Editorial for the statistical journal of the IAOS

Abstract

1. Indigenous data, human rights, and ethical statistical practice

A defining feature of this issue is its strong focus on Indigenous data and the ethical responsibilities of statistical systems. The contributions highlight a shift from viewing Indigenous populations merely as subjects of data collection toward recognizing them as rights-holders and co-producers of data.¹ Papers on Indigenous health data governance and international recommendations for national planning emphasize the need for frameworks that respect sovereignty, cultural context, and community priorities.

Equally important is the operationalization of human rights in statistical production. Moving “from principles to practice,” the authors demonstrate that embedding human rights is not an abstract commitment but a concrete methodological and institutional challenge.² The emphasis on co-design as ethical statistical practice reinforces the growing recognition that legitimacy and quality in official statistics are inseparable from inclusiveness and participation.³ This is echoed in the examination of statistical visibility of Indigenous peoples in Mexico, where measurement itself becomes a matter of equity and recognition.⁴

The discussion of data ethics, including insights drawn from situationism, further broadens the lens. It reminds us that statistical organizations must remain critically aware of the social contexts in which data are produced and used.⁵ These contributions align with broader international movements, such as the UN Fundamental Principles of Official Statistics and the increasing emphasis on data stewardship and trustworthiness in the data ecosystem.

2. Digital transformation, data governance, and the future of official statistics

Digital transformation continues to redefine the role of national statistical offices (NSOs). The case of Switzerland illustrates how official statistics are adapting to a data-rich environment, balancing traditional mandates with new expectations for timeliness, integration, and relevance.⁶ The Lithuanian experience offers a particularly forward-looking perspective, presenting the evolution from an NSO to a state data agency as a potential model for governance in the digital age.⁷

Interoperability emerges as a critical enabler of this transformation. The development of systems such as Caliper for managing statistical classifications demonstrates the importance of harmonization in an increasingly interconnected data landscape.⁸ At the same time, the changing skills profile within statistical offices—illustrated by the penetration of data science in French official statistics—highlights the human dimension of transformation.⁹ The future of official statistics will depend not only on technological infrastructure but also on the ability to cultivate multidisciplinary expertise.

These developments resonate with broader global discussions on data governance, including the integration of administrative data, private sector data, and emerging data sources into coherent and accountable systems.

3. New data sources and innovation in official statistics

Innovation in data sources is another central theme of this issue. Several papers explore the use of mobile network data, both as standalone sources and in combination with traditional surveys, to produce more granular and timely statistics.¹⁰ These studies demonstrate the potential of such data to enhance mobility analysis and inform policy, while also raising important questions about privacy, representativeness, and methodological robustness.¹¹

Advances in automation and machine learning are also evident.¹² The use of text-based similarity measures for enterprise classification and the analysis of motivations for sharing smartphone sensor data illustrate how unstructured data can be harnessed for statistical purposes.¹³ These contributions reflect a broader trend toward the integration of data science techniques into official statistics, expanding both the scope and the complexity of statistical production.

At the same time, they highlight the importance of maintaining transparency and methodological rigor, particularly when working with novel and potentially biased data sources.

4. Census, surveys, and statistical methodology

Despite the rapid evolution of data sources, traditional statistical methods remain foundational. This issue includes important contributions to census methodology, small area estimation, and sampling theory. Uruguay's 2023 combined census exemplifies the integration of administrative registers with traditional enumeration, offering a model for modern census design that balances efficiency with coverage.¹⁴

Methodological advances in small area estimation, including the integration of survey and geospatial data, demonstrate how statistical techniques are adapting to meet demands for more detailed and localized information.¹⁵ Similarly, work on nonprobability samples and inclusion probabilities addresses some of the most pressing challenges in contemporary survey methodology, particularly in the context of declining response rates and increasing reliance on alternative data sources.^16,17

The application of machine learning in health survey modeling further illustrates the convergence of traditional statistical frameworks with modern computational approaches.¹⁸ Together, these papers reinforce the importance of methodological innovation as a cornerstone of official statistics.

5. Economic, environmental, and sectoral applications

The issue concludes with applications that highlight the societal relevance of official statistics. The use of AIS data to estimate greenhouse gas emissions from shipping activities demonstrates how new data sources can contribute to environmental monitoring and climate policy.¹⁹ Similarly, the development of indicators for food price anomalies in the Philippines underscores the role of statistics in addressing economic volatility and supporting policy responses.²⁰

These applications reflect the broader mission of official statistics: to provide reliable, relevant, and timely information for decision-making in an increasingly complex world.

6. Concluding reflections

Taken together, the contributions in this issue point to a statistical system in transition. The boundaries of official statistics are expanding—across data sources, institutional roles, and ethical considerations. This transformation is not without challenges. Questions of trust, governance, quality, and inclusiveness are becoming ever more central.

Yet, the papers in this issue also demonstrate that the statistical community is actively engaging with these challenges. By embracing co-design, advancing methodological innovation, and rethinking institutional frameworks, official statistics are positioning themselves to remain a cornerstone of evidence-based policymaking.

As we move forward, it will be essential to continue fostering dialogue across disciplines, sectors, and communities. The future of official statistics will depend not only on technological advancements but also on our collective commitment to principles of integrity, inclusiveness, and public value.

This issue of the SJIAOS offers both inspiration and guidance in that journey.

Footnotes

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Ring I, Connolly M, Waldon J, et al. Indigenous health data and international recommendations for national planning and data governance. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261430897.

Willis-Núñez F and Núñez JF. From principles to practice: Embedding human rights in the production of Indigenous data and statistics. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261445836.

Tractenberg RE and Thornton S. No less than fit for purpose: Embedding co-design in ethical statistical practice for indigenous and other communities. Stat J IAOS 2026.

Salgado Naime FY. Statistical visibility of Indigenous People in Mexico*. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261416015.

Guillaume R-M. Data ethics: Lessons from situationism for a National Statistical Organization. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261439346.

Ulrich G-S and Schütz S. The evolving role of Official Statistics in the digital era—the case of Switzerland. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261439347.

Di Gennaro Splendore L. From national statistical office to state data agency: The Lithuanian experience as a test case for NSO-led data governance. Stat J IAOS 2026; 42: na–na.

Caracciolo C, Matadeen C, Gianfaldoni V, et al. Caliper: Interoperability and management of statistical classifications. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261445388.

Beaurepaire C, Coquet F and Le Saout R. How data science permeates the skills network in French official statistics. Stat J IAOS 2026; 42: na–na.

10.

D’Orazio M. Experiments on leveraging mobile network data for official statistics. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261445837.

11.

Tano B and Zhang L-C. Combining travel survey and mobile network operator data to produce disaggregated trip statistics. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261445838.

12.

Watambwa P, Dhliwayo L and Lepkowski J. Automating enterprise industry classifications for official statistics: Leveraging text-based similarity measures. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261433544.

13.

Smeets M, Bakker J and Meertens V. Text analysis of motivations for (not) donating smartphone sensor data. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261442051.

14.

Diego A and Federico S. Uruguay’s 2023 combined census: Integrating administrative registers and traditional enumeration, 2026. Stat J IAOS 2026.

15.

Zebua HI, Santosa SHMB and Sudrajat. Spatio-Temporal small area estimation of subdistrict per capita expenditure: Integrating panel survey and geospatial big data. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261435168.

16.

Liu A-C, Scholtus S, Van Deun K, et al. Domain estimation from weighted nonprobability samples. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261445373

17.

Czaplicki N, Xiong Y and Thompson KJ. A data dependent procedure for determining thresholds for minimum inclusion probabilities in an unequal probability sample. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261445374 .

18.

Oktora SI, Matualage D, Notodiputro KA, et al. Enhancing health survey data modeling through mixed-effects machine learning: A comparative study. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261418172.

19.

Utami NR, Krismawati D, Muryawan M, et al. An AIS-Based approach for estimating greenhouse gas emissions from shipping activities in Indonesian waters. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261420815.

20.

Albis MLF, Romasoc SO, Cajipe YNI, et al. Indicator of food price anomalies for the Philippines. Stat J IAOS 2026;0(0). doi:https://doi.org/10.1177/18747655261425760.