Digitalisation and the associated rapid growth of the availability of data are affecting all areas of human activity, including politics, work, the economy, and other. A lot of hope and expectations is put on the digital transformation of our societies but this also raises complex challenges linked to participation, responsibility, security, and surveillance
The impact of digitalisation on monitoring the SDGs is too often only considered under the single angle of closing the data gap. It is true that using big data may greatly help in the data production for a range of the SDG indicators, their dissemination and their analysis. 2However, digitalisation may also impact the way stakeholders interact, exchange information, establish partnerships and communicate. Important aspects include:
- Data governance. Regulations on how data may be captured, stored and exchanged should ensure that people are protected (confidentiality, right to oblivion, right to correction) and that the data is secured. The legislative framework should also set rules for organising partnerships (private/public data collaboratives) and for setting precise standards and norms for data collection, dissemination and analysis. In digitalisation, the nine. Beside the 10 UNSD fundamental principles of official statistics (UN Fundamental Principles of Official Statistics.), also digital principles (www.digitalprinciples.org) should always be observed.
- Technology. Technology is essential for using open data, however not everyone has access to the necessary IT equipment (PC, connected telephones and other), network (broadband, telephone network), data archiving and storage. Vulnerable groups are more likely to be left behind, in some cases women don’t have access to mobile devices and fundamental human rights might be at stake with easier access for repressive governments to personal information.
- Database/ Data Mining/ Artificial Intelligence/ Tools: Data are digitalised and ideally stored in a central database. This usually involves working with digital applications and database solutions that provide interfaces for storage, and can then be used for validation, further processing and analysis. To increase the compatibility of different data sources (data repositories, databases) and collection methods, structured data should ideally be standardised with respect to format (syntax) and sense (semantics). Standardised data can far more readily be integrated, used in interoperable systems and re-used, which makes SDG monitoring much more efficient. There are already many open standards for exchange formats and access to external applications (application programming interfaces – APIs). Relevant data sources that can be used for data collection/analysis need to be identified. One challenge is the availability of digital data and their machine-readability (e.g. content on digital photos and scanned forms are not machine-readable). To analyse the data, they are accessed on the central database in order to generate findings (data mining). Various algorithms and artificial intelligence are used here. This results in reports and systematic analyses in the form of tables, diagrams, heat maps, other maps, etc. It is also possible to use business intelligence (BI) tools that can be connected to the database and, depending on the findings sought, can convert the data into relevant findings (offering the possibility of multivariate analysis where there are interdependent variables – e.g. effects of safe drinking water on health). The results of the analyses can themselves be used as a data source in the core process of data collection and processing, and as a basis for formulating proxy indicators.
- Capacity development. The development of capacities must be directed to the use of and the access to open data. However, and in order to protect sources and users, it is crucial to also invest in building a strong sense of responsibility among the data owners and users. Advocacy and communication are essential with the producers/owners of open data but also with the users to make sure that the benefits and risks are well understood and internalised in their respective behaviours and practices.
- Data donation is also an important aspect in the development of digitalisation. The private sector is a large producer of data. This kind of data may have a great value for analysing people’s behaviours and well-being and constitutes the basis for developing targeted actions and programmes to their benefit. There are recent examples of big companies making data and skills donations to developing countries for better deploying public social services such as Orange in Senegal or MasterCard in Kenya.
Data production, collection, use, analysis and dissemination are enhanced by digital means.
Contents / Outputs
- Legislation on data protection (GDPR)/consumer protection/ online rules to regulate which data can be collected and stored by the state or citizens (data protection)/ regulations of who owns data, and how data can be captured, stored and after a certain period deleted (data ownership, data life cycle management) are in place.
- Data is available in digital form: A central database on a data storage medium is available that facilitates further processing
- Innovative digital data collection methods/ tools address data gaps in official statistics (use of non-official statistics): Big Data, Citizen Generated Data, Real Time Data, Open Data
Possible Activities & Good Practices
- Data gap analysis: Relevant data sources that can be used for data collection/analysis need to be identified
- Plans for making analogue data machine-readable (e.g. content on digital photos and scanned forms are not machine-readable)
- Advisory services on establishing central databases
- Development of Digital Tools for Data Collection/ Analysis/ Dissemination with Design Thinking/ user centric approaches
- Mapping the local systems and protocols, collaboration and engagement with local actors, available technologies, etc.
- Setting up data collaboratives with the private sector
Links with other elements of the process landscape
Links with Steering processes: A strong and comprehensive legal framework is required to guarantee the protection of individual rights, data security and confidentiality. Quality standards must be applied to all processes of digitalisation (against the 9 principles above). Budget and funding is required.
Links with Core processes: data mapping for the SDGs will necessarily call for using open data and other non-statistical sources; it is important to communicate on benefits and risks of digitalisation. The digital revolution calls for adjusting methods and tools for the production, the dissemination and the use of data.
Links with other Supporting processes: Dealing with open and big data will require new competences in the NSOs (mathematicians in particular) but also in other institutions involved. Communication and advocacy will help promoting benefits and discussing risks of digitalisation.
Quality standards and references
National actors involved
Agency/ Ministry/ Secretariat for Digitalisation
Data Providers from the public sector (Space agencies (NASA, ESA)
postal service, telecommunications providers, armed forces, police force (criminal statistics, GAF/remote sensing weather services)
Data Providers from the Private Sector: Social media (Facebook, Twitter),
telecommunications providers, Digital Globe Gallup (polling institution), Insurers
rail companies/airlines, logistics companies
Academia and research institutions