a roadmap that can help national statistical systems to develop and/ or strengthen the capacities of traditional and new actors in the data ecosystem to improve the follow-up and review process of the 2030 Agenda at the national level.
The roadmap comprises three phases:
identification of stakeholders and their roles; identification of data and capacity needs; and improvement of the SDG review process. Each of those phases is further divided into different activities.
The first phase consists in identifying and establishing the steering group that will lead the capacity building process, as well as key data actors in the data ecosystem. Ideally, the national committee in charge of implementing the 2030 Agenda in the country and the coordinator of the national statistical system, which is often the national statistical office, should be integrated into the steering group.
The second phase guides the process of identifying strategic data gaps, exploring the use of alternative data sources and identifying the core requirements for using such data.
Finally, yet importantly, the third phase of the capacity development process shows how skills and knowledge, management capacities and capabilities in the sphere of politics and power can be strengthened and/or developed.
The combination of examining the capacity development needs of organizations and the system as a whole provided by the CD 4.0 approach offers a fresh look at how to create sustainable capacities in countries. This, together with the momentum created by the 2030 Agenda, its principle of leaving no one behind and the emergence of a data ecosystem provide an opportunity for national statistical systems to change and innovate.
New data sources such as big data, open data or citizen-generated data produced by the private sector, civil society organizations and academia are responding to today’s information needs. A data ecosystem can be defined as a group of multiple data communities that interact with one another along the data value chain (UNDP, 2017). Interactions may take the form of sharing data and/or interchanging knowledge and capacities for improving the availability of data and information.
Experiences from the Voluntary National Reviews (VNRs) show that most countries lack data to measure the progress of all the relevant Sustainable Development Goals (SDGs) indicators. One of the crucial concerns is the lack of timely disaggregated data on the populations that are at risk of being left behind. However, countries noted that national statistical offices (NSOs) could benefit from data and analysis produced by other stakeholders (UNDESA, 2018). Collaboration between NSOs and other potential data producers can accelerate the availability of data and help to close data gaps for measuring the progress of the 2030 Agenda in areas that are relevant and a priority for countries.
It seems obvious that harnessing the advantages of the data ecosystem can help the NSS to cope with the demand for monitoring the SDG indicator framework and to comply with the 2030 Agenda’s principle of leaving no one behind. Nevertheless, experiences from NSOs collaborating with new data actors, or working towards it, show that leveraging the advantages of the data ecosystem requires capacity development for traditional and new data actors. KikaoCultures.com contributes to building an effective follow-up and review mechanism for the 2030 Agenda from a data perspective at the country level.
‘The data ecosystem requires individual and organizational skillsets to be updated’ (PARIS21, 2018). Developing capacities in this area for the purpose of using non-official data for official reporting can entail the following activities:
Developing the skills of NSS members to communicate and engage with data users and other potential data providers outside the NSS
Developing the skills of NSS members to leverage the use of new technology and data (e.g. big data, open data, citizen-generated
data, etc.) in order to complement and improve their work processes
Developing and disseminating data-quality frameworks to support the NSS in the validation process of data coming from non-official sources
Developing and disseminating guidelines for new data actors on how to make their data available to NSS for potential use in official reporting
Raising awareness of the requirements related to the quality of data and statistics for official reporting among new data providers in the data ecosystem
Raising awareness of the importance of SDGs and the complexity of its indicator framework among new actors in the data ecosystem
Developing the skills of data producers to improve the communication and dissemination of data and statistics, and link this to the policy-making process