Big data reduction framework for value creation in sustainable enterprises
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The extant literature highlights analytical processes and methods such as text analytics, audio analytics, video analytics, social media analytics, predictive analysis of data, and others reported as descriptive analytics, inquisitive analytics, prescriptive analytics and pre-emptive data analytics.
“The extant literature highlights a number of analytical processes and methodssuch as text analytics, audio analytics, video analytics, social media analytics, predictive analysis of data (Gandomi & Haider, 2015) and others reported of descriptive analytics, inquisitive analytics, prescriptive analytics and pre-emptive data analytics (Assunção et al., 2015;Rehman, Chang, Batool, & Teh, 2016).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Rehman, Chang, Batool, & Teh, 2016
The Monitoring section describes descriptive and prescriptive analytics, supporting two of the categories attached to the Rehman et al. citation. It does not identify inquisitive or pre-emptive data analytics; searches for those terms returned no matching passage. The earlier list of text, audio, video and social media analytics is attached to a different citation. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPredictive analytics is concerned with forecasting and statistical modelling to determine future possibilities based on supervised, unsupervised, and semi-supervised learning models.
“This analytics is concerned with forecasting and statistical modelling to determine the future possibilities based on supervised, unsupervised, and semi-supervised learning models (Joseph & Johnson, 2013;Rehman et al., 2016;Waller & Fawcett, 2013).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Rehman et al., 2016
Descriptive analytics are the simplest form of BDA method and involve summarization and description of knowledge patterns using simple statistical methods such as mean, median, mode, standard deviation, variance, and frequency measurement of specific events in BD streams.
“Descriptive analytics are the simplest form of BDA method, and involves the summarization and description of knowledge patterns using simple statistical methods, such as mean, median, mode, standard deviation, variance, and frequency measurement of specific events in BD streams (Rehman et al., 2016).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Rehman et al., 2016
Although difficult to deploy, prescriptive analytics contribute to handling the information shift and the continuous evolution of business process models.
“Although difficult to deploy, prescriptive analytics contribute to handling the information shift and the continuous evolution of business process models (Rehman et al., 2016).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Rehman et al., 2016
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Big Data Capabilities and Firm’s Performance: A Mediating Role of Competitive Advantage
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- Authors
- Rehman, M. H. U., Chang, V., Batool, A., & Wah, T. Y.
- Affiliations
- University of Malaya (MY)
- Xi’an Jiaotong-Liverpool University (CN)
- Iqra University (PK)
- Corresponding author
- Rehman, Muhammad Habib ur
- Wah, Teh Ying
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 36
- Issue
- 6
- Pages
- 917-928
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2407961458
- Cited by
- 279
- References
- 75
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 18 · 2023: 30 · 2022: 39 · 2021: 34 · 2020: 43 · 2019: 35 · 2018: 38 · 2017: 16 · 2016: 5
- FWCI
- 56.24
- 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)
- Funders
- University of Malaya Research (VOTE RP028C-14AET)
- Bright Spark Unit of University of Malaya (BSP/APP/1634/2013)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Big Data and Digital Economy
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- value creation, sustainable enterprises, cloud service costs, consumer trust, consumer privacy, secure data sharing, business model canvas, technology adoption barriers, enterprise data management
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