Resolution 71/313, adopted by the General Assembly of the United Nations on 6 July 2017, establishes a list of 2321 indicators covering the 17 SDGS and the 169 related targets. It followed a long process of work by an “Inter-Agency and Expert Group on Sustainable Development Goal Indicators” set up in 2015 by the UN Statistical Commission. This list is called the “Global indicator framework on SDGs” and is to be refined annually and reviewed comprehensively in 2020 and in 2025. It will also be complemented by indicators at the regional and national levels, which will be developed by regional bodies respectively Member States. This is thus an evolving reference and according to resolution 68/261 of the UN General Assembly it is asked that the “Sustainable Development Goal indicators should be disaggregated, where relevant, by income, sex, age, race, ethnicity, migratory status, disability and geographic location, or other characteristics, in accordance with the Fundamental Principles of Official Statistics..” 

The indicators are classified into three Tiers, related to their level of conceptualisation (definition) and the regularity of corresponding data production: Tier I comprises indicators that are conceptually clear, have an established methodology and are data are regularly produced; the indicators of Tier II are also conceptually clear but data are not (yet) regularly produced; there is no established methodology for the indicators of Tier III. The classification is also regularly reviewed and some indicators have been reclassified (in particular those from Tier III) according to the progress of the work of the Expert group.  

The SDG data ecosystem is characterized by several tensions and thus core challenges: 

  • between global and local data needs – for instance between globally comparable statistics and disaggregated data that is compiled for local decision-making, 
  • between top-down data producers (such as UN agencies or multilateral and bilateral development entities) and bottom-up ones such as small civil society organizations or local companies; 
  • between structured data exchange processes, such those based on the Statistical Data and Metadata eXchange (SDMX) suite of standards, and more organic processes, such as informal in country data sharing between development actors; and  
  • between data producers and users from sectoral (health, education, etc.) and cross-cutting (gender, human-rights, partnerships) domains. 

Collecting reliable data to support the measurement of progress on the SDGs will depend on four key elements: 

  • Crafting a robust set of national monitoring indicators and gathering the information to produce them. A lot of countries are far from being able to produce the respective data on the whole set of the recommended indicators and to follow up on the regular adjustments that are made on the number of indicators and on the methodologies for their data production on a regular basis. Translating the international recommendations into meaningful country indicators has proven to be tricky: for some indicators the relevant data may be available but only for limited periods or years and for limited coverage and disaggregation (thematic/geographic). There is a sensitive trade-off to realise between the imperatives of international comparison, the needs for a country-tailored system of indicators and the existing national statistical data production (and the structure of this production). Another crucial aspect is to provide time series and to make sure that the production of the data is done regularly and not as a one-shot process. The initial step will be to map the existing statistical system in order to assess the data gaps and to discuss the ways to fill them up. Questions linked to the sources (methodologies, quality in particular), and to the disaggregation will have to be discussed while doing this data mapping. The exchange of the data sources between the various actors of the NSS will have to be secured using modern transmission tools. It will be important to document the associated meta-data and to share them with all the members of the NSS.  
  • Strengthening the country statistical capacity. In many countries, the statistical capacities are still weak. The resources allocated to data production and dissemination are low and they often remain at a level that doesn’t guarantee that the data producers are able to cover the whole extent of an increasing demand for statistical information. The pressure is very high on the data producers, who have to learn how to use more modern technologies for gathering, processing and disseminating data. In addition, the data production for the SDGs indicators will mobilise all the members of the NSS but also other actors (from the private sector or the civil society) who are not necessarily familiar with quality data production and, in particular, the fundamental principles for statistics. There is thus a huge demand for capacity reinforcement for both the individual professionals and their organisations/institutions. The NSS is a key player in this area and must be given the resources to assist its national partners. Capacity development and modernisation efforts should focus on capacities to produce statistics in general rather than to consider only the SDG indicators specifically.
  • Capitalizing on the data revolution, harnessing new technologies and new sources of data. In the last ten years, there has been an in depth reflection within the community of statisticians on how to use sources for statistics that would not necessarily involve large and costly operations (such as Censuses and surveys). This has resulted in a large number of experiences and tests realised in some countries on the use of administrative data and other data sources (open/big data, citizen generated data, satellite data …) for statistics which can now be shared and discussed, particularly in the context of the SDG indicators. New tools and techniques have also been developed regarding the gathering, processing and dissemination of data that may help accelerating the release of statistics and improving their freshness and use. 
  • Making the indicators and data available and accessible to all. Statistics are produced to be used and it is the responsibility of the data producers to make them available and accessible to the largest number of users. Some countries have set up SDG platforms (web sites or portals) where the latest data are regularly uploaded. There are two important aspects that have to be considered when disseminating statistics. First, the patterns for the consumption of statistics by the users evolved a lot in the last decade and electronic dissemination and on-line consultation are now central in data dissemination. The data producers must develop and maintain capacities in these areas that require specific knowledge and equipment. Second, trust in statistics builds largely on the transparency and the regularity of the release of the data to the users. It is thus crucial to set a release calendar and to respect it. 

Objective/ Outcome

High quality data relevant for the measurement of the SDGs are collected from statistical and non-statistics sources. The data for the SDGs indicators are produced and disseminated on a regular basis. 

Contents / Outputs

Country specific indicators are defined in alignment to the global SDG indicator framework 

  • SMART indicators,
  • International requirements versus national priorities (adjustment and contextualisation) are identified 
  • proxy indicators are developed (e.g. for Tier II and Tier III indicators),  

Data availability and data gaps are identified 

The data collection process is enhanced: 

  • SMART indicators,
  • International requirements versus national priorities (adjustment and contextualisation) are identified 
  • proxy indicators are developed (e.g. for Tier II and Tier III indicators),  

Data processing and validation is enhanced according to international standards 

  • New technologies (e.g. artificial intelligence, algorithms) are used 

The release of data underlies international quality standards. 

Possible Activities

Data assessment matrix for mapping (data gap analysis): 

    • Clarify which data are available and which are not 
    • How can data gaps be closed through new/other data sources? (digital solutions) readiness status, tier, metadata, national/external data, rephrasing, split, source of data and source of publication, organisation responsible for the data, custodian agency, breakdowns available, freshness, regularity of the production …

Technical feasibility:

    • issues linked to disaggregation (topical and geographical), use of qualitative sources and mix with quantitative ones, issues linked to the use of big data and open data …

Gathering and securing data sources:

    • Data transfer/exchange (SDMX), data storage, data archiving, treatment and documentation of the associated meta-data, data security and confidentiality … 

Data collection and processing

    • Use existing data sources  
    • Statistical offices use new data sources (e.g. data from telecommunication providers, social media, remote sensing, civil registration and vital statistics (CRVS) data, etc.) 
    • Use of new technologies for statistics (GIS and others …), artificial intelligence for statistics, data mining, digitalisation (open data for statistics) …
    • Cooperate with new actors (private providers, e.g. Gallup) to access alternative/innovative data sources (big data, open data, real-time data, citizen-generated data)  
    • Coordinate interfaces 
    • Create synergies between traditional (statistical offices/authorities) and ’new‘ actors (private sector including start-up scene) and their data 

Data Production/Data collection 

    • Review the technical feasibility of implementing indicators defined and agreed at the policy level (SMART) 
    • Operationalise monitoring 
    • Define approach to disaggregation bearing in mind the do no harm (DNH) principle (no disadvantage for vulnerable groups → think of monitoring as an intervention, be mindful of possible abuse by authoritarian regimes, etc.) 
    • Apply relevant standards
    • Collect and integrate qualitative data (e.g. using proxy indicators) into statistical systems in an intercultural context
    • Identify and procure required sectoral expertise  

Data validation and quality assessment

    • Agreement on concepts, standards and methodologies (compliance with international requirements (e.g. Standards and criteria of the UN Statistics Division (see below), consideration of the specific national context, involvement of all the producers and coordination with the users) 


    • Process raw and metadata using statistical programs (e.g. SPSS, Stata, [relational] databases, etc.)


    • National platform (accessibility, format and level of details/disaggregation, release calendar, micro data …), regional and international platforms (SDGs oriented and other – UN, WB …) 

Good Practices

GIZ examples:  

Links with other elements of the process landscape

  • Links with the Steering processes: The NSS and its partners from non-government must have the means to contribute to the data production and to data exchange. There are quality requirements along the whole process of data production and dissemination. 
  • Links with the other Core processes: It is crucial that the collaboration mechanisms among the actors are effective and that the respective mandates are understood, particularly for the non-government actors. Coordination among the actors involved is a key for a successful and relevant data production. The relevance of data dissemination is key for ensuring a proper usage of the data. 
  • Links with the Supporting processes: Statistical capacities will need to be strengthened beyond the members of the NSS. Digitalisation will greatly help in the mobilisation of new data sources. The indicators, the corresponding data and their dissemination must be the central part of the SDG national communication plan. 

National actors involved

All the actors who are producing information and data useful for the regular production of the SDG indicators: members of the NSS and other producers from the private sector and the civil society.