‘If we are not counted, we do not count, and we remain invisible’
- Representative of the Stakeholder Group of Persons with Disabilities
Data plays an increasingly central role in discussions around the sustainable development goals (SDGs) and was featured prominently at the 2018 High-Level Political Forum on Sustainable Development (HLPF). While discussions about SDG data have been present - to various degrees - at all HLPFs (see the analysis of four years of HLPF reports), 2018 brought a certain maturity to the discussion. The creation of the Cape Town Global Action Plan for Sustainable Development Data in March 2017, and the adoption of the Indicator Framework in UN General Assembly resolution A/RES/71/313 in July 2017, moved the discussion from ‘we need more and better data’ to ‘we need more and better capacity, partnerships and investment for data’. This shift in focus was reflected at the two official sessions and numerous side events that were dedicated to data challenges and opportunities for the SDGs.
The importance of data for the SDGs was underscored by many. For example, in his opening address, Liu Zhenmin (Under-Secretary-General of Economic and Social Affairs of the UN) emphasised the need to ‘ensure that all countries have rigorous evidence and data to guide their actions towards 2030’. This wish was reflected in the Ministerial Declaration, adopted at the end of the conference, which stated the importance of evidence-based policy making ‘underpinned by high quality, timely, reliable and disaggregated data’ (paragraph 10), which requires strengthening ‘collaboration at the bilateral, regional and global levels for capacity building and sharing of best practices’ (paragraph 18).
Despite the general excitement about the ‘data revolution’ in today’s society, the amount of funding going to development data is relatively small, about 0.33% of the total official development assistance, and it has not increased in the last few years. While this might suggest a modest role of data in development efforts, the discussions about data reflected a clear sense of urgency. In the first place, there is simply a great amount of data needed to measure the 232 SDG indicators, and without this data ‘we’re flying blind on the 2030 Agenda’ (John Pullinger, UK National Statistician). Yet, two-thirds of the SDG indicators currently lack sufficient data, and even when the data is available, it is often not disaggregated.
There is a particular need for the disaggregation of data in order to integrate income, sex, age, race, ethnicity, disability, and geographic dimensions. There is an overarching belief that disaggregated data is needed to make visible those people who are currently unseen. However, the sensitivities that may arise from hyper-disaggregated data, such as discrimination and data protection concerns, were largely absent from the discussion.
Data is not only needed to monitor and evaluate, but also when ‘looking forward’, designing policies, and allocating resources. In fact, data can be seen as the ‘lifeblood of decision-making’ (UN Data Revolution Report). How can we improve the lives of those most left behind without knowing who and where they are? In addition, some pointed out the utility of data in communicating with the public and holding governments accountable.
The measurement of the SDGs puts National Statistical Institutes (NSIs) at the centre stage. They have a key role to play in ensuring that data is objective, independent, and unbiased, and in turning data into insights that are usable for policymakers, adapted to local needs.
Considering the large data gaps for monitoring the SDGs, many started to look for alternative sources that could be integrated into existing measurement efforts. For example, big data could provide valuable - and possibly more accurate - information to be used for monitoring and policy implementation, especially when it is combined with traditional statistics. Such new sources of data could even result in the possibility of real-time monitoring of the SDGs. Structured big data in particular, such as satellite imagery and mobile data, was considered to be a source of potentially great value.
In fact, the utility of earth observation data was discussed in several sessions, and was even the topic of a full-day workshop. Geospatial information could provide new insights, identify inter-relations between the SDGs, and allow statistical data to ‘come to life’ (Greg Scott, Inter-Regional Advisor on Global Geospatial Information Management at the UN Statistics Division); its visual nature lends itself well as a tool for storytelling and awareness raising.
At the same time, some feared that the excitement around big data has led to unrealistic and misguided expectations. They pointed out that big data can easily be manipulated and unrepresentative, which is particularly problematic in fulfilling the pledge to leave no one behind. In fact, ‘the growing specter of “big data” may have cast its shadow’. Rather, they urged to focus on the integration of qualitative data, traditional, indigenous knowledge, and participatory methods of data collection. That said, ‘big’ and ‘small’ data might not be mutually exclusive. For example, Australian Ambassador Gillian Bird raised the need of better understanding ‘the individual level’ and combining geospatial data with disaggregated data ‘to ensure no one is left behind’.
The data revolution, with its new, automatically generated, sources of data, might seem like a seamless solution for closing the SDG data gaps. However, whether including big data or traditional knowledge, the smooth integration and standardisation of such data will continue to be challenging, with official statistics already being plagued by coordination challenges. The comparability of data could be promoted through a highly standardised information architecture - ‘a dream for a statistician’ - and yet such an architecture would risk putting measurement efforts into ‘straitjackets’ (Stefan Schweinfest, Director of the UN Statistical Division).
The real challenge, highlighted in all sessions and side events related to data, relates to a lack of capacity and resources. While even sophisticated NSIs grapple with this challenge, the situation is particularly precarious for NSIs in developing countries. This may even result in a ‘data divide’. The scarcity of basic data in developing countries could lead to uninformed decisions and an inability to properly target interventions to help the most vulnerable. In fact, global data divides might even hold back collective progress.
Increasing the capacity of NSIs to produce the disaggregated data needed to monitor progress includes taking institutional measures. Capacity building efforts need to be accompanied by strategies, policy frameworks, codes of practices, and an infrastructure that allows the new data ecosystem to thrive.
Capacity building requires financial resources and knowledge sharing. While the additional amount of money needed to fund SDG data - annually about USD$ 200 million - is relatively small compared to the sum needed for SDG implementation, there is an important funding gap. To close this gap, there is a need for political support and awareness, and a change of the perception about the utility of official statistics among policymakers. This also requires smoothening the relation between policymakers and data producers, with NSIs demonstrating the power of data; ‘unless data is made valuable, we won’t get the wallets to open up’. Finally, partnerships with civil society, academia, and the private sector will be key. These sectors could address gaps in data and methodologies, and share the burden of work.
The 2018 HLPF put data in the spotlight, recognising its necessity both for the monitoring and the implementation of the SDGs. At the same time, it has become clear that current data efforts fall short of the data needs of the 2030 Agenda, especially due to the lack of capacity and resources. This even risks creating a data divide that only provides the possibility of evidence-based decision making to those systems that are already well-advanced, potentially further leaving behind vulnerable populations that are made invisible with incomplete data. Despite the many new available sources of data, it remains a challenge to ensure their reliability and smooth integration into measurement systems. Fortunately, even small investments can make a big difference, and the amount of funding needed to overcome SDG data challenges is relatively feasible. The prominent place of data and statistics in the 2018 HLPF may reflect an increased political awareness, which is needed to drive investments into statistical systems. Now, it will be paramount to get the system’s wheels turning to ensure that no one is left behind in the data revolution.
Side events and other sessions