Trusted Smart Statistics: How new data will change official statistics

Ricciato, Fabio; Wirthmann, Albrecht; Hahn, Martina

doi:10.1017/dap.2020.7

Cited by 22 publications

(17 citation statements)

References 11 publications

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“…But maybe the winds of change are starting to blow, gently. A recent article from Ricciato et al [19] entitled 'Trusted Smart Statistics: How new data will change official Statistics' discusses the need for a more inter-disciplinary participatory approach to official statistics, one that includes contributions beyond the 'traditional competence perimeter of statisticians' towards what they call 'Citizen Statistics'.…”

Section: Resultsmentioning

confidence: 99%

You say you want a [data] revolution: Reflections one year on

Nastav

MacFeely

2020

SJI

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In August 2019, the IAOS discussion platform was launched with a special session at the ISI World Congress in Kuala Lumpur, Malaysia. The first paper published as part of that discussion series was ‘You say you want a [data] Revolution: A proposal to use unofficial statistics for the SDG Global Indicator Framework’. This follow-on paper looks back over the year since the launch, summarizes the online debate, highlights some other relevant papers, and reflects on where the discussion rests today.

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Section: Resultsmentioning

confidence: 99%

You say you want a [data] revolution: Reflections one year on

Nastav

MacFeely

2020

SJI

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“…are transformed into mobility data products with a higher degree of comparability across MNOs than the original matrices. Although the derived insights have proved extremely useful, constraints of data heterogeneity across operators could be overcome by formulating refined data requests following Trusted Smart Statistics 7 concept as in Ricciato et al (2020b), with the results of providing harmonized statistics on human mobility to more effectively feed epidemiological models.…”

Section: Mobility Data: Definitions Harmonization and Comparability Across Providersmentioning

confidence: 99%

On the use of data from multiple mobile network operators in Europe to fight COVID-19

et al. 2021

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The rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information regarding collective mobility patterns to inform de-escalation strategies as well as to provide forecasting capacity for re-escalation policies aiming at addressing further waves of the virus. Such information can be extracted using aggregate anonymized data from innovative sources such as mobile positioning data. This paper presents lessons learnt and results of a unique Business-to-Government initiative between several mobile network operators in Europe and the European Commission. Mobile positioning data have supported policy-makers and practitioners with evidence and data-driven knowledge to understand and predict the spread of the disease, the effectiveness of the containment measures, their socio-economic impacts while feeding scenarios at European Union scale and in a comparable way across countries. The challenges of these data sharing initiative are not limited to data quality, harmonization, and comparability across countries, however important they are. Equally essential aspects that need to be addressed from the onset are related to data privacy, security, fundamental rights, and commercial sensitivity.

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“…These granular data are manageable by new technologies to produce official statistics. Nano-data also refers to data records at the sub-individual level (eventbased) [2,17].…”

Section: Statistical Engineeringmentioning

confidence: 99%

“…The development of TSS focuses on data that may be pre-processed by data providers or data sources, preparing it for immediate use for official statistics. This usually means combining this data with existing surveys or administrative data [17]. Trusted Smart Statistics is a natural evolution of official statistics in the new datafied world.…”

Section: Trusted Smart Statisticsmentioning

confidence: 99%

Data science training for official statistics: A new scientific paradigm of information and knowledge development in national statistical systems

Ashofteh

Bravo

2021

SJI

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The ability to incorporate new and Big Data sources and to benefit from emerging technologies such as Web Technologies, Remote Data Collection methods, User Experience Platforms, and Trusted Smart Statistics will become increasingly important in producing and disseminating official statistics. The skills and competencies required to automate, analyse, and optimize such complex systems are often not part of the traditional skill set of most National Statistical Offices. The adoption of these technologies requires new knowledge, methodologies and the upgrading of the quality assurance framework, technology, security, privacy, and legal matters. However, there are methodological challenges and discussions among scholars about the diverse methodical confinement and the wide array of skills and competencies considered relevant for those working with big data at NSOs. This paper develops a Data Science Model for Official Statistics (DSMOS), graphically summarizing the role of data science in statistical business processes. The model combines data science, existing scientific paradigms, and trusted smart statistics, and develops around a restricted number of constructs. We considered a combination of statistical engineering, data engineering, data analysis, software engineering and soft skills such as statistical thinking, statistical literacy and specific knowledge of official statistics and dissemination of official statistics products as key requirements of data science in official statistics. We then analyse and discuss the educational requirements of the proposed model, clarifying their contribution, interactions, and current and future importance in official statistics. The DSMOS was validated through a quantitative method, using a survey addressed to experts working at the European statistical systems. The empirical results show that the core competencies considered relevant for the DSMOS include acquisition and processing capabilities related to Statistics, high-frequency data, spatial data, Big Data, and microdata/nano-data, in addition to problem-solving skills, Spatio-temporal modelling, machine learning, programming with R and SAS software, Data visualisation using novel technologies, Data and statistical literacy, Ethics in Official Statistics, New data methodologies, New data quality tools, standards and frameworks for official statistics. Some disadvantages and vulnerabilities are also addressed in the paper.

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Trusted Smart Statistics: How new data will change official statistics

Cited by 22 publications

References 11 publications

You say you want a [data] revolution: Reflections one year on

You say you want a [data] revolution: Reflections one year on

On the use of data from multiple mobile network operators in Europe to fight COVID-19

Data science training for official statistics: A new scientific paradigm of information and knowledge development in national statistical systems

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