Page 83 - Proceedings of the 2017 ITU Kaleidoscope

Challenges for a data-driven society

• Many people and groups are still ignored – some From an extensive literature review, a wide set of initiatives
ethnicities, for instance, are being left further behind. aimed at improving the functioning and the results
• There are data and knowledge gaps – new science, generated by the national statistics entities have been
technology and innovation (among others) are needed identified – including models, standards, frameworks,
to fill such gaps. processes and programs, enterprise architectures, and
• There is not enough high-quality data – many countries readiness studies. Figure 1 shows some existing efforts
cannot rely on their data because it is outdated, grouped by categories.
incomplete, or it simply does not represent the reality
accurately.
• Lots of data that is unused or are unusable – many
countries still have data that is of insufficient quality to
be used to make informed decisions, for governments
to be accountable or to fostering innovation.

These challenges limit governments’ ability to act properly
towards the achievement of the SDGs.

A key role of the UN and other international organizations
is to set up principles and standards, and to lead the actions
according to common norms. Mobilizing the data revolution
for achieving sustainable development urgently requires
actions such a raising awareness, improving capacity,
setting standards, and building on existing initiatives in
various domains, among others. In particular, initiatives
built over previous foundations should consider the data
production ecosystem to understand the multi-stakeholder
engagement issues related to data sharing, ownership, risks,
and responsibilities. Such initiatives are indispensable to
enable data to play its essential role in the implementation
of the development agenda.
Fig. 1: Initiatives for improving quality in statistics generation
The Independent Expert Advisory Group on a Data
Revolution for Sustainable Development calls for Among the frameworks for data or statistics, the following
“international and regional organizations to work with other can be highlighted:
stakeholders to set and enforce common standards for data
collection, production, anonymization, sharing and use to • National Statistics Quality Framework – based on the
ensure that new data flows are safely and ethically European Statistical System dimensions of quality (as
transformed into global public goods, and maintain a system laid out in the National Statistics Code of Practice
of quality control and audit for all systems and all data Protocol on Quality Management), aims to improve the
producers and users” [6, p. 18]. Towards this aim, efforts quality of data collected, compiled and disseminated
must be made to support countries in empowering their through enhancing the organization's processes and
statistical system to be resourced and independent in order management [11].
to be able to respond to new realities of data, and to • Frameworks for National Statistics – define the status
produce and use high-quality data in quantitative and and governance framework for official statistics. For
qualitative ways.
example, the one developed by the UK Statistics
Authority [12] focuses on economy and society.
3. STATISTICS DATA QUALITY INITIATIVES
• Statistics Quality Frameworks (SQF) – set forth main
quality principles and elements guiding the production
The importance of the role of the national statistics entities
in the production of official statistics for the monitoring and of statistics. An example is The European Central Bank
Statistics Quality Framework [13].
implementation of the development agenda, and the
importance of high-quality statistics have been described in • Monitoring and Evaluation Frameworks – aim at
literature [9]. In order to serve sustainable and inclusive identifying trends, measuring changes and capturing
development, statistics should be obtained from high- knowledge to improve programs’ performance and
quality, timely, easily accessible, reliable and disaggregated increased transparency. For example, the SDG Fund
data. Data disaggregation, in particular, is key to achieve Secretariat [14] has established a Monitoring and
the principle of leaving no one behind [10]. Evaluation framework with key indicators that allows
to obtain a comprehensive overview of the contribution
to sustainable development.

– 67 –