Data quality gaps for the UN SDG report according to current literature and reports.
|#||Data Quality Gap||Supporting quote||Suggested improvement strategies for better DQF design|
|1||Timeliness, punctuality and accuracy.||“Most countries do not regularly collect data for more than half of the global indicators. The lack of accurate and timely data on many marginalised groups and individuals makes them “invisible” and exacerbates their vulnerability.”. (The-Sustainable-Development-Goals-Report-2019, 2019)||Timeliness and accuracy are among the essential dimensions for any DQF, and higher weight should be assigned to these two DQ dimensions under any future DQF.|
|“The demand for high-quality, timely and accessible data for development planning is increasing. To meet that demand, countries need to establish a strong national statistical plan that has sufficient funding and political backing to improve statistical capacity across the national statistical system.” (Ref.)|
|“Up to 77 countries remain unable to provide poverty data in a timely manner, and 44% of all countries are assessed as not even having basic functioning civil registration and vital statistics systems (CRVS) that are 90% complete”. (Jütting and McDonnell, 2017 p. 24).|
|2||Data accessibility, availability and comparability||“As in previous years, the Sustainable Development Report 2019 presents the most up-to-date metrics to gauge the performance of countries on the SDGs. Trends are presented at the level of goals and for 75 individual indicators. This year, we are able to report trends as of 2015 – when the SDGs were adopted – for 11 indicators (primarily for OECD countries). While this is progress, it underscores how infrequently the key data on the SDGs are collected today”. United Nations. (2019).||The data collection process for the new DQF should be connected directly to the source API of data.|
|3||Local DQFs||“Localised assessments of SDG progress are on the rise as there is a growing consensus that we will not achieve the SDGs without significant involvement of mayors and local policymakers”. United Nations. (2019).||One DQF that is comprehensive, unique and under one management is a must for all the countries to ensure the highest DQF.|
|4||Data comparability||“The 2019 SDSN survey finds there is no common approach across countries for monitoring SDG implementation. The number of national indicators to monitor the SDGs varies greatly from 34 indicators in Belgium to 244 in Canada. The European Union, via Eurostat, has identified 100 indicators to monitor the implementation of the SDGs in the EU. The frequency and approach to measuring distance to SDG targets is also very different across countries. Few have undertaken quantitative assessments of distance to SDG targets”. United Nations. (2019)||This emphasises the need for one universal DQF.|
|5||New data sources||“New sources of data, including big data, remote sensing, and satellite imagery, can help bridge data gaps in official statistics and support evidence-based policymaking. TReNDS, the SDSN’s thematic network on data and statistics, provides guidance on how to improve the quality of available data and ensure adequate data governance”. United Nations. (2019)||The new DQF should include new dimensions that are able to bridge the gap between traditional data and the big data era.
If data are collected for direct sources, then the new DQF should include a DQ dimension and indicators that can measure quality from new and direct sources of data.
|“New data sources and technologies for data collection and for the integration of various data sources will need to be explored, including through partnerships with civil society, the private sector and academia. The integration of geospatial information and statistical data will be particularly important for the production of several indicators.” (The-Sustainable-Development-Goals-Report-2019, 2019)|
|“New data sources and technologies for data collection and for the integration of various data sources will need to be explored, including through partnerships with civil society, the private sector and academia”. United Nations. (2019)|
|“The integration of geospatial information and statistical data will be particularly important for the production of several indicators”. United Nations. (2019)|
|6||Data collection||“Data measuring household income for that analysis were limited. Only 13 countries in sub-Saharan Africa had data on income growth for the most recent period. That points to the ongoing need for improved data collection and statistical capacity-building, especially in the poorest countries”. United Nations. (2019)||Cost to maintain high data quality is very high for poor countries; therefore, measuring data cost as a quality dimension is essential.|
|Jütting and McDonnell (2017) reported that 55 countries have a methodology but data is not yet being collected and reported for them in most countries.|
|7||NSO funding||“In 2018, 129 countries worldwide had implemented a national statistical plan, up from 102 in 2017. However, many countries lacked the necessary funding to do so. In sub-Saharan Africa, only 23% of plans were fully funded, compared to 94 per cent in Europe and Northern America.” United Nations. (2019)||Strong statistical plans are a must for designing a new DQF. This goal has to be strategically planned by UNSD and promoted to all countries. Otherwise, countries lacking DQFs or the necessary funding will suffer from very low data quality.|
|8||DQ cost||“In 2016, countries received support valued at $623 million from multilateral and bilateral donors for all areas of statistics, up from $591 million in 2015. Such support increased by almost $400 million from 2006 to 2016, yet was still insufficient to satisfy data and statistical demands created by the SDGs. To meet statistical capacity building objectives by 2030, current commitments to statistics— 0.33 per cent of total ODA—need to double”. United Nations. (2019)||Measuring the cost between low and high data quality is an essential indicator for improvement.|
|10||New data sources and traditional DQF||“Tracking progress on the SDGs requires the collection, processing, analysis and dissemination of an unprecedented amount of data and statistics at subnational, national, regional and global levels, including those derived from official statistical systems and from new and innovative data sources”. United Nations. (2019)||The new DQF with new dimensions should be designed to support artificial intelligence methodologies.|
|15||Methodological soundness||Jütting and McDonnell (2017) report that 37.9% (88/232) of SDG indicators have no defined methodology and are thus uncollectable, a further 23.7% (55/232) have a methodology, but data is not yet being collected and reported for them in most countries. That means that even relatively sophisticated national statistical offices may have hands-on familiarity with only some 40% of the eventual full range of SDG indicators.||More quantitative methods should be applied under this dimension.|
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