The DOI matches its Crossref record.
The Current State of Business Intelligence
Open accessAbstract
Business intelligence (BI) is now widely used, especially in the world of practice, to describe analytic applications. BI is currently the top-most priority of many chief information officers. BI has become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation. BI is a process that includes two primary activities: getting data in and getting data out. Getting data in, traditionally referred to as data warehousing, involves moving data from a set of source systems into an integrated data warehouse. Getting data in delivers limited value to an enterprise; only when users and applications access the data and use it to make decisions does the organization realize the full value from its data warehouse. Thus, getting data out receives most attention from organizations. This second activity, which is commonly referred to as BI, consists of business users and applications accessing data from the data warehouse to perform enterprise reporting, OLAP, querying, and predictive analytics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
Impact of business analytics and enterprise systems on managerial accounting
Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2017.03.003 · Cited by 554
LifeLogging: Personal Big Data
Gurrin, C., Smeaton, A. F., & Doherty, A. R. (2014) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000033 · Cited by 434
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud1
Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012) · MIS Quarterly · doi:10.2307/41703508 · Cited by 298
A survey on fog computing for the Internet of Things
Bellavista, P., Berrocal, J., Corradi, A., Das, S. K., Foschini, L., & Zanni, A. (2019) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.12.007 · Cited by 268
Big data analytics: transforming data to action
Bumblauskas, D., Nold, H., Bumblauskas, P., & Igou, A. (2017) · Business Process Management Journal · doi:10.1108/bpmj-03-2016-0056 · Cited by 130
Metadata
- Authors
- Watson, H. J., & Wixom, B. H.
- Venue
- Computer
- Published
- 2007-09
- Volume
- 40
- Issue
- 9
- Pages
- 96-99
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W2024231807
- Cited by
- 639
- References
- 0
- FWCI
- 27.69
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.