Big Data: Challenges and Opportunities in Financial Management
Bibliographic record
Abstract
This paper describes the challenges and opportunities of using “big data” in the practice of financial management. The research question addressed in this work is what the major topics in existing research concerning the demand for big data skills are and where the resulting gaps in financial management occur. The experts noticed the transformation of the finance manager profession and predict that in next decade big data skills will be required for financial managers. Th e purposes of the paper are: to analyze the current state of the financial manager profession in selected labor markets, to identify the number of job positions with big data skills currently needed and to check additional skills and competencies needed in practice. The purpose of the literature study is to highlight the opportunities and challenges of big data technologies in financial management. To present a snapshot of big data skills demand in the European labor market for financial managers, we conducted research which reveals core skills currently needed for this position. We examined the most popular job search websites to find finance managers job openings that require big data skills in selected European countries. In conclusion, we provide potential areas for further research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".