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A review and future direction of agile, business intelligence, analytics and data science

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  • Larson, Deanne
  • Chang, Victor

Abstract

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.

Suggested Citation

  • Larson, Deanne & Chang, Victor, 2016. "A review and future direction of agile, business intelligence, analytics and data science," International Journal of Information Management, Elsevier, vol. 36(5), pages 700-710.
    • Handle: RePEc:eee:ininma:v:36:y:2016:i:5:p:700-710
    DOI: 10.1016/j.ijinfomgt.2016.04.013
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    References listed on IDEAS

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    1. Solomon Negash & Paul Gray, 2008. "Business Intelligence," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 2, chapter 45, pages 175-193, Springer.
    2. Frada Burstein & Clyde Holsapple, 2008. "Handbook on Decision Support Systems 2," International Handbooks on Information Systems, Springer, number 978-3-540-48716-6, November.
    3. Newman, Russell & Chang, Victor & Walters, Robert John & Wills, Gary Brian, 2016. "Model and experimental development for Business Data Science," International Journal of Information Management, Elsevier, vol. 36(4), pages 607-617.
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    Cited by:

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    2. Erkan Bayraktar & Ekrem Tatoglu & Arafat Salih Aydiner & Dursun Delen, 2024. "Business Analytics Adoption and Technological Intensity: An Efficiency Analysis," Information Systems Frontiers, Springer, vol. 26(4), pages 1509-1526, August.
    3. Rehman, Muhammad Habib ur & Chang, Victor & Batool, Aisha & Wah, Teh Ying, 2016. "Big data reduction framework for value creation in sustainable enterprises," International Journal of Information Management, Elsevier, vol. 36(6), pages 917-928.
    4. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    5. Ranjan Chaudhuri & Sheshadri Chatterjee & Demetris Vrontis & Alkis Thrassou, 2024. "Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture," Annals of Operations Research, Springer, vol. 339(3), pages 1757-1791, August.
    6. Gupta, Manjul & George, Joey F. & Xia, Weidong, 2019. "Relationships between IT department culture and agile software development practices: An empirical investigation," International Journal of Information Management, Elsevier, vol. 44(C), pages 13-24.
    7. Falana, Gbenga Ayodele & Olusola Esther (PhD) & Dagunduro, Muyiwa Emmanuel, 2023. "Effect of Big Data on Accounting Information Quality in Selected Firms in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(3), pages 789-806, March.
    8. Yousafzai, Abdullah & Chang, Victor & Gani, Abdullah & Noor, Rafidah Md., 2016. "Directory-based incentive management services for ad-hoc mobile clouds," International Journal of Information Management, Elsevier, vol. 36(6), pages 900-906.
    9. Basile, Luigi Jesus & Carbonara, Nunzia & Pellegrino, Roberta & Panniello, Umberto, 2023. "Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making," Technovation, Elsevier, vol. 120(C).
    10. Maël Schnegg & Klaus Möller, 2022. "Strategies for data analytics projects in business performance forecasting: a field study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 33(2), pages 241-271, June.
    11. Božič, Katerina & Dimovski, Vlado, 2019. "Business intelligence and analytics for value creation: The role of absorptive capacity," International Journal of Information Management, Elsevier, vol. 46(C), pages 93-103.
    12. Bashir, Raja Sehrab & Lee, Sai Peck & Khan, Saif Ur Rehman & Chang, Victor & Farid, Shahid, 2016. "UML models consistency management: Guidelines for software quality manager," International Journal of Information Management, Elsevier, vol. 36(6), pages 883-899.
    13. Sariyer, Gorkem & Kumar Mangla, Sachin & Chowdhury, Soumyadeb & Erkan Sozen, Mert & Kazancoglu, Yigit, 2024. "Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies," Journal of Business Research, Elsevier, vol. 181(C).
    14. Ariyaluran Habeeb, Riyaz Ahamed & Nasaruddin, Fariza & Gani, Abdullah & Targio Hashem, Ibrahim Abaker & Ahmed, Ejaz & Imran, Muhammad, 2019. "Real-time big data processing for anomaly detection: A Survey," International Journal of Information Management, Elsevier, vol. 45(C), pages 289-307.
    15. Maria Hoffmann Jensen & John Stouby Persson & Peter Axel Nielsen, 2023. "Measuring benefits from big data analytics projects: an action research study," Information Systems and e-Business Management, Springer, vol. 21(2), pages 323-352, June.
    16. Madalina Cuc & Anca Gabriela Petrescu, 2024. "IoT Modeling for Digital Enterprises and Decision Analysis: A Descriptive Presentation," International Journal of Innovation in the Digital Economy (IJIDE), IGI Global, vol. 15(1), pages 1-8, January.
    17. Darehshiri, Mahsa & Ghaemi Asl, Mahdi & Babatunde Adekoya, Oluwasegun & Shahzad, Umer, 2022. "Cross-spectral coherence and dynamic connectedness among contactless digital payments and digital communities, enterprise collaboration, and virtual reality firms," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    18. Jimenez-Marquez, Jose Luis & Gonzalez-Carrasco, Israel & Lopez-Cuadrado, Jose Luis & Ruiz-Mezcua, Belen, 2019. "Towards a big data framework for analyzing social media content," International Journal of Information Management, Elsevier, vol. 44(C), pages 1-12.
    19. Maryia Zaitsava & Elona Marku & Maria Chiara Guardo & Azar Shahgholian, 2023. "A fine-grained perspective on big data knowledge creation: dimensions, insights, and mechanism from a pilot study," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 547-573, June.
    20. Iqbal, Kiram, 2023. "Acceptance conditions of algorithmic decision support in management," Junior Management Science (JUMS), Junior Management Science e. V., vol. 8(4), pages 887-925.
    21. Hassani, Abdeslam & Mosconi, Elaine, 2022. "Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    22. Muhammad Ovais Ahmad & Iftikhar Ahmad & Nripendra P. Rana & Iqra Sadaf Khan, 2023. "An Empirical Investigation on Business Analytics in Software and Systems Development Projects," Information Systems Frontiers, Springer, vol. 25(2), pages 917-927, April.

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