Research on Barriers to Implementing Business Analytics in Organizations

Research Article
Open access

Research on Barriers to Implementing Business Analytics in Organizations

Yaolin Lian 1*
  • 1 Beijing Jiaotong University    
  • *corresponding author 15281237@bjtu.edu.cn
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/16/20231022
AEMPS Vol.16
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-75-1
ISBN (Online): 978-1-915371-76-8

Abstract

Business Analytics has been widely applied to multiple aspects with a data-explosive background. People utilize data mining tools and models to guide marketing strategy, organization management, evaluate financial budgets, etc. However, what comes after its popularity is various implemental obstacles, this is why only a few companies have the capacity to run their own business analytics teams and adopt conclusions stemming from data and models into their organizations’ operation and decision-making. After reading and organizing previous related papers, and distilling and comparing the findings of different researchers this paper divides the barriers to the use of Business Analytics into six parts and summarizes the causes and possible effects of these barriers. In this context, barriers to implementing business analytics in organizations are introduced in dimensions of data quality, security and accessibility, analytical tools and knowledge, financial investment, human resources, analytics procedure, and organizational barriers. This article provides a basic overview of the barriers and may be useful to guide and improve the practical application of Business Analytics.

Keywords:

business, analytics, data, barriers

Lian,Y. (2023). Research on Barriers to Implementing Business Analytics in Organizations. Advances in Economics, Management and Political Sciences,16,275-282.
Export citation

References

[1]. “Chahal et al. - 2019 - Business Analytics Concept and Applications.pdf.”

[2]. M. Wedel and P. K. Kannan, “Marketing Analytics for Data-Rich Environments,” J. Mark., vol. 80, no. 6, pp. 97–121, Nov. 2016, doi: 10.1509/jm.15.0413.

[3]. D. Delen and S. Ram, “Research challenges and opportunities in business analytics,” J. Bus. Anal., vol. 1, no. 1, pp. 2–12, Jan. 2018, doi: 10.1080/2573234X.2018.1507324.

[4]. R. Ramanathan, Y. Duan, G. Cao, and E. Philpott, “Diffusion and Impact of Business Analytics: A Conceptual Framework,” vol. 6, no. 9, p. 6, 2012.

[5]. atory Findings from Select Cases,” vol. 2, no. 2, p. 13, 2014.

[6]. Y. Liu, H. Han, and J. E. DeBello, “The Challenges of Business Analytics: Successes and Failures,” p. 10, 2018.

[7]. A. Alharthi, V. Krotov, and M. Bowman, “Addressing barriers to big data,” Bus. Horiz., vol. 60, no. 3, pp. 285–292, May 2017, doi: 10.1016/j.bushor.2017.01.002.

[8]. G. V. Post and A. Kagan, “Information Security Tradeoffs: The User Perspective,” EDPACS, vol. 34, no. 3, pp. 1–10, Sep. 2006, doi: 10.1201/1079.07366981/46248.34.3.20060901/94536.1.

[9]. R. Bose, “Advanced analytics: opportunities and challenges,” Ind. Manag. Data Syst., vol. 109, no. 2, pp. 155–172, Mar. 2009, doi: 10.1108/02635570910930073.

[10]. F. Kache and S. Seuring, “Challenges and opportunities of digital information at the intersection of Big Data An-alytics and supply chain management,” Int. J. Oper. Prod. Manag., vol. 37, no. 1, pp. 10–36, Jan. 2017, doi: 10.1108/IJOPM-02-2015-0078.

[11]. R. G. Richey, T. R. Morgan, K. Lindsey-Hall, and F. G. Adams, “A global exploration of Big Data in the supply chain,” Int. J. Phys. Distrib. Logist. Manag., vol. 46, no. 8, pp. 710–739, Sep. 2016, doi: 10.1108/IJPDLM-05-2016-0134.

[12]. B. Roßmann, A. Canzaniello, H. von der Gracht, and E. Hartmann, “The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study,” Technol. Forecast. Soc. Change, vol. 130, pp. 135–149, May 2018, doi: 10.1016/j.techfore.2017.10.005.

[13]. T. T. Herden, B. Nitsche, and B. Gerlach, “Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations,” Logistics, vol. 4, no. 1, p. 5, Feb. 2020, doi: 10.3390/logistics4010005.

[14]. Md. A. Moktadir, S. M. Ali, S. K. Paul, and N. Shukla, “Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh,” Comput. Ind. Eng., vol. 128, pp. 1063–1075, Feb. 2019, doi: 10.1016/j.cie.2018.04.013.

[15]. V. Fernandez and E. Gallardo-Gallardo, “Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption,” Compet. Rev. Int. Bus. J., vol. 31, no. 1, pp. 162–187, Jan. 2021, doi: 10.1108/CR-12-2019-0163.

[16]. E. Brynjolfsson, “The productivity paradox of information technology,” Commun. ACM, vol. 36, no. 12, pp. 66–77, Dec. 1993, doi: 10.1145/163298.163309.

[17]. I. Malaka and I. Brown, “Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry,” in Proceedings of the 2015 Annual Research Conference on South Af-rican Institute of Computer Scientists and Information Technologists - SAICSIT ’15, Stellenbosch, South Africa, 2015, pp. 1–9. doi: 10.1145/2815782.2815793.

[18]. L. Bassi, “Raging Debates in HR Analytics,” p. 5, 2011.

[19]. S. Maity, “Identifying opportunities for artificial intelligence in the evolution of training and development prac-tices,” p. 13, Mar. 2019.

[20]. J. Paschen, M. Wilson, and J. J. Ferreira, “Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel,” Bus. Horiz., vol. 63, no. 3, pp. 403–414, May 2020, doi: 10.1016/j.bushor.2020.01.003.

[21]. T. Schoenherr and C. Speier-Pero, “Data Science, Predictive Analytics, and Big Data in Supply Chain Manage-ment: Current State and Future Potential,” J. Bus. Logist., vol. 36, no. 1, pp. 120–132, Mar. 2015, doi: 10.1111/jbl.12082.

[22]. S. Zhu, J. Song, B. T. Hazen, K. Lee, and C. Cegielski, “How supply chain analytics enables operational supply chain transparency: An organizational information processing theory perspective,” Int. J. Phys. Distrib. Logist. Manag., vol. 48, no. 1, pp. 47–68, Feb. 2018, doi: 10.1108/IJPDLM-11-2017-0341.

[23]. M. P. V. de Oliveira, K. McCormack, and P. Trkman, “Business analytics in supply chains – The contingent effect of business process maturity,” Expert Syst. Appl., vol. 39, no. 5, pp. 5488–5498, Apr. 2012, doi: 10.1016/j.eswa.2011.11.073.

[24]. W. L. Pomeroy, “Academic Analytics in Higher Education: Barriers to Adoption,” p. 205, 2014.

[25]. V. Grover, R. H. L. Chiang, T.-P. Liang, and D. Zhang, “Creating Strategic Business Value from Big Data Ana-lytics: A Research Framework,” J. Manag. Inf. Syst., vol. 35, no. 2, pp. 388–423, Apr. 2018, doi: 10.1080/07421222.2018.1451951.

[26]. S. Croom, N. Vidal, W. Spetic, D. Marshall, and L. McCarthy, “Impact of social sustainability orientation and supply chain practices on operational performance,” Int. J. Oper. Prod. Manag., vol. 38, no. 12, pp. 2344–2366, Oct. 2018, doi: 10.1108/IJOPM-03-2017-0180.

[27]. E. F. Amissah, E. Gamor, M. N. Deri, and A. Amissah, “Factors influencing employee job satisfaction in Ghana’s hotel industry,” J. Hum. Resour. Hosp. Tour., vol. 15, no. 2, pp. 166–183, Apr. 2016, doi: 10.1080/15332845.2016.1084858.

[28]. S. L. Dustin and A. R. Belasen, “The Impact of Negative Compensation Changes on Individual Sales Performance,” J. Pers. Sell. Sales Manag., vol. 33, no. 4, pp. 403–417, Dec. 2013, doi: 10.2753/PSS0885-3134330404.

[29]. J. Bichsel, “Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations,” p. 31, 2012.

[30]. S. Ransbotham, D. Kiron, and P. K. Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” p. 18, 2016.

[31]. P. Mikalef, M. Boura, G. Lekakos, and J. Krogstie, “Big Data Analytics Capabilities and Innovation: The Medi-ating Role of Dynamic Capabilities and Moderating Effect of the Environment,” Br. J. Manag., vol. 30, no. 2, pp. 272–298, Apr. 2019, doi: 10.1111/1467-8551.12343.

[32]. P. P. Tallon, “Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost,” Computer, vol. 46, no. 6, pp. 32–38, Jun. 2013, doi: 10.1109/MC.2013.155.

[33]. A. Lauterbach, “Artificial intelligence and policy: quo vadis?,” Digit. Policy Regul. Gov., vol. 21, no. 3, pp. 238–263, May 2019, doi: 10.1108/DPRG-09-2018-0054.

Cite this article

Lian,Y. (2023). Research on Barriers to Implementing Business Analytics in Organizations. Advances in Economics, Management and Political Sciences,16,275-282.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2nd International Conference on Business and Policy Studies

ISBN：978-1-915371-75-1(Print) / 978-1-915371-76-8(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.confbps.org/
Conference date: 26 February 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.16
ISSN：2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. “Chahal et al. - 2019 - Business Analytics Concept and Applications.pdf.”

[2]. M. Wedel and P. K. Kannan, “Marketing Analytics for Data-Rich Environments,” J. Mark., vol. 80, no. 6, pp. 97–121, Nov. 2016, doi: 10.1509/jm.15.0413.

[3]. D. Delen and S. Ram, “Research challenges and opportunities in business analytics,” J. Bus. Anal., vol. 1, no. 1, pp. 2–12, Jan. 2018, doi: 10.1080/2573234X.2018.1507324.

[4]. R. Ramanathan, Y. Duan, G. Cao, and E. Philpott, “Diffusion and Impact of Business Analytics: A Conceptual Framework,” vol. 6, no. 9, p. 6, 2012.

[5]. atory Findings from Select Cases,” vol. 2, no. 2, p. 13, 2014.

[6]. Y. Liu, H. Han, and J. E. DeBello, “The Challenges of Business Analytics: Successes and Failures,” p. 10, 2018.

[7]. A. Alharthi, V. Krotov, and M. Bowman, “Addressing barriers to big data,” Bus. Horiz., vol. 60, no. 3, pp. 285–292, May 2017, doi: 10.1016/j.bushor.2017.01.002.

[8]. G. V. Post and A. Kagan, “Information Security Tradeoffs: The User Perspective,” EDPACS, vol. 34, no. 3, pp. 1–10, Sep. 2006, doi: 10.1201/1079.07366981/46248.34.3.20060901/94536.1.

[9]. R. Bose, “Advanced analytics: opportunities and challenges,” Ind. Manag. Data Syst., vol. 109, no. 2, pp. 155–172, Mar. 2009, doi: 10.1108/02635570910930073.

[10]. F. Kache and S. Seuring, “Challenges and opportunities of digital information at the intersection of Big Data An-alytics and supply chain management,” Int. J. Oper. Prod. Manag., vol. 37, no. 1, pp. 10–36, Jan. 2017, doi: 10.1108/IJOPM-02-2015-0078.

[11]. R. G. Richey, T. R. Morgan, K. Lindsey-Hall, and F. G. Adams, “A global exploration of Big Data in the supply chain,” Int. J. Phys. Distrib. Logist. Manag., vol. 46, no. 8, pp. 710–739, Sep. 2016, doi: 10.1108/IJPDLM-05-2016-0134.

[12]. B. Roßmann, A. Canzaniello, H. von der Gracht, and E. Hartmann, “The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study,” Technol. Forecast. Soc. Change, vol. 130, pp. 135–149, May 2018, doi: 10.1016/j.techfore.2017.10.005.

[13]. T. T. Herden, B. Nitsche, and B. Gerlach, “Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations,” Logistics, vol. 4, no. 1, p. 5, Feb. 2020, doi: 10.3390/logistics4010005.

[14]. Md. A. Moktadir, S. M. Ali, S. K. Paul, and N. Shukla, “Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh,” Comput. Ind. Eng., vol. 128, pp. 1063–1075, Feb. 2019, doi: 10.1016/j.cie.2018.04.013.

[15]. V. Fernandez and E. Gallardo-Gallardo, “Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption,” Compet. Rev. Int. Bus. J., vol. 31, no. 1, pp. 162–187, Jan. 2021, doi: 10.1108/CR-12-2019-0163.

[16]. E. Brynjolfsson, “The productivity paradox of information technology,” Commun. ACM, vol. 36, no. 12, pp. 66–77, Dec. 1993, doi: 10.1145/163298.163309.

[17]. I. Malaka and I. Brown, “Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry,” in Proceedings of the 2015 Annual Research Conference on South Af-rican Institute of Computer Scientists and Information Technologists - SAICSIT ’15, Stellenbosch, South Africa, 2015, pp. 1–9. doi: 10.1145/2815782.2815793.

[18]. L. Bassi, “Raging Debates in HR Analytics,” p. 5, 2011.

[19]. S. Maity, “Identifying opportunities for artificial intelligence in the evolution of training and development prac-tices,” p. 13, Mar. 2019.

[20]. J. Paschen, M. Wilson, and J. J. Ferreira, “Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel,” Bus. Horiz., vol. 63, no. 3, pp. 403–414, May 2020, doi: 10.1016/j.bushor.2020.01.003.

[21]. T. Schoenherr and C. Speier-Pero, “Data Science, Predictive Analytics, and Big Data in Supply Chain Manage-ment: Current State and Future Potential,” J. Bus. Logist., vol. 36, no. 1, pp. 120–132, Mar. 2015, doi: 10.1111/jbl.12082.

[22]. S. Zhu, J. Song, B. T. Hazen, K. Lee, and C. Cegielski, “How supply chain analytics enables operational supply chain transparency: An organizational information processing theory perspective,” Int. J. Phys. Distrib. Logist. Manag., vol. 48, no. 1, pp. 47–68, Feb. 2018, doi: 10.1108/IJPDLM-11-2017-0341.

[23]. M. P. V. de Oliveira, K. McCormack, and P. Trkman, “Business analytics in supply chains – The contingent effect of business process maturity,” Expert Syst. Appl., vol. 39, no. 5, pp. 5488–5498, Apr. 2012, doi: 10.1016/j.eswa.2011.11.073.

[24]. W. L. Pomeroy, “Academic Analytics in Higher Education: Barriers to Adoption,” p. 205, 2014.

[25]. V. Grover, R. H. L. Chiang, T.-P. Liang, and D. Zhang, “Creating Strategic Business Value from Big Data Ana-lytics: A Research Framework,” J. Manag. Inf. Syst., vol. 35, no. 2, pp. 388–423, Apr. 2018, doi: 10.1080/07421222.2018.1451951.

[26]. S. Croom, N. Vidal, W. Spetic, D. Marshall, and L. McCarthy, “Impact of social sustainability orientation and supply chain practices on operational performance,” Int. J. Oper. Prod. Manag., vol. 38, no. 12, pp. 2344–2366, Oct. 2018, doi: 10.1108/IJOPM-03-2017-0180.

[27]. E. F. Amissah, E. Gamor, M. N. Deri, and A. Amissah, “Factors influencing employee job satisfaction in Ghana’s hotel industry,” J. Hum. Resour. Hosp. Tour., vol. 15, no. 2, pp. 166–183, Apr. 2016, doi: 10.1080/15332845.2016.1084858.

[28]. S. L. Dustin and A. R. Belasen, “The Impact of Negative Compensation Changes on Individual Sales Performance,” J. Pers. Sell. Sales Manag., vol. 33, no. 4, pp. 403–417, Dec. 2013, doi: 10.2753/PSS0885-3134330404.

[29]. J. Bichsel, “Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations,” p. 31, 2012.

[30]. S. Ransbotham, D. Kiron, and P. K. Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” p. 18, 2016.

[31]. P. Mikalef, M. Boura, G. Lekakos, and J. Krogstie, “Big Data Analytics Capabilities and Innovation: The Medi-ating Role of Dynamic Capabilities and Moderating Effect of the Environment,” Br. J. Manag., vol. 30, no. 2, pp. 272–298, Apr. 2019, doi: 10.1111/1467-8551.12343.

[32]. P. P. Tallon, “Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost,” Computer, vol. 46, no. 6, pp. 32–38, Jun. 2013, doi: 10.1109/MC.2013.155.

[33]. A. Lauterbach, “Artificial intelligence and policy: quo vadis?,” Digit. Policy Regul. Gov., vol. 21, no. 3, pp. 238–263, May 2019, doi: 10.1108/DPRG-09-2018-0054.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.