The OECD Going Digital Measurement Roadmap 2026

The OECD Going Digital Measurement Roadmap 2026

Measuring digital transformation is a key component of designing and implementing evidence-based policies. Yet measuring the digital parts of the economy is complex, in part because digital technologies and data are everywhere to some extent, rendering the notion of a siloed “digital economy” obsolete. Key challenges to measuring digital transformation include improving the international comparability of priority indicators and ensuring that statistical systems are flexible and responsive to the introduction of new and rapidly evolving concepts driven by digital technologies and data. Looking ahead, the challenge for the statistical community is to design new and interdisciplinary approaches to data collection and analysis, and to strengthen data infrastructure capabilities. Moreover, partnerships with the private sector and engagement with stakeholders to bring reliable and representative data that is gathered with trust into the policymaking process is an important overarching objective.

To address these challenges, it is important to not only identify common priorities (i.e. what to measure) but also common approaches (i.e. how to measure). The OECD Going Digital Measurement Roadmap 2026 (the Roadmap) aims to support and encourage a co-ordinated approach to digital measurement activities among key actors in the international statistical system. It includes ten actions aimed at advancing the capacity of countries to monitor digital transformation and its impacts. The Roadmap reflects a recognition that national statistical systems need to adapt and expand to adequately reflect the digitalisation of our economies and societies, with disaggregated data providing an evidence base from which to identify where digital divides exist and those who are most at risk from the disruption technological change brings. It also highlights the need for new, complementary data infrastructures capable of monitoring digital activities and data flows on a timely basis wherever they happen. The ten actions are outlined below.

Action 1: Make digital transformation more visible in economic statistics

Measuring digital transformation and its impacts requires the development of indicators that complement traditional measurement frameworks, such as those used to measure gross domestic product (GDP) and other macroeconomic variables in the system of national accounts. But even within these current frameworks, the way that firms, products and transactions are classified and identified requires adaptation. In addition, it is essential to accelerate efforts to capture relevant phenomena outside of the current production boundary of the national accounts, for example those concerning the consumption (and value) of online services provided to consumers at zero price, such as free apps, and services provided by online search and social networking sites.

At the same time, tackling the challenges of globalisation and the measurement of services not physically fixed to a single location (e.g. cloud services and services provided by online platforms) should be further encouraged. Online platforms in particular raise new policy and measurement challenges, as it is not always clear the full extent of their operations, the types of activities in which they engage, the services they provide, the value they create and the locations from which they operate. In addition, by providing easy access to customers for transport services, accommodation, food delivery and many others, online platforms are increasing the importance of household production, blurring the lines between different institutional sectors within the economy and changing the nature of work.

National statistical offices (NSOs), research communities and international organisations are encouraged to continue to work together to:

• Produce digital supply-use tables using the OECD Handbook on Compiling Digital Supply and Use Tables (OECD, 2023[1]), in particular for those areas that supplement national accounts, to obtain new information and perspectives. For the latter, it would also be important to develop further guidance to assist countries in compiling the relevant estimates.

• Better capture digitally enabled production by households and continue to develop estimates of unpaid household activities in economic statistics and tackle the challenge of understanding and estimating the value generated by services provided to users free of charge (though often involving an implicit transaction related to personal data).

• Support the development of common definitions and taxonomies of different types of online platforms, formulate standard questions on platform work for inclusion in relevant surveys (e.g. labour force, information and communication technology (ICT) usage and time-use surveys) to derive robust estimates of the numbers of platform workers, and explore the role of administrative data and alternative data sources (e.g. web-scraped data) to gain insights into platform-intermediated transactions.

• Develop taxonomies or principles to assess digital intensity across sectors to inform an understanding the sectoral heterogeneity of digital transformation and its relationship with various aspects of production.

Action 2: Enhance data valuation and measurement of data for effective decision-making

As data continues to drive economic growth and innovation, its valuation and measurement becomes increasingly crucial. The increasing adoption of digital technologies like cloud computing and artificial intelligence (AI) have made data a core input for modern business models, yet its unique properties – non-rivalry, context dependence, and the ease of flow across borders – complicate its measurement as an economic asset. Much of data’s value is not traded, leading to measurement challenges as this often implies making assumptions and imputations. While the sum-of-cost approach offers promise for valuing firm-generated data and is widely used in economic statistics, it requires a measure of all of the inputs that contribute to the production of data and may not well capture the multiple ways data are used and exchanged. It is also highly sensitive to the different assumptions made regarding the markup and the prices/wages associated with these inputs. Further work is needed to elaborate a robust approach that could lead to high-quality reliable estimates of the value of data.

The 2025 System of National Accounts (SNA) recognises data as an economic asset (UN et al., 2025[2]). This inclusion is an important step towards bridging existing gaps in the valuation and measurement of data, providing a clearer framework for capturing its economic contribution. However, measuring cross-border data flows is particularly challenging, especially in an evolving environment of data localisation, privacy concerns, and emerging data governance frameworks. Addressing these multifaced challenges will require not only refined statistical methods, new data collection or the use of alternative data, but also strengthened international co-operation and the development of guidelines that consider the impacts of data. By enhancing data valuation, we can better measure its contribution to productivity growth, innovation, and competitiveness, demands on energy requirements and the environment, and concerns about data privacy and security.

The statistical, business and research communities, and international organisations are encouraged to work together to:

• Ensure sufficient effort and resources are directed towards developing harmonised taxonomies, classifications, and guidance to better capture data-related products and services in economic statistics. Additional work to further improve the methods should be conducted.

• Refine measurement methodologies for cross-border data flows employing advanced econometric approaches, machine learning techniques and dedicated surveys.

• Improve the sum-of-cost approach for valuing firm-generated data to ensure that the value of data assets is accurate, particularly when data assets are combined. Complement the sum-of-cost approach with other approaches that take into account the value of data across networks and their unique properties in certain sectors (e.g. the health sector).

• Foster international co-operation to address multidisciplinary aspects of data valuation and governance across borders and sectors.

• Develop metrics to measure the impacts of existing practices and policies to promote public sector data sharing, access and re-use, including with regard to citizens’ trust in government to adequately protect personal data.

Action 3: Capture cross-border and domestic trade from a digital perspective

Digital technologies have profoundly reshaped the trade landscape, revolutionising the exchange of goods and services both domestically and across borders. Recent technological advancements have significantly increased e-commerce and digital trade by empowering a broader range of actors, including small firms and individual entrepreneurs, to actively engage in commercial activities both domestically and across borders. Moreover, the increasing prominence of online platforms, such as digital intermediation platforms (DIPs), has been pivotal in facilitating these developments, connecting more buyers and sellers in new ways and facilitating access to foreign markets for households and small firms.

To fully grasp the implications of this transformation, it is essential to enhance the measurement frameworks that govern these activities, ensuring they remain aligned with the evolving dynamics of a highly digitalised economy. This requires a re-evaluation of existing standards and definitions, the development of estimates of both the scale and the value of digital trade, and the identification of the unique characteristics inherent in electronic transactions. By refining data collection methodologies through enhanced surveys and, possibly, the incorporation of alternative data sources, the statistical community can effectively capture the intricate dynamics of these transactions, thereby equipping policymakers with the insights necessary to navigate and harness the potential of this rapidly evolving environment.

NSOs, research communities and international organisations are encouraged to continue to work together to:

• Review and potentially finetune statistical standards, definitions and guidelines related to trade in the digital age, in light of the OECD definition of e-commerce and related guidance (OECD, 2025[3]), to adapt to new developments.

• Improve data collection on the value of total e-commerce transactions that distinguishes between domestic and international sales through enhancements to surveys of ICT use by businesses and individuals. The OECD could support this effort by working with the international statistical community to develop a model “digital trade module” for inclusion in ICT Access and Usage surveys.

• Incorporate e-commerce questions into other appropriate surveys (particularly e-commerce revenues in structural business surveys and online spending in household expenditure surveys) as well as the use of alternative data (e.g. anonymised information on transactions from banks and credit card companies).

• Use the Handbook on Measuring Digital Trade (IMF et al., 2023[4]) as a guide to develop estimates of digital trade. This will involve identifying transactions based on their “digital nature” (i.e. digitally ordered and/or digitally delivered) and new actors relevant to trade in the digital age (e.g. DIPs, e-tailers and firms dependent on online platforms).

• Develop indicators to measure the contribution of foreign direct investment (FDI), including via multinational enterprises (MNEs), to “digital” economic activity, with a focus on the role of MNEs as catalysts for digital transformation, highlighting their contributions to communications and services infrastructure, e-commerce, and technological capacity.

Action 4: Measure key digital technologies, notably artificial intelligence

Technological breakthroughs, including data-dependent technologies like big data analytics and AI, are reshaping the technology landscape, bringing productivity enhancements and convenience, but also risks. Other key developments include Internet of Things (IoT) technologies, cloud computing, next generation wireless networks (e.g. 5G and beyond), distributed ledger technologies (e.g. blockchain), immersive technologies and quantum computing. Alone, each of these technologies is powerful, bringing opportunities to improve efficiency and increase consumer choice. These applications affect a wide range of sectors, including health, education, agriculture, transportation, manufacturing, and energy, among others. They also have potential to revolutionise production as well as contribute to tackling global challenges, such as those related to health. At the same time, these technologies have downsides, such as risks to security and privacy. The general purpose and interdisciplinary nature of these digital technologies underscore the need for a consistent framework to define them, identify their emergence, monitor their development and diffusion, and quantify their positive and negative economic and societal impacts.

Policymakers, regulatory authorities, business, statistical and research communities are encouraged to:

• Develop internationally harmonised statistical definitions, taxonomies and methodologies for measuring AI, quantum and other key digital technologies and their impacts, fit for the purpose of monitoring the development of these technologies and their applications across countries and sectors, including defining the key policy needs for measurement.

• Create commonly accepted taxonomies and methodologies to assess AI innovation and other key technologies using patent statistics, data on scientific production, and other sources in line with related OECD methodologies and standards such as those for research and development (R&D) and innovation more broadly. Ensure that these standards are periodically reviewed and updated, given the rapid evolution that characterises these cutting-edge technologies.

• Build on the OECD definition of IoT (OECD, 2018[5]) and related taxonomies for its application (e.g. massive machine communications such as sensors for smart cities and critical IoT requiring ultra-fast and highly reliable connections such as automated vehicles). Provide clear prioritisation for the measurement of IoT elements and indicators of most relevance to policymakers, beyond simple counts of machine-to-machine connected devices, to measure the potential demands IoT might create for communication infrastructures due to the flow of large amounts of data generated.

• Engage with stakeholders within the IoT ecosystem (e.g. different connectivity providers, IoT platform providers, etc.) for the benefit of data collection and policy and regulatory analysis.

• Develop tools to monitor the adoption of IoT, AI and other key technologies by businesses and governments and the impact of their diffusion on performance and productivity.

• Define metrics to assess the effect of digital technologies on driving more efficient, inclusive and tailored public service delivery.

Action 5: Understand the economic impacts of ICTs

Digital technologies are incorporated as a part of business processes, and together with labour, capital and knowledge-based capital assets, they help drive growth. The initial and strongest evidence of the economic impact of digital technologies will likely manifest itself in microdata (data about firms, workers or consumers) before showing up in macrodata. To this end, it is important to be able to link together existing datasets, exploit the potential of administrative records, and develop measures of digital maturity in business that can then be used to analyse the impacts of digital technologies on firm performance. Robust measures of changes in prices and quality are also crucial to analysing the contribution of digital technologies to economic performance. For example, measures of broadband quality are critical for consumers to make informed choices, and for policymakers and regulators alike to ensure well-functioning and competitive markets. However, they are also key to measuring productivity and assessing the contribution of ICTs to economic growth. Digitalisation may also further complicate the measurement of prices and volumes more generally, as it increases the pace of quality change, leads to changes in the outlets through which products are sold, and may involve new price differentiation practices, among others.

Quality of service provision should also be considered in the context of digital divides, such as between businesses of different sizes, or households with different compositions, incomes or locations. To this end, business and household surveys that measure the adoption of digital technologies should continue to be reviewed regularly to fully account for emerging phenomena, such as high-speed broadband, cloud computing services, data assets and other technologies, both as enablers of innovation and as contributors to business performance and consumer choice. At the same time, opportunities to further capitalise on administrative data by linking existing datasets should continue to be exploited. In addition, surveys of technology adoption and administrative data need to be aligned with aggregate economic measures.

The international statistical community is encouraged to:

• Improve the measurement of granular indicators on broadband availability (e.g. coverage and subscriptions) and performance (quality) at a subnational level to inform end user choice including experienced speeds, latency, and reliability of broadband services across geographical areas. A standardised broadband map with harmonised spatial definitions at the subnational level would help monitor broadband availability and performance.

• Provide breakdowns of key data by firm size to assess market concentration and the implications for small and medium sized enterprises (SMEs).

• Regularly review the framework for measuring ICT usage to identify and prioritise areas in which surveys can improve and evolve in line with ongoing developments and policy priorities; this includes delivering sufficiently granular detail for the differentiated analysis of impacts of digital technologies on individuals, firms and places.

• Better measure the economic impacts of ICTs by focusing on the link between microdata and macrodata.

• Exploit the statistical potential of administrative data sources and review existing data collections to maximise data-linking opportunities for research. Improve access to these datasets while ensuring data confidentiality.

• Improve the measurement of ICT investment to arrive at internationally comparable deflators for hardware, software and communication infrastructure, including the pricing of broadband services bundles, and analyse the impacts and opportunities digital technologies offer in relation to the measurement of prices and volumes more generally.

• Align OECD Members’ priorities for collecting sex-disaggregated statistics using common measurement methodologies and approaches. Encourage the collection of sex-disaggregated statistics in official surveys as well as other data collection approaches and make that data easily accessible to support evidence-based policymaking.

Action 6. Encourage measurement of the impact of digital technologies on the well-being of individuals and society

Digital technologies impact many aspects of people’s lives and society more broadly. These impacts can be both positive and negative, and striking a balance to ensure more opportunities than risks is crucial. Accordingly, measurement frameworks play a key role in understanding the extent to which digital technologies and new business models can help address societal goals, including those associated with health and ageing populations and resiliency of the environment, and to fostering individual well-being. Well-being measures have been developed in response to the need for better metrics of societal progress, its distribution across the population, or its sustainability over time, also taking into account how people feel about and experience their own lives. Statistical frameworks on well-being such as the OECD Well-being Framework, cover a broad range of issues, including but not limited to a consideration of digital technologies and their impacts, and tend to leverage a combination of data on material and non-material people’s life conditions (e.g. health, income, wealth, housing, social connections and subjective assessments of their emotional state or degree of life satisfaction).

Today, evidence of the impacts of digital technologies on well-being is limited in many areas. For example, relevant data disaggregated by socio-demographic and socio-economic groups to analyse how the use of digital technologies and online behaviours affect the mental health of individuals, including children and people of all ages, is not collected frequently or in a harmonised manner. Such data is important to determining those groups who are most at risk. Survey instruments are an important source of self-reported objective and subjective data, and they can be used to collect data on people’s life experiences in a digitalised society, as well as to attempt to establish causal relationships (e.g. between the diffusion of digital technologies and various well-being outcomes). Digital devices also offer valuable real-time insights into individual’s daily usage patterns and responses to technological advancements.

The international statistical community is encouraged to:

• Assess the feasibility of adding modules to the OECD Model Survey on ICT Access and Usage by Households and Individuals (OECD, 2015[6]) to develop harmonised subjective well-being indicators (Hatem and Ker, 2021[7]).

• Include detailed ICT use variables in household surveys (e.g. general social surveys and labour force surveys), and especially in longitudinal surveys, to better understand the causal relationships between Internet use and well-being outcomes over time.

• Use the OECD Well-being Framework (OECD, 2020[8]) and well-being dashboards (e.g. the OECD Going Digital Toolkit and the OECD Digital Well-being Hub) to monitor progress on well-being indicators.

• Forge partnerships with key stakeholders to develop new statistical tools including surveys to monitor exposure to and the impact of ICT use on children and young people in digital environments, including on mental health outcomes. Develop internationally agreed and standardised measures of children and young people’s digital engagement with both the risks and opportunities of digital technologies to facilitate comparative analysis across countries and regions.

• Develop new statistical tools, including surveys, to monitor the incidence of technology-facilitated violence against women and girls, using approaches such as fostering collaboration among related advisory boards, committees and rights organisation. This includes the dissemination and exploitation of non-consensual intimate content, cyberviolence targeting women engaged in public activities, as well as risks to women and girls associated with emerging technologies and algorithms.

• Forge partnerships to develop new statistical tools, including surveys, to map and evaluate the risks and opportunities for people with disabilities associated with emerging technologies and algorithms.

• Improve measurement of the uptake and impacts of digital technologies, including by enhancing statistical linkages among ICT use surveys, consumer expenditure surveys, supply-use tables and industry-level data.

Action 7: Define and measure skill needs to thrive in digital economies and societies

Advances in the digital technology ecosystem increase the demand for certain skills that are often in short supply. At work, a shortage of ICT specialists may be compounded by managerial obstacles to the development of new business models, new organisational structures and new working methods. At the same time, demand is rising for complementary skills, such as the capability to compile and analyse information, communicate on social networks, brand products on e-commerce platforms and so on. Traditionally, official statistics have used educational attainment, vocational training with standardised content, or occupational categories with codified and predictable tasks as a proxy for skills. New insights could be gained by exploiting and harmonising detailed national surveys on tasks and skills and by working with the business community to define new metrics of skill shortages to meet future labour market demand.

The statistical, business and research communities, and international organisations are encouraged to work together to:

• Exploit the potential of existing public and private statistics on skills, including disaggregated by sex, age, and at the subnational level, together with occupation and industry classifications, and promote the harmonisation of national job tasks surveys. Particular consideration should be given to older employees, focusing on the impact of digital technologies on their skills, identifying gaps, and determining the necessary training to help them adapt to a digitalised economy.

• Better utilise existing cross-country surveys (e.g. the European Survey of Working Conditions and the OECD Survey of Adult Skills (PIAAC)) and promote the linking of employer-employee datasets containing information on skills, jobs and activities at the individual level while addressing and bridging digital divides.

• Improve access to and the use of online vacancy datasets to measure vacancies in digital-related jobs, their duration and rate of filling.

• Encourage the systematic use of expert assessment to identify emerging skills needs at a detailed level of tasks and occupations, and across countries and regions.

• Develop comprehensive datasets that capture telework trends, job quality indicators, and the digital skills premium to inform analysis and policymaking.

• Develop measures and collect data on young people’s digital literacy and competences to thrive in the digital world, including through existing surveys such as the OECD Programme for International School Assessment (PISA).

• Develop metrics to capture the digital transformation of educational institutions, including by tracking the development and implementation of online learning capabilities at both secondary and tertiary levels.

Action 8: Measure trust in digital environments

To fully benefit from the opportunities of the digital transformation, individuals, firms and governments need to trust that engaging in digital environments will bring more advantages than disadvantages. As people increasingly use a wide variety of online services, firms become more reliant on digital technologies. At the same time, malicious and adversarial actors are increasingly sophisticated, leading to a rapidly evolving threat landscape. Ensuring security, privacy and consumer protection online, as well as the general level of trust of the population in digital environments, have become key policy issues as individuals, businesses and governments shift large parts of their daily activities to the Internet. Moreover, efforts to address security risks should avoid, wherever possible, creating mechanisms that impose centralised control over information or suppress freedom of expression and the open exchange of ideas. Despite the broadly acknowledged importance of trust between partners in online exchanges, measurement of these aspects of trust is not a longstanding practice, especially within official statistics.

Trust in the information ecosystem is another issue that has risen in tandem with the Internet becoming an important information source. The creation and dissemination of deliberately manipulated and misleading content online poses risks to people and society and reduces trust. Efforts to address these challenges should prioritise the protection of freedom of expression, ensuring transparency, and targeting specific threats. Indicators could focus on identifying specific adversarial propaganda campaigns, understanding their origins and provenance, and analysing their dissemination methods. A co-ordinated measurement agenda developed and implemented in a cross-country comparable way and in partnership with the private sector is important in this respect. In addition, indicators focused on better understanding the policy landscape and changes over time will help create a baseline for comparative analysis against which countries can assess their actions. Analysis should focus on expanding access to credible information, exposing adversarial actions, and promoting the open exchange of ideas to foster trust in digital environments.

The statistical community, regulators and other stakeholders, such as Internet intermediaries, business and consumer associations, and international organisations are invited to work together to:

• Complement official statistics by exploring alternative data sources and innovative techniques to develop cross-country comparable metrics for monitoring public perceptions of uncertainty related to cybersecurity risks to identify emerging threats, including from malign State actors, and potentially predict changes in key indicators like ICT investment.

• Measure cybersecurity innovation by developing harmonised taxonomies and methodologies in partnership with the cybersecurity experts and leveraging patents statistics and data on cybersecurity-related scientific publications.

• Develop a reliable and comprehensive dataset on digital security incidents and digital risk management practices, key elements of which include reaching a consensus on typology and taxonomy, the creation of a trusted public-private digital security incident repository, and incentives to promote the reporting of incidents and data sharing by organisations and private entities, including insurance companies.

• Promote common methodologies to measure privacy in digital environments in partnership with stakeholders in the privacy community, including privacy enforcement authorities, by focusing on aspects such as data transparency, control, and the adoption of privacy enhancing technologies (PETs).

• Build on the methodology developed in the OECD Truth Quest survey (OECD, 2024[9]) to generate more insights into cross-country differences in the ability of individuals to identify the veracity of information online, as well as differences across socio-demographic and socio-economic groups. Assess the effects of generative AI on the creation and propagation of “false and misleading content online” and their impacts on individuals and society, with a particular focus on women.

• Map the circulation of adversarial propaganda and illegal content online, such as by developing metrics of the number of removals of illegal content, to enhance insights into the trustworthiness and safety of online platforms.

• Further study consumer attitudes and behaviour to highlight contexts in which trust in online interactions increases or decreases, with a view to improving survey methodologies in this area.

• Develop specialised behavioural experiments and consumer surveys to assess the financial and non-financial benefits and harms arising from consumers’ engagement in digitally enabled commercial transactions.

• Monitor policies on information integrity to develop metrics to assess the implementation of the public policy framework outlined in the Recommendation on Information Integrity (OECD, 2024[10]), as a baseline for understanding public policy responses in this space.

Action 9: Design new and interdisciplinary approaches to timely data collection

Given the fast pace of technological change, it is understandable that current measurement frameworks are not yet able to reveal the full magnitude and scope of digital transformation and its impacts. However, digital technologies can be part of the solution as they generate enormous flows of information. Numerous online actions leave digital “footprints” that can be observed using tools that scan, interpret, filter, gather and organise information from across the Internet. While they offer great opportunities for statistics, Internet-based data also raise several issues regarding statistical quality, security and privacy that must be addressed.

The Internet also enables the creation of non-physical organisations and the flexible outsourcing of business activities, within sectors of activity and across locations, thus blurring the boundaries between firms and markets and between work and social life. This creates challenges for current approaches for collecting statistics. New interdisciplinary methods of analysis are therefore necessary to understand innovative behaviour, its determinants and its impacts at the level of the individual and the organisation. The pace of technological advances raises the stakes for the compilation of timely data. However, there remains a shortage of timely and comparable data on the growth of the “digital” parts of the economy. Real-time indicators, utilising innovative methods and alternative data, have proven valuable in complementing official statistics and should be employed to measure the scale and value of the “digital economy”.

NSOs, regulators, Internet service providers (ISPs), the research community, the Internet community and international organisations are invited to work together to:

• Further develop international statistical standards for the collection of Internet-based data and their compilation into statistical indicators (e.g. treatment of web search results). Work collectively to develop approaches to better understanding and interpreting such indicators.

• Assess alternative models of co-operation among businesses, Internet intermediaries and NSOs for the collection and treatment of Internet-based data; and promote the development of an associated regulatory framework, including technical and regulatory solutions, to preserve user security and privacy.

• Develop interdisciplinary approaches to data collection and new units of data collection, and develop approaches to robust non-probability sampling, and use innovative methodologies that integrate traditional data sources (such as surveys and administrative data) with non-traditional approaches, including the use of satellite imagery and geo-referenced databases.

• Improve the measurement of digital activities in complex business structures, organisations and networks.

• Leverage big data and machine learning techniques to produce timely and comparable estimates of growth in key sectors underpinning digital transformation, thereby complementing official statistics.

Action 10. Strengthen data infrastructure capabilities

While data collection is critical for measurement, high-quality data infrastructures are needed to store, display, combine and analyse data. Data infrastructure includes datasets, technology, skills and processes that makes data useable. High-performance computing plays a critical role in digital transformation, as it enables advanced analytics and AI applications by supporting large-scale simulations, machine learning, and complex modelling. It also underpins research and innovation ecosystems, which are essential for universities, research institutions, and digital government initiatives. Robust data infrastructure facilitates the blending of data from multiple data sources and the use of innovative designs, methods, technologies and capabilities to create new insights from existing data, including through data visualisation which helps individuals understand data. Data infrastructure should be designed and supported to benefit as many stakeholders as possible, and it should be open and easy to use for statisticians and policymakers alike.

NSOs and international organisations are invited to:

• Develop and maintain innovative data infrastructures capable of capturing real-time digital activities and data flows.

• Improve data visualisation tools and capabilities and ensure that such tools allow for the combination of datasets across policy domains to yield new insights with existing data.

• Create and regularly update taxonomies, ontologies and semantics through a collaborative process among organisations to ensure harmonised and up-to-date definitions that maintain the interoperability and quality of datasets and data infrastructures.

• Incorporate long-term strategies for maintaining data infrastructure and retaining skilled personnel, including mechanisms for continuous training, career development, and incentives to ensure the sustainability of capacity-building efforts.

• Develop frameworks to measure the availability and distribution of critical digital infrastructure, such as data centres, cloud computing services, and high-performance computing.