Understanding Data Valuation: Valuing Google’s Data Assets
Bibliographic record
Abstract
Digital personal data are increasingly understood as a key asset in our digital economies. But how should we value such data? Numerous policymakers, regulators, and stakeholders are trying to work out how to manage the collection, use, and valuation of data in order to balance the advantages and disadvantages of its collection and use. The negative implications of data practices may include privacy loss, data breaches, or declining market competition, while social and economic benefits include improved service delivery, more efficient welfare systems, or better products. Increasingly, data are conceptualized as an asset. To understand the value of data as an asset means understanding how data are configured as an asset; data value does not reflect ownership and property rights per se, but rather diverse modes of access and use restrictions (usually delineated by opaque contractual agreements) Data are increasingly controlled by a few, large digital technology firms, especially so-called Big Tech firms. In this paper, we use Google as a case study of how Big Tech firms configure and value digital data as an asset. We analyse how Google understands, frames, values, and monetizes the data they collect from users. We qualitatively analyse an extensive dataset of financial documentary materials produced by and about Google to identify the different modes of access and use restrictions that Google deploys to turn digital data into a valuable asset. We conclude that, despite being highly ambiguous, Google’s approach to data value focuses on monetizing users, not data.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".