5.7 Possible ways forward

5.7 Possible ways forward#

So far, this chapter has explored the different layers of national data ecosystems, considered the need for strategies for engagement with that ecosystem, and looked at some of the potential benefits and challenges of different types of engagement. This final section briefly considers how a NSO might start to engage with the national data ecosystem in a step-by-step way:

Vision and strategy

The essential precondition to successful engagement with the national data ecosystem is for the NSO to set out a clear vision of what it wants to achieve, and a strategy of how to achieve it. This vison and strategy need to be developed together with policymakers and other stakeholders, and take into account broader government visions for data.
Review legal frameworks, institutional arrangements and mandates

The NSO should check whether the strategy is consistent with existing statistical and other relevant legislation, as well as in line with existing institutional arrangements. In some cases, legislation may need to be amended. Sensitive data may be subject to data protection legislation, and developing a good relationship with the agency responsible for managing data protection (if such an agency exists), will be essential. Each player in the national data ecosystem has a specific mandate. It is therefore necessary to understand the mandates and constraints of potential partners, and to try to find ways to engage that do not conflict with those mandates and constraints.
NSO mandate and organizational set-up

The NSO must work towards and eventually obtain a clear mandate for the role that it envisions. It should identify and implement the necessary organizational changes, which may require support from policymakers.
Acquire skills and competencies

The NSO will need to train or recruit staff so that they have the necessary skills and competences to successfully implement the strategy. This typically involves enhancing competencies in soft skills such as communication and negotiation, though specific skills in areas such as data protection legislation may also be needed.
Establish technical infrastructures and standards to facilitate interoperability

The NSO should ensure it has the necessary technological and technical infrastructures and capabilities to engage effectively with other players in the national data ecosystem. This may include, but is not limited to, secure servers, database systems and systems to manage staff access to sensitive or confidential data. The NSO should also be actively involved in the specification of any cross-government technical infrastructures, and standards, to try to ensure that these are as easy as possible for the NSO to use.
Develop new mindsets on data integration and sharing

The NSO needs to foster an internal culture that supports the use of non-statistical data sources, promotes data integration, and encourages data sharing (within the limits of statistical confidentiality). This is often a considerable culture change for NSOs that have traditionally focused on collecting data through statistical surveys and censuses, and have been accustomed to operating in statistical subject-matter stovepipes.
Outreach, transparency and communication

To be a trusted partner in the national data ecosystem, national statistical offices need to develop a reputation for transparency and open, effective communications. This is essential to maintain good relations with partners, as well as to ensure acceptance of the NSO role by all stakeholders, including the general public. How far public opinion is likely to welcome, or at least tolerate, data sharing within the national data ecosystem, will vary considerably between countries, depending on the national context.

Any data ecosystem needs some level of governance, including organisations that will take on data stewardship roles. However, too much control and regulation of a national data ecosystem can risk limiting innovation, whereas not enough can risk inefficiency, or even data breaches. There is a need to find the correct balance. It will rarely be the responsibility of the NSO alone to find this balance, however, the NSO representatives in any discussions and negotiations with actual or potential partners, must always bear in mind the need to find a suitable balance.

The United Nations Statistical Commission, a functional commission of the Economic and Social Council (the Council), decided at its 56th Session to establish a working mechanism on data governance(🔗). The mechanism is expected to propose a way forward to address the challenges of national statistical systems in response to growing innovation and new data ecosystems.

Box 1: Case studies of engagement with national data ecosystems

Cameroon: The national statistical system is decentralized, and as of 2020, it is governed by a dedicated legal structure that regulates statistical activity in the country. [1] Cameroon has been actively developing advanced legislation regarding data governance and has made substantial recent changes. A new law relevant to NSS activities includes the 2024 law organizing the Civil Registration system. [2] This law modernizes the Cameroon civil registration governance ecosystem and ensures paper and digital records are robust and have accountability mechanisms to ensure fairness and completeness. Cameroon has also passed comprehensive data protection legislation as of 2024. [3] The data protection authorities are tasked with working across agencies to enforce the law and cooperate on data governance. Consent requirements and other aspects of the data protection law will likely require cooperation with NSS at various points and may require additional consideration to procedural or administrative adjustments to workflows, depending on context.

China, Hong Kong SAR: The Census and Statistics Department (C&SD) has broad responsibility for most general-purpose statistics. The C&SD has statistical units established in 33 government bureaus and departments, which forms the Government Statistical Service (GSS), a network of government statistical services crossing sectors. The Commissioner for Census and Statistics leads the C&SD and the GSS has responsibilities for establishing, coordinating, and monitoring the work of field. The C&SD and GSS is governed by the Census and Statistics Ordinance, which specifies that the Commissioner may promulgate implementing regulations when necessary. The ability for the Commissioner to create updates responsive to emergent challenges and risks provides the C&SD with an enhanced ability to harmonize within a complex and evolving regulatory environment. One such environment is the evolving data protection legal and compliance landscape. Hong Kong SAR’s privacy law is similar to but does not mirror Europe’s GDPR in all respects, which can create complex points of friction between government agencies and the private sector regarding data norms. The cooperation between government entities with differing data missions is particularly important to address these frictions, as well as some degree of dialogue and cooperation between the private sector and the government regarding differing norms and legal workarounds.

Chile: The National Statistical Agency, the Instituto Nacional de Estadísticas (INE) has a National Directorate [4] and is headquartered in Santiago with 15 regional documentation centers. It is functionally decentralized, operating as a “technical autonomous agency”. Currently there is no mandate to ensure that the INE is the repository of the various official statistics generated by the various state bodies in charge of official statistics. In the absence of the formal legal update, the INE has worked through extensive intersectoral coordination to seek to regulate the production of state statistics. The INE’s role in assisting to disseminate basic standards for the information being shared between institutions will continue to remain important. Updated legislation that further enshrines best practices and allocates funding for INE’s efforts in this area will likely become increasingly important, however. This is especially so given that in December 2024, Chile passed its new Personal Data Protection law (Law 21.719). [5]

Thailand: Thailand has created a goal-oriented, government-wide effort toward increasing digitalization and the use of data. A feature of this effort is its decentralized statistical system with the National Statistical Office as the central statistical unit of the country. Thailand’s NSO has a detailed set of regulations, ethics, and a Masterplan to effectuate the government’s digital development efforts. [6] To accomplish its aims, Thailand’s statistical data architecture extends data functions across ministries and departments to ensure that a range of government actors are focused on varied aspects of data creation, gathering, digitalization, analysis, and use. Other prominent actors include the Thailand National Cybersecurity Agency which has dedicated legislation, [7] the Data Protection Authority which has comprehensive dedicated legislation, [8] and other government entities such as the Government Big Data Institute (GBDI), [9] which is specifically responsible for Big Data. The Ministry of Digital Economy and Society (MDES) [10] supervises multiple departments as well as the Thai AI strategy and action plan (2022 -2027), [11] with a budget allocated across 68 agency departments.

New Zealand: Statistics New Zealand has become one of the first NSOs to fully embrace a leadership role in the national data ecosystem. The Chief Executive of Statistics New Zealand is also the Government Chief Data Steward. This is described in more detail (🔗).

Lithuania: In addition to producing official statistics, Statistics Lithuania has been given the role of State Data Agency, with responsibility for governance of “state data”, i.e the public sector layer of the national data ecosystem. See (🔗) (pages 16-19).

Ireland: The Central Statistics Office of Ireland has been very active in recent years to develop strategic partnerships with other public sector bodies in Ireland, in the context of building a “national data infrastructure”. A key partnership is with Ordnance Survey Ireland, the national mapping and cartographic agency. This partnership has resulted in several joint data products, and was leveraged during the COVID-19 pandemic to quickly produce urgently needed data and dashboards for monitoring and managing the pandemic. See (🔗) and (🔗).

Indonesia: The government is committed to leveraging digital technology to bring greater social cohesion, support a digital economy and provide better public services, through its One Data Policy Initiative. This policy aims to make government data more timely, accurate, accountable, and accessible. It also aims to strengthen data sharing and integration arrangement between government institutions and to make better use of government data and official statistics for policy making. The policy is envisaged to become a governance framework that would allow the development of integrated and interoperable data platforms across central and local government through a common standard. See (🔗).

Philippines: The government is committed to digital transformation, leveraging technology to enhance public services, streamline governance, and foster economic growth. The Philippine Statistics Authority’s vision is to be a solid, responsive, and world-class authority on quality statistics, efficient civil registration, and an inclusive identification system. It is responsible for coordinating government-wide programs governing the production of official statistics, general purpose statistics, civil registration services and an inclusive identification system. See (🔗).

Uzbekistan: The Statistics Agency under the President of the Republic of Uzbekistan (UZStat) has been undertaking a vigorous and productive modernization programme of their CPI since 2020. UZStat has expanded their data acquisition for the CPI from two to five data sources, including Electronic Point of Sale (EPOS or scanner) data collected in partnership with the Tax Agency of Uzbekistan, as well as web-scraped and other commercial data sources. These alternative data sources have the potential to increase accuracy, improve timeliness, allow for greater granularity, as well as reduce data collection costs and response burden when compared with using traditional sources alone. See (🔗).

Other case studies are available on UN Statistics Wiki (🔗).