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Research challenges and opportunities in business analytics

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  • Dursun Delen
  • Sudha Ram

Abstract

There are plenty of definitions proposed for business analytics – some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert data into actionable insight for better and faster decision-making. Although the name is new, its purpose has been around for several decades, characterised under different labels. Largely driven by the need in the business world, business analytics has become one of the most active research areas in academics and in industry/practice. The Journal of Business Analytics is created to establish a dedicated home for analytics researchers to publish their research outcomes. Covering all facets of business analytics (descriptive/diagnostic, predictive, and prescriptive), the journal is destined to become the pinnacle for rigorous and relevant analytics research manuscripts. Herein we provide an overview of research challenges and opportunities for business analytics to lay the groundwork for this new journal.

Suggested Citation

  • Dursun Delen & Sudha Ram, 2018. "Research challenges and opportunities in business analytics," Journal of Business Analytics, Taylor & Francis Journals, vol. 1(1), pages 2-12, January.
    • Handle: RePEc:taf:tjbaxx:v:1:y:2018:i:1:p:2-12
    DOI: 10.1080/2573234X.2018.1507324
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    Citations

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    Cited by:

    1. Ron Berman & Ayelet Israeli, 2022. "The Value of Descriptive Analytics: Evidence from Online Retailers," Marketing Science, INFORMS, vol. 41(6), pages 1074-1096, November.
    2. Steffen Kurpiela & Frank Teuteberg, 2024. "Linking business analytics affordances to corporate strategic planning and decision making outcomes," Information Systems and e-Business Management, Springer, vol. 22(1), pages 33-60, March.
    3. Luqman, Adeel & Wang, Liangyu & Katiyar, Gagan & Agarwal, Reeti & Mohapatra, Amiya Kumar, 2024. "Unpacking associations between positive-negative valence and ambidexterity of big data. Implications for firm performance," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    4. Tarkan Tan & M. Hakan Akyüz & Bengisu Urlu & Santiago Ruiz, 2024. "Stop Auditing and Start to CARE: Paradigm Shift in Assessing and Improving Supplier Sustainability," Interfaces, INFORMS, vol. 54(3), pages 241-263, May.
    5. Cankaya, Burak & Topuz, Kazim & Delen, Dursun & Glassman, Aaron, 2023. "Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents," Omega, Elsevier, vol. 120(C).

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