Big Data: Issues, Challenges, and Techniques in Business Intelligence

Wani, Mudasir Ahmad; Jabin, Suraiya

doi:10.1007/978-981-10-6620-7_59

Cited by 28 publications

(19 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This research expands their work by empirically examining the association between BDU, SCP and SMEs' performance through moderating and mediating effects of information sharing and TI (product and process) respectively. In addition, this study answers to the future call for research by Wani and Jabin (2018). They conducted a general review of literature related to BDU techniques and its theoretical association with business intelligence.…”

Section: Discussionmentioning

confidence: 94%

Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation

Saleem

Ali

et al. 2020

JEIM

View full text Add to dashboard Cite

PurposeThis paper aims to investigate the use of big data (BDU) in predicting technological innovation, supply chain and SMEs' performance and whether technological innovation mediates the association between BDU and firm performance. Additionally, this research also seeks to explore the moderating effect of information sharing in the association between BDU and technological innovation.Design/methodology/approachUsing survey methods and structural associations in AMOS 24.0., the proposed model was tested on SME managers recruited from the largest economic and manufacturing hub of China, Pearl River Delta.FindingsThe findings suggest that BDU is positively related to technological innovation (product and process) and organizational outcomes (e.g., supply chain and SMEs performance). Technological innovation (i.e., product and process) significantly mediates the association between BDU and organizational outcomes. Moreover, information sharing positively moderates the association between BDU and technological innovations.Practical implicationsThis research provides deeper insights into how BDU is useful for SME managers in achieving the firm’s goals. Particularly, SME managers can bring technological innovation into their business processes, overcome the challenges of forecasting, and generate dynamic capabilities for attaining the best SMEs’ performance. Additionally, BDU with information sharing enables SMEs reduce their risk and decrease production costs in their manufacturing process.Originality/valueFirms always need to adopt new ways to enhance their productivity using available resources. This is the first study that contributes to big data and performance management literature by exploring the moderating and mediation mechanism of information sharing and technological innovation respectively using RBVT. The study and research model enhances our insights on BDU, information sharing, and technological innovation as valuable resources for organizations to improve supply chain performance, which subsequently increases SME productivity. This gap was overlooked by previous researchers in the domain of big data.

show abstract

Section: Discussionmentioning

confidence: 94%

Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation

Saleem

Ali

et al. 2020

JEIM

View full text Add to dashboard Cite

show abstract

“…Hidden information cannot be extracted or visualized due to the lack of scalable visualization tools. If the data is not presented more comprehensibly, visualization and decision-making also become difficult for the data miners [111].…”

Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

View full text Add to dashboard Cite

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

show abstract

“…Review: Cloud computing is gaining substantial strength coping with scalability issues [14]. One study [52] discussed the challenge of data arriving at a much faster rate than computational speeds can handle. This article noted that scalable machine learning algorithms already exist.…”

Section: Structured Vs Unstructured Data (Sud 10)mentioning

confidence: 99%

Big Data Redux: New Issues and Challenges Moving Forward

Espinosa

Kaisler

Armour

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics.

show abstract

Big Data: Issues, Challenges, and Techniques in Business Intelligence

Cited by 28 publications

References 40 publications

Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation

Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation

Text Mining in Big Data Analytics

Big Data Redux: New Issues and Challenges Moving Forward

Contact Info

Product

Resources

About