A review and future direction of agile, business intelligence, analytics and data science
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Prescriptive analytics has been increasingly gathering research interest.
“Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest.”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Larson & Chang, 2016
The Analytics section says prescriptive analytics began to emerge and that organizations are focusing more on it. This supports growing interest in the field, but the passages describe organizational attention, not an increase in research interest specifically. No page number is available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAlthough there have been many descriptive and predictive algorithms utilizing data mining, machine learning and artificial intelligence, prescriptive analytics has not gathered the same attention yet.
“Although there have been a lot of descriptive and predictive algorithms utilizing data mining, machine learning and artificial intelligence (Larson & Chang, 2016;Tsai et al., 2015), prescriptive analytics has not gathered the same attention yet.”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Larson & Chang, 2016
The Analytics section (page unavailable) says descriptive analysis was prominent while predictive and prescriptive analytics were emerging, which supports the broad comparison. The Model/Design/Development section (page unavailable) links descriptive and predictive analysis to machine-learning algorithms. Our copy does not establish that many such algorithms use artificial intelligence, and it also says organizations are increasingly focusing on prescriptive analysis. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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“…related fields, like the agile approach used in software development, are being considered for use in data science projects, there is no clear clarity on whether they are fully suitable for the purpose, as indicated by Larson and Chang (2016); therefore, we did not include them in the current scope.”
Method
- Authors
- Larson, D., & Chang, V.
- Affiliations
- Xi’an Jiaotong-Liverpool University (CN)
- Corresponding author
- Chang, Victor
- ORCID
- Chang, Victor
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 36
- Issue
- 5
- Pages
- 700-710
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2340139852
- Cited by
- 427
- References
- 33
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 37 · 2023: 53 · 2022: 44 · 2021: 78 · 2020: 61 · 2019: 58 · 2018: 39 · 2017: 22 · 2016: 10 · 2012: 1
- FWCI
- 77.18
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- International Journal of Information Management (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- agile methods, business intelligence, data analytics, data science, big data
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