Systematic Literature and Expert Review of Agile Methodology Usage in Business Intelligence Projects
Downloads
Background: Agile methodology is known for delivering effective projects with added value within a shorter timeframe, especially in Business Intelligence (BI) system which is a valuable tool for informed decision-making. However, identifying impactful elements for successful BI implementation is complex due to the wide range of Agile attributes.
Objective: This research aims to systematically review and analyze the integration of BI within Agile methodology, providing valuable guidance for future projects implementation, enhancing the understanding of effective application, and identifying influential factors.
Methods: Based on the Kitchenham method, 19 papers were analyzed from 288 papers, sourced from databases like Scopus, ACM, IEEE, and others published in 2016-2022. Meanwhile the extracted key factors impacting agile BI implementation were validated by qualified expert.
Results: Agile was discovered to provide numerous benefits to BI projects by promoting flexibility, collaboration, and rapid iteration for enhanced adaptability, while effectively addressing challenges including those related to technology, management, and skills gaps. In addition, Agile methods, including tasks such as calculating cycle time, measuring defect backlogs, mapping code ownership, and engaging end users, offered practical solutions. The advantages included adaptability, success, value enhancement, cost reduction, shortened timelines, and improved precision. The research additionally considered other critical Agile elements such as BI tools, Agile Practices, Manifesto, and Methods, thereby enhancing insights for successful implementation.
Conclusion: In conclusion, the research outlined Agile BI implementation into seven key factor groups, validated by qualified expert, providing guidance for BI integration and practices, and establishing a fundamental baseline for future applications.
Keywords: Agile Methodology, Business Intelligence (BI), Expert Judgement, Kitchenham, Systematic Literature Review (SLR)
V. H. Pirttimäki, “Conceptual Analysis of Business Intelligence,” S Afr J Inf Manag, vol. 9, no. 2, Aug. 2007, doi: 10.4102/sajim.v9i2.24.
K. K. Ramachandran, A. Apsara Saleth Mary, S. Hawladar, D. Asokk, B. Bhaskar, and J. R. Pitroda, “Machine Learning and Role of Artificial Intelligence in Optimizing Work Performance and Employee Behavior,” in Materials Today: Proceedings, 2022. doi: 10.1016/j.matpr.2021.11.544.
R. Krawatzeck, B. Dinter, and D. A. P. Thi, “How to Make Business Intelligence Agile: The Agile BI Actions Catalog,” in Proceedings of the Annual Hawaii International Conference on System Sciences, IEEE Computer Society, Mar. 2015, pp. 4762–4771. doi: 10.1109/HICSS.2015.566.
D. Larson and V. Chang, “A Review and Future Direction of Agile, Business Intelligence, Analytics and Data Science,” Int J Inf Manage, vol. 36, no. 5, pp. 700–710, Oct. 2016, doi: 10.1016/j.ijinfomgt.2016.04.013.
J. Kisielnicki and A. M. Misiak, “Effectiveness of Agile Compared to Waterfall Implementation Methods in IT Projects: Analysis Based on Business Intelligence Projects,” Foundations of Management, vol. 9, no. 1, pp. 273–286, Oct. 2017, doi: 10.1515/fman-2017-0021.
S. Rezaie, S. J. Mirabedini, and A. Abtahi, “Identifying key effective factors on the implementation process of business intelligence in the banking industry of Iran,” Journal of intelligence studies in business, vol. 7, no. 3, 2017.
S. Bajaj and T. Rai, “Survey on Agile Implementation of The BI Systems,” International Journal of Engineering & Technology, vol. 7, p. 898, Aug. 2018, doi: 10.14419/ijet.v7i4.38.27604.
M. Prouza, S. Brodinová, and A. M. Tjoa, “Towards an Agile Framework for Business Intelligence Projects,” in 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 2020, pp. 1280–1285. doi: 10.23919/MIPRO48935.2020.9245166.
B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic Literature Reviews in Software Engineering - A Systematic Literature Review,” Information and Software Technology, vol. 51, no. 1. pp. 7–15, Jan. 2009. doi: 10.1016/j.infsof.2008.09.009.
C. A. Tavera Romero, J. H. Ortiz, O. I. Khalaf, and A. R. Prado, “Business Intelligence: Business Evolution After Industry 4.0,” Sustainability (Switzerland), vol. 13, no. 18. MDPI, Sep. 01, 2021. doi: 10.3390/su131810026.
M. Williams, T. Ariyachandra, and M. Frolick, “Business Intelligence - Success Through Agile Implementation,” Journal of Management & Engineering Integration, vol. 10, no. 1, pp. 14–21, 2017.
L. Rao, M. McNaughton, and G. Mansingh, “An Agile Integrated Methodology for Strategic Business Intelligence (AimS-BI),” in Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018, 2018. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054214653&partnerID=40&md5=9952865df3550e1cd340ce9c929c38f6
D. Batra, “Agile Values or Plan-Driven Aspects: Which Factor Contributes More Toward The Success of Data Warehousing, Business Intelligence, and Analytics Project Development?,” Journal of Systems and Software, vol. 146, pp. 249–262, 2018, doi: 10.1016/j.jss.2018.09.081.
R. Krawatzeck and B. Dinter, “Agile Business Intelligence: Collection and Classification of Agile Business Intelligence Actions by Means of a Catalog and a Selection Guide,” Information Systems Management, vol. 32, no. 3, pp. 177–191, Jul. 2015, doi: 10.1080/10580530.2015.1044336.
M. Al-Hasan and M. S. Hossain, “Adapting SCRUM in Data Analytics Solution Development for Telecom Operators in Bangladesh,” in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019, pp. 1–5. doi: 10.1109/ECACE.2019.8679124.
N. A. El-Adaileh and S. Foster, “Successful Business Intelligence Implementation: A Systematic Literature Review,” Journal of Work-Applied Management, vol. 11, no. 2. Emerald Group Holdings Ltd., pp. 121–132, Nov. 14, 2019. doi: 10.1108/JWAM-09-2019-0027.
J. Yin and V. Fernandez, “A Systematic Review on Business Analytics,” Journal of Industrial Engineering and Management, vol. 13, no. 2. Universitat Politecnica de Catalunya, pp. 283–295, 2020. doi: 10.3926/jiem.3030.
K. Biesialska, X. Franch, and V. Muntés-Mulero, “Big Data analytics in Agile Software Development: A Systematic Mapping Study,” Information and Software Technology, vol. 132. Elsevier B.V., Apr. 01, 2021. doi: 10.1016/j.infsof.2020.106448.
I. Krasteva and S. Ilieva, “Adopting Agile Software Development Methodologies in Big Data Projects - A Systematic Literature Review of Experience Reports,” in Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 2028–2033. doi: 10.1109/BigData50022.2020.9378118.
J. S. Saltz and I. Shamshurin, “Big Data Team Process Methodologies: A Literature Review and The Identification of Key Factors for A Project’s Success,” in Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Institute of Electrical and Electronics Engineers Inc., 2016, pp. 2872–2879. doi: 10.1109/BigData.2016.7840936.
P. Mikalef, I. O. Pappas, J. Krogstie, and M. Giannakos, “Big Data Analytics Capabilities: A Systematic Literature Review and Research Agenda,” Information Systems and e-Business Management, vol. 16, no. 3, pp. 547–578, Aug. 2018, doi: 10.1007/s10257-017-0362-y.
R. Sandstø and C. Reme-Ness, “Agile Practices and Impacts on Project Success,” Journal of Engineering, Project, and Production Management, vol. 11, no. 3, pp. 255–262, Sep. 2021, doi: 10.2478/jeppm-2021-0024.
P. q Hohl et al., “Back to The Future: Origins and Directions of The ‘Agile Manifesto’ – Views of The Originators,” Journal of Software Engineering Research and Development, vol. 6, no. 1, Dec. 2018, doi: 10.1186/s40411-018-0059-z.
B. Gina and A. Budree, “A Review of Literature on Critical Factors that Drive the Selection of Business Intelligence Tools,” in 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), 2020, pp. 1–7. doi: 10.1109/icABCD49160.2020.9183852.
N. U. Ain, G. Vaia, W. H. DeLone, and M. Waheed, “Two Decades of Research on Business Intelligence System Adoption, Utilization and Success – A Systematic Literature Review,” Decis Support Syst, vol. 125, Oct. 2019, doi: 10.1016/j.dss.2019.113113.
A. S. Saabith, T. Vinothraj, and M. Fareez, “Business Intelligence Tools-Systematic Review,” 2022. [Online]. Available: www.ijres.org394|
M. Muntean and T. Surcel, “Agile BI - The Future of BI,” Informatica Economica, vol. 17, no. 3/2013, pp. 114–124, Sep. 2013, doi: 10.12948/issn14531305/17.3.2013.10.
S. Theobald, A. Schmitt, and P. Diebold, “Comparing Scaling Agile Frameworks Based on Underlying Practices,” in Lecture Notes in Business Information Processing, Springer Verlag, 2019, pp. 88–96. doi: 10.1007/978-3-030-30126-2_11.
S. Beecham, T. Clear, R. Lal, and J. Noll, “Do Scaling Agile Frameworks Address Global Software Development Risks? An Empirical Study.” 2020. doi: 10.48550/arxiv.2009.08193.
D. K. Rigby, J. Sutherland, and H. Takeuchi, “The Secret History of Agile Innovation,” Harv Bus Rev, vol. 4, 2016.
K. Bhavsar, V. Shah, and Dr. S. Gopalan, “Scrum: An Agile Process Reengineering in Software Engineering,” International Journal of Innovative Technology and Exploring Engineering. 2020. doi: 10.35940/ijitee.c8545.019320.
A. Zaid, “Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation.” 2023. doi: 10.31219/osf.io/ksj5q.
D. Greer and Y. Hamon, “Agile Software Development,” Softw Pract Exp, vol. 41, no. 9, pp. 943–944, Aug. 2011, doi: 10.1002/spe.1100.
R. Srivastava, “Iterative Minimum Viable Product Approach to Implementing AI, RPA, and BI Solutions,” Westcliff International Journal of Applied Research, vol. 5, no. 1, pp. 44–50, Nov. 2021, doi: 10.47670/wuwijar202151rs.
J. McLean and R. Canham, “Managing the electronic resources lifecycle with kanban,” Open Information Science, vol. 2, no. 1, pp. 34–43, 2018.
J. Kisielnicki and A. M. Misiak, “Effectiveness of Agile Compared to Waterfall Implementation Methods in IT Projects: Analysis Based on Business Intelligence Projects,” Foundations of Management, vol. 9, no. 1, pp. 273–286, Oct. 2017, doi: 10.1515/fman-2017-0021.
L. Mathiassen and J. Pries-Heje, “Business Agility and Diffusion of Information Technology,” European Journal of Information Systems, vol. 15, no. 2, pp. 116–119, Apr. 2006, doi: 10.1057/palgrave.ejis.3000610.
D. Mishra and A. Mishra, “Complex software project development: agile methods adoption,” Journal of Software Maintenance and Evolution: Research and Practice, vol. 23, no. 8, pp. 549–564, Dec. 2011, doi: https://doi.org/10.1002/smr.528.
J. Black, K. Kim, S. Rhee, K. Wang, and S. Sakchutchawan, “Self-Efficacy and Emotional Intelligence,” Team Performance Management, 2019, doi: 10.1108/tpm-01-2018-0005.
K. Kaur, “Business Intelligence on Supply Chain Responsiveness and Agile Performance: Empirical Evidence from Malaysian Logistics Industry,” International Journal of Supply Chain Management, vol. 6, no. 3, pp. 31–63, 2021, [Online]. Available: www.iprjb.orgwww.iprjb.org
C. Caserio, “IT Governance in Enterprise Resource Planning and Business Intelligence Systems Environment: A Conceptual Framework,” International Journal of Management & Information Technology, 2017, doi: 10.24297/ijmit.v12i1.5915.
K. C. Moke, T. J. Low, and D. Khan, “IoT Blockchain Data Veracity with Data Loss Tolerance,” Applied Sciences, 2021, doi: 10.3390/app11219978.
N. S. Mosavi and M. F. Santos, “Implementation Considerations for the Applied Business Intelligence in Health Care,” 2021, doi: 10.1007/978-981-16-2502-2_19.
Noteboom Cherie, Ofori Martinson, Sutrave Kruttika, and El-Gayar Omar, Agile Project Management: A Systematic Literature Review of Adoption Drivers and Critical Success Factors. Proceedings of the 54th Hawaii International Conference on System Sciences, 2021.
Copyright (c) 2023 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
