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Challenges and opportunities of adopting business intelligence in SMEs: collaborative model

Published: 01 October 2018 Publication History

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Abstract

The increases of market competition, and the huge amount of data collected by business activities, raise the importance of using Business intelligence (BI) system. BI systems enable firms to benefit from the data and gain insights about the business opportunities, which will lead to a better decision and increase the profit. Large firms can use the existing BI solutions, as they have the financial power and the capacity to use them, whereas the small and medium sized enterprises are overlooked due to the lack of finance. This paper surveys some current BI solutions that can be used by SMEs in attempt to remedy the challenges of adopting BI in SME sector. In addition, a new proposed model is presented to potentially support SMEs to overcome the challenges of adopting BI in SME sector - It is expected that such model could extra level of reliability for SMEs support.

References

[1]
Schmiemann, M., 2008. Enterprises by size class-overview of SMEs in the EU. Statistics in focus, 31, p.2008.
[2]
Kotelnikov V, Kim H. Small and Medium Enterprises and ICT, Asia-Pacific Development Information Programme e-Primers for the Information Economy. Society and Polity,(APCICT). 2007.
[3]
World Bank (2006). Micro, Small and Medium Enterprises. International Financial Corporation Report.
[4]
Turban E, Sharda R, Aronson JE, King D. Business intelligence: A managerial approach. Upper Saddle River, NJ: Pearson Prentice Hall; 2008.
[5]
Chaudhuri S, Dayal U, Narasayya V. An overview of business intelligence technology. Communications of the ACM. 2011 Aug 1;54(8):88--98.
[6]
Sharda R, Delen D, Turban E, Aronson J, Liang TP. Businesss Intelligence and Analytics: Systems for Decision Support-(Required). London: Prentice Hall; 2014.
[7]
Adelman S, Moss L, Barbusinski L. I found several definitions of BI. DM Review. 2002 Aug:5700--1.
[8]
Bezon A, Smith SJ. Data Warehousing, Data Mining & OLAP. MeGraw-Hill Edition. 2001. Adelman S, Moss L, Barbusinski L. I found several definitions of BI. DM Review. 2002 Aug:5700--1.
[9]
Kimball R, Ross M. The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons; 2011 Aug 8.
[10]
Chaudhuri S, Dayal U, Narasayya V. An overview of business intelligence technology. Communications of the ACM. 2011 Aug 1;54(8):88--98.
[11]
Davenport TH. Competing on analytics. harvard business review. 2006 Jan 1;84(1):98.
[12]
Gangadharan GR, Swami SN. Business intelligence systems: design and implementation strategies. InInformation Technology Interfaces, 2004. 26th International Conference on 2004 Jun 7 (pp. 139--144). IEEE.
[13]
Sang G, Xu L, de Vrieze PT. Implementing a Business Intelligence System for small and medium-sized enterprises.
[14]
Scholz P, Schieder C, Kurze C, Gluchowski P, Böhringer M. Benefits and Challenges of Business Intelligence Adoption in Small and Medium-Sized Enterprises. InECIS 2010 (p. 32).
[15]
Aljawarneh, S. A. and Vangipuram, R. 2018. GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things. The Journal of Supercomputing, 1--38.
[16]
Aljawarneh, S. A., Vangipuram, R., Puligadda, V. K., and Vinjamuri, J. 2017. G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things. Future Generation Computer Systems, 74, 430--443.
[17]
Radhakrishna, V., Aljawarneh, S. A., Kumar, P. V., and Janaki, V. 2018. A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining. Future Generation Computer Systems, 83, 582--595.
[18]
Aljawarneh, Shadi A., Raja A. Moftah, and Abdelsalam M. Maatuk. "Investigations of automatic methods for detecting the polymorphic worms signatures." Future Generation Computer Systems 60 (2016): 67--77.
[19]
Muneer Bani Yassein, Shadi A. Aljawarneh, and Esraa Masadeh. 2017. A new elastic trickle timer algorithm for Internet of Things. J. Netw. Comput. Appl. 89, C (July 2017), 38--47.
[20]
Shadi A. Aljawarneh, Mohammed R. Elkobaisi, and Abdelsalam M. Maatuk. 2017. A new agent approach for recognizing research trends in wearable systems. Comput. Electr. Eng. 61, C (July 2017), 275--286.

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DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
October 2018
274 pages
ISBN:9781450365369
DOI:10.1145/3279996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 October 2018

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Author Tags

  1. business intelligence (BI)
  2. decision support systems (DSS)
  3. small and medium sized enterprises (SMEs)

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  • (2024)Accelerating the software development process through the application of MVP and monolithic architectureProceedings of the XXIII Brazilian Symposium on Software Quality10.1145/3701625.3701632(469-477)Online publication date: 5-Nov-2024
  • (2023)A System Implementation: Point-of-Sales (POS) System Integrated with Business Intelligence (BI) Capability Focused on SME in Indonesia2023 15th International Conference on Developments in eSystems Engineering (DeSE)10.1109/DeSE58274.2023.10099802(287-292)Online publication date: 9-Jan-2023
  • (2022)Development of a Model for the Implementation of Business Intelligence in SMEsProceedings of the 12th International Conference on Information Communication and Management10.1145/3551690.3551700(61-68)Online publication date: 13-Jul-2022
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