Big Data Governance:The Case of Mobile Positioning Data for Official Tourism Statistics In Indonesia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".