Page 82 - Proceedings of the 2017 ITU Kaleidoscope

2017 ITU Kaleidoscope Academic Conference

High-quality data is critical for transforming the SDGs into Applying a data revolution perspective to SDGs involves
useful tools for problem-solving and for proper decision- the integration of new data (e.g. crowd-sourced data,
making. Without timely and reliable data, the design, citizen-generated data, etc.) with traditional data (e.g.
tracking, and assessment of policies are almost impossible. census information) to produce high-quality information
For these reasons, data is one of the key elements of the that is more detailed, timely and relevant for many purposes
accountability framework for the SDGs. High-quality data and users, especially to foster and monitor sustainable
that can be transformed into information that reflects the development [6]. Traditional statistics entities must
progress, monitors the allocation of resources, informs therefore not only engage with new data sources but also
policy making, and assesses the impacts of policy and with new technologies and data analysis tools. Supporting
programs, is fundamental for accountability and monitoring the evolution and modernization of the statistics production
of the 2030 Agenda. systems is also demanded by the large number of indicators
for which novel and innovative data sources and
Notwithstanding the inherent complexity of the national methodologies are needed [5].
data ecosystems, this research adopts an organization
thinking approach to explore the potential interventions National Statistical Offices (NSOs), the traditional
towards improving the capacity of organizations within the guardians of data for the public good remain central to the
national data ecosystem to be more effective in producing government efforts to harness the data revolution for
high-quality data and therefore in monitoring the SDG sustainable development. To fill this role, however, they
indicators. need to change more quickly than in the past. To be able to
adapt to the constant changes, they need to abandon
The rest of the paper is organized as follows. Section 2 expensive and inefficient production processes, incorporate
discusses the unfolding data revolution especially in the new data sources, and ensure that the data cycle matches the
context of social indicators monitoring for SDGs. Section 3 decision cycle. However, many NSOs lack sufficient
presents an extensive review of the current initiatives on capacity and funding, and remain vulnerable to political and
improving the quality of statistical data. Section 4 motivates interest group influence. Data quality should be protected
for the use of Capability Maturity Models (CMM) within and improved by strengthening NSOs, and ensuring they are
the SDG indicators framework for improved quality of SDG functionally autonomous, independent of sector ministries
indicators data. This is followed by a presentation of a and political influence. Their transparency and
preliminary multidimensional prescriptive CMM in Section accountability must be improved, including their direct
5. Sections 6 and 7 provide recommendations and a communication with the public they serve [6].
conclusion to the paper (respectively).
The data revolution, as any transformation, raises new risks.
2. THE DATA REVOLUTION One of the main challenges for monitoring SDGs is to
minimize the risks and maximize the opportunities that
The volume of data in the world is increasing exponentially. come from the data revolution for sustainable development.
One estimate is that 90% of the data in the world has been Among them, the enlargement of the data divide (i.e. the
created in the last two years [6]. The volume and types of gap between those who have ready access data and
data available nowadays have increased exponentially due information, and those who do not) is one of the riskiest.
to the evolution of technology and its impact on the social Inequalities in the access and use of information must be
behavior. All players in the ecosystem, including tackled to reduce the breach between information-rich and
governments, companies, academia, and civil society, need information-poor countries. A way of managing risks and
to adapt to this new reality and need to be prepared to exploring opportunities is by enhancing national capabilities
continue adapting to a world that produces more and more in data science. National and international support and
data, generated at a faster speed, and coming from new resources are needed, especially in developing countries, to
sources. This new reality has been defined as the data achieve high-quality official statistics that are required for
revolution. the data revolution to contribute to sustainable
development.
The concept of data revolution was coined in 2013 in the
report of the High-Level Panel of Eminent Persons on the Several efforts and important investments have been made
post-2015 Development Agenda [8] and it is defined as “an for monitoring MDGs. Some of those efforts have been
explosion in the volume of data, the speed in which data is successful and have improved the way data for monitoring
produced, the number of producers of data, the and accountability is used. Consequently, there is now a
dissemination of data, and the range of things on which much better understanding of the realities of the world,
there is data, coming from new technologies such as mobile including the ones of the people that need more help.
phones and the Internet of Things, and from other sources, However, and in spite of this significant progress, some big
such as qualitative data, citizen-generated data and challenges still need to be tackled:
perception data” [6, p. 6].

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