Beyond the hype: Big data concepts, methods, and analytics
Open accessAbstract
Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
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Social network analysis has been researched in relation to electronic word-of-mouth, identifying influencers, and investigating how individuals influence others, the relationship between influence and tie strength, and the flow of information through social media.
“Social network analysis has been researched in relation to electronic word-of-mouth (Sohn, 2009), identifying influencers (Zhang & Li, 2014) and investigating how individuals influence others, the relationship between influence and tie strength and the flow of information through social media (Gandomi & Haider, 2015).”
From Setting the future of digital and social media marketing research: Perspectives and research propositions (2021) · cites this paper as Gandomi & Haider, 2015
Different researchers have distinct understandings towards data characteristics: some say 3Vs, others 4Vs, and 6Vs.
“Different researchers have distinct understandings towards the data characteristicssuch as some say 3Vs [volume, velocity and variety] of data (e.g.Shah, Rabhi, & Ray, 2015), others reported 4Vs [volume, velocity, variety, and variability] of data (e.g.Liao, Yin, Huang, & Sheng, 2014) and 6Vs [volume, velocity, variety, veracity, variability, and value] of data (Gandomi & Haider, 2015).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Gandomi & Haider, 2015
Data from mobile devices provide sound information about customers, such as geospatial location, buying behaviour and patterns, which can be analyzed in real-time to generate value for customers.
“These data also provide sound information about customers, such as their geospatial location, buying behaviour and patterns, which can be analyzed in real-time to generate value for customers (Gandomi & Haider, 2015).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Gandomi & Haider, 2015
The necessity to deal with inaccurate and ambiguous data is another facet of big data, addressed using tools and analytics developed for management and mining of unreliable data.
“Thus, the necessity to deal with inaccurate and ambiguous data is another facet of BD, which is addressed using tools and analytics developed for management and mining of unreliable data (Gandomi & Haider, 2015).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Gandomi & Haider, 2015
There is a need to develop new solutions for predictive analytics for structured BD.
“Gandomi and Haider (2015) asserts the need to develop new solutions for predictive analytics for structured BD.”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Gandomi and Haider (2015)
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 Gandomi & Haider, 2015
Gandomi and Haider categorised predictive analysis into two groups: regression techniques (e.g., multinomial logit models) and machine learning techniques (e.g., neural networks).
“Gandomi and Haider (2015) categorised predictive analysis into two groupsregression techniques (e.g., multinomial logit models) and machine learning techniques (e.g., neural networks).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Gandomi and Haider (2015)
95% of big data is unstructured, necessitating the development of new analytic tools and techniques specific to the properties of big data sets.
“In contrast, 95% of big data is unstructured, necessitating the development of new analytic tools and techniques specific to the properties of big data sets [22].”
From A Big Data Analytics Method for Tourist Behaviour Analysis (2017) · cites this paper as [22]
The current study contributes to the literature on Big Data and extends current reviews (Sagiroglu and Sinanc 2013; Fosso-Wamba et al. 2015; Gandomi and Haider 2015; Wang et al. 2016; Khorheh et al. 2015; Mishra et al. 2016a;2016b).
“The current study contributes to the literature on Big Data and extends current reviews (Sagiroglu and Sinanc 2013;Fosso-Wamba et al. 2015;Gandomi and Haider 2015;Wang et al. 2016;Khorheh et al. 2015;Mishra et al. 2016a;2016b)”
From Big Data and supply chain management: A review and bibliometric analysis (2018) · cites this paper as Gandomi and Haider 2015
Further data analyses were conducted in a data-driven manner with artificial intelligence, such as k-means or unsupervised cluster analysis, recursive feature elimination, rotation random forest classifier, and supervised machine learning algorithms.
“Further data analyses were conducted in a data-driven manner [223] with artificial intelligence, such as k-means [176] or unsupervised cluster analysis [172], recursive feature elimination technique [170], rotation random forest classifier [130], and supervised machine learning algorithms using logistic regression, decision tree, and random forest [215].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [223]
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- Authors
- Gandomi, A., & Haider, M.
- Affiliations
- Toronto Metropolitan University (CA)
- Corresponding author
- Gandomi, Amir
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 35
- Issue
- 2
- Pages
- 137-144
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2261525379
- Cited by
- 4,226
- References
- 34
- Citations by year
- 2026: 184 · 2025: 301 · 2024: 372 · 2023: 404 · 2022: 425 · 2021: 576 · 2020: 498 · 2019: 504 · 2018: 447 · 2017: 312 · 2016: 141 · 2015: 53 · 2014: 6 · 2013: 1
- FWCI
- 319.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Information Management (publishedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- big data, big data analytics, unstructured data, predictive analytics, data heterogeneity, computational efficiency, spurious correlations, big data definition
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