Valuing data: Where are we, and where do we go next?
Bibliographic record
Abstract
The importance of data as a driver of technological advancement cannot be underestimated, but how can it be measured? This paper looks at measuring the value of data in national accounts using three different categories of data-related assets: data itself, databases and data science. The focus then turns to three recent studies by statistical agencies in Canada, the Netherlands and the United States to examine how each country uses a cost-based analysis to value data-related assets. Although there are two other superior ways of valuing data (the income-based method and the market-based method, as well as a hybrid approach), the authors find that these methods will be difficult to implement. The paper concludes with recommendations that include widening data-valuation efforts to the public sector, which is a major holder of data. The social value of data also needs to be calculated by considering both the positive and negative aspects of data-related investment and use. Appropriate data governance strategies are needed to ensure that data is being used for everyone's benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.040 | 0.075 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".