Improving Business Intelligence Traceability and Accountability
Bibliographic record
Abstract
A Business Intelligence (BI) system provides users with multi-dimensional information (a so-called ‘BI product') to support decision-making. However, existing BI systems overlook the lineage metadata which supports individual data quality dimensions such as data believability and ease of understanding. Using a design science research paradigm, this paper proposes and develops an integrated framework (known as BI Product and Metacontent Map - ‘BIP-Map') to facilitate the traceability and accountability of BI products. Specifically, the business workflow layer of the integrated framework is modelled using business process modelling notation, and an information product map is used to model the second layer's information manufacturing process, whilst the third layer represents the metacontent detail of the data validation stage, from source system through to ETL, to the data warehousing stage. Also, the authors develop a BIP-Map informed prototype in collaboration with an online job advertising firm, the framework then being validated by key BI stakeholders of the firm. The integrated framework addresses individual-related data quality issues and builds user confidence by enhancing the traceability and accountability of a BI product.
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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 | 0.051 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".