A Conceptual Model of Metadata’s Role in BI Success
Bibliographic record
Abstract
Modern organizations rely on Business Intelligence (BI) systems to provide the information needed to support a wide array of decisions, many of which have significant financial and strategic consequences. As such, information quality is critically important but is also highly contextual, meaning that information that is of sufficient quality for one purpose may not be so for others. The implication of this fact is that users must have the ability to assess information for its fitness to specific purposes. The authors submit that metadata provides this capability. Metadata is information that serves to provide insight into the meaning, quality, location, and lineage of information resources (for example, data elements, queries, and reports) provided by BI systems. In this chapter, they describe how organizations can increase the levels of use of their BI systems by providing the right metadata to users. The authors propose a conceptual model that describes how metadata contributes to the level of BI system use by creating positive attitudes toward the information available. They validate the model through consultation with experts in the fields of BI, information quality, and metadata management as well as through a survey of over 250 BI practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".