The Big (data) Bang: Opportunities and Challenges for Compiling SDG Indicators
| Published date | 01 January 2019 |
| DOI | http://doi.org/10.1111/1758-5899.12595 |
| Author | Steve MacFeely |
| Date | 01 January 2019 |
The Big (data) Bang: Opportunities and
Challenges for Compiling SDG Indicators
Steve MacFeely
United Nations Conference on Trade and Development, Switzerland, and
Centre for Policy Studies, University College Cork, Ireland
Abstract
Official statisticians around the world are faced with the herculean task of populating the Sustainable Development Goals glo-
bal indicator framework. As traditional data sources appear to be insufficient, statisticians are naturally considering whether
big data can contribute anything useful. While the statistical possibilities appear to be theoretically endless, in practice big
data also present some enormous challenges and potential pitfalls: legal; ethical; technical; and reputational. This paper exami-
nes the opportunities and challenges presented by big data for compiling indicators to support Agenda 2030.
Introduction
In March 2017 the United Nations (UN) Statistical Commis-
sion adopted a measurement framework for the UN Agenda
2030 for Sustainable Development (UN, 2015), comprised of
232 indicators designed to measure the 17 Sustainable
Development Goals (SDGs) and their respective 169 targets
.1
These universal goals cover all three key development pil-
lars: economic, social, and environment, as well as enablers
such as institutional coherence, policy coherence, and
accountability. The ambition of this challenge led Mogens
Lykketoft, President of the 70th session of the UN General
Assembly, to describe it as an ‘unprecedented statistical
challenge’(Lebada, 2016).
National statistical offices (NSOs) and statistical agencies
of International Organizations (IOs) around the world and
members of the Inter-agency and Expert Group on SDG
Indicators (IAEG-SDGs), the group established by the UN Sta-
tistical Commission to develop and implement the global
indicator framework (GIF) for the targets of the 2030
Agenda are faced with several questions, among them:
whether that challenge can be met? And what contribution,
if any, might big data make? Of the 232 SDG indicators, only
93 are classified as Tier 1, meaning that the indicator is con-
ceptually clear, has internationally established methodology
and standards, and data are regularly compiled for at least
50 per cent of the countries. The remaining indicators are
Tier 2 (72 indicators) meaning the indicator is conceptually
clear but the data are not regularly produced by countries
or Tier 3 (62 indicators), meaning that no internationally
established methodology or standards are yet available. Five
indicators are determined as having several tiers (Inter-
Agency and Expert Group on Sustainable Development
Goals, 2018). In other words, as of May 2018, less than half
(only 40 per cent) of the SDG indicators can be populated.
At the end of the Millennium Development Goals (MDGs)
life cycle in 2015, countries could populate, on average, only
68 per cent of MDG indicators (UN Conference for Trade
and Development, 2016). Compared with the 169 targets set
out by the SDG programme, the MDGs requirements were
modest, both in number (21 targets and 60 indicators) and
complexity. If past performance is any indication of the
future, then it is not unreasonable to predict, that unless
something dramatic changes, the proportion of populated
indicators for the SDG GIF will not be significantly different
to the MDGs. Could big data be that dramatic change?
Over recent years the potential of big data for govern-
ment, for business, for society, for official statistics has
excited much comment, debate, and even evangelism.
Described as the ‘new science’with all the answers (Gel-
singer, 2012) and a paradigm destroying phenomena of
enormous potential (Stephens-Davidowitz, 2017) big data
are all the rage. Statisticians must decide whether big data,
which seem to offer rich and tantalizing opportunities to
augment or supplant existing data sources or generate com-
pletely new statistics, will be useful for compiling SDGs. The
jury is still out. On the one hand, some argue that big data
need to be seen as an entirely new ecosystem (Letouz
e and
J€
utting, 2015) whereas others argue to the contrary that big
data are just hype and that big data are just data (Thamm,
2017). Buytendijk (2014) argued that big data has already
passed the top of the ‘hype cycle’and moving toward the
‘trough of disillusionment’. Beyond the hype of big data,
and hype it may well be, statisticians understand that big
data are not always better data and that more data doesn’t
automatically mean more insight. In fact, more data may
simply mean more noise. As Boyd and Crawford (2012, p.
668) eloquently counsel ‘Increasing the size of the haystack
does not make the needle easier to find’.
In simplistic terms, one can think of big data as the
collective noun for all new digital data arising from our
digital activities. Our day-to-day dependence on
Global Policy (2019) 10:Suppl.1 doi: 10.1111/1758-5899.12595 ©2019 University of Durham and John Wiley & Sons, Ltd.
Global Policy Volume 10 . Supplement 1 . January 2019 121
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