Big Data Governance:The Case of Mobile Positioning Data for Official Tourism Statistics In Indonesia
Notice bibliographique
Résumé
The National Statistics Office (NSO) of Indonesia (Statistics Indonesia) has been using Mobile Positioning Data (MPD) to measure cross-border foreign visitors since 2016. Indonesia is one of the few countries that have already used MPD as one of its official statistics products, as it provides more accurate data with better coverage and timeliness on tourism arrival compared to the traditional method. Following its success, since 2018, research on the potential use of MPD has been expanded to other purposes, such as measuring domestic tourism and people’s mobility in metropolitan areas. Not only the MPD but research on the potential use of other Big Data sources for official statistics has also increased response to the demand by related stakeholders and decision-makers. Following that, with the development and application of Big Data in various sectors and purposes, including official statistics, the role of Big Data governance is becoming increasingly important. Big Data governance is a holistic approach that allows the harmonization of people, methods, tools, and technologies to deal with structured and unstructured data. Big Data governance is also a new stage in the development of data governance, especially in exploring its theory and practice to improve organizational data management and utilization. Currently, despite the current success of the use of MPD, there are some challenges regarding Big Data governance that have possibly become threats to data sustainability and the entire data provision process. In this paper, we aim to investigate the issues and challenges of Big Data governance in the case study of MPD for tourism statistics in Indonesia. Our research aims to identify challenges in the dimensions of the big data governance framework, specifically in addressing issues on the role and communication among stakeholders, institutions or organizations, data quality, and regulatory compliance. To that aim, we conducted a field study in Statistics Indonesia, consisting of semi-structured interviews with related stakeholders. Through the result findings of our qualitative research on the MPD case study, we expect both to provide more insight and understanding of the urgency of big data governance and its framework for official statistics.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».