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2019 | OriginalPaper | Chapter

7. A Review and Future Direction of Business Analytics Project Delivery

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Abstract

Business analytics is a core competency critical to organizations to stay competitive; however, many organizations are challenged at business analytics delivery, and these projects have a high rate of failure. The volume, variety, and velocity of the big data phenomenon and the lack of current methodologies for delivering business analytics projects are the primary challenges. Applying traditional information technology project methodologies is problematic and has been identified as the largest contributing factor for business analytics project failure. Business analytics projects focus on delivering data insights as well as software delivery. Agile methodologies focus on the ability to respond to change through incremental, iterative processes. Agile methodologies in software delivery have been on the rise, and the dynamic principles align with the discovery nature of business analytics projects. This article explores the big data phenomenon, its impact on business analytics project delivery, and recommends an agile framework for business analytic project delivery using agile methodology principles and practices.

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Literature
go back to reference Abbasi, A., & Adjeroh, D. (2014). Social media analytics for smart health. IEEE Intelligent Systems, 29(2), 60–64.CrossRef Abbasi, A., & Adjeroh, D. (2014). Social media analytics for smart health. IEEE Intelligent Systems, 29(2), 60–64.CrossRef
go back to reference Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016). Data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), i–xxxii.CrossRef Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016). Data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), i–xxxii.CrossRef
go back to reference Alnoukari, M. (2015). ASD-BI: An agile methodology for effective integration of data mining in business intelligence systems. Hershey, PA: IGI Publishing. Alnoukari, M. (2015). ASD-BI: An agile methodology for effective integration of data mining in business intelligence systems. Hershey, PA: IGI Publishing.
go back to reference Bole, U., Popovič, A., Žabkar, J., Papa, G., & Jaklič, J. (2015). A case analysis of embryonic data mining success. International Journal of Information Management, 35(2), 253–259.CrossRef Bole, U., Popovič, A., Žabkar, J., Papa, G., & Jaklič, J. (2015). A case analysis of embryonic data mining success. International Journal of Information Management, 35(2), 253–259.CrossRef
go back to reference Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64–72. Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64–72.
go back to reference Grimes, S. (2006). In BI deployments, methodology does matter. Intelligent Enterprise - San Mateo, 9(11), 9. Grimes, S. (2006). In BI deployments, methodology does matter. Intelligent Enterprise - San Mateo, 9(11), 9.
go back to reference Jurney, R. (2017). Agile Data Science 2.0. Sebastopol, CA: O’Reilly Media. Jurney, R. (2017). Agile Data Science 2.0. Sebastopol, CA: O’Reilly Media.
go back to reference Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700–710.CrossRef Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700–710.CrossRef
go back to reference Layton, M. C., & Ostermiller, S. J. (2017). Agile project management for dummies. Hoboken, NJ: Wiley. Layton, M. C., & Ostermiller, S. J. (2017). Agile project management for dummies. Hoboken, NJ: Wiley.
go back to reference Marbán, Ó., Mariscal, G., & Segovia, J. (2009). A data mining & knowledge discovery process model. In Data Mining and Knowledge Discovery in Real Life Applications. Julio Ponce and Adem Karahoca, ISBN 978-3-902613-53-0, pp. 438, February 2009, I-Tech, Vienna, Austria Marbán, Ó., Mariscal, G., & Segovia, J. (2009). A data mining & knowledge discovery process model. In Data Mining and Knowledge Discovery in Real Life Applications. Julio Ponce and Adem Karahoca, ISBN 978-3-902613-53-0, pp. 438, February 2009, I-Tech, Vienna, Austria
go back to reference Sahu, A. K. (2016). The criticism of data mining applications and methodologies. International Journal of Advanced Research in Computer Science, 7(1), 52–55. Sahu, A. K. (2016). The criticism of data mining applications and methodologies. International Journal of Advanced Research in Computer Science, 7(1), 52–55.
go back to reference Sim, J. (2014). Consolidation of success factors in data mining projects. GSTF Journal on Computing (JoC), 4(1), 66. Sim, J. (2014). Consolidation of success factors in data mining projects. GSTF Journal on Computing (JoC), 4(1), 66.
Metadata
Title
A Review and Future Direction of Business Analytics Project Delivery
Author
Deanne Larson
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-319-93299-6_7

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