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Amalgamation of Business Intelligence with Corporate Strategic Management

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Decision Intelligence Analytics and the Implementation of Strategic Business Management

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Abstract

Strategic management is one of the most important aspects that lead to companies’ success. The process of building a complex strategy needs a lot of time and effort, especially with the increasing speed of changes in the markets and the speed of obtaining information. It was necessary to use new tools that help decision makers. Here the role of business intelligence has emerged, which provides all that is necessary for the decision makers to be in a state of readiness to build strategies or modify them based on real-time data.

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References

  1. X. Belle, Peng, Review of business intelligence through data analysis. BIJ 21(2), 300–311 (2014). https://doi.org/10.1108/BIJ-08-2012-0050

    Article  Google Scholar 

  2. R. Heath, Prediction machines: The simple economics of artificial intelligence. J. Inform. Technol. Case Appl. Res. 21(3–4), 163–166 (2019). https://doi.org/10.1080/15228053.2019.1673511

    Article  Google Scholar 

  3. Larson, Chang, A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inform. Manage. 36(5), 700–710 (2016). https://doi.org/10.1016/j.ijinfomgt.2016.04.013

    Article  Google Scholar 

  4. Vercellis, Business intelligence: data mining and optimization for decision making (Wiley, London, 2009)

    Book  Google Scholar 

  5. L. Arnott, Song, Patterns of business intelligence systems use in organizations. Decis. Support Syst. 97, 58–68 (2017)

    Article  Google Scholar 

  6. D. Wiltbank, Read, D. Sarasvathy, What to do next? The case for non-predictive strategy. Strat. Manage. J. 27(10), 981–998 (2006). https://doi.org/10.1002/smj.555

    Article  Google Scholar 

  7. Thevenet, Salinesi, Aligning IS to organization’s strategy: The InStAl method BT, in Advanced Information Systems Engineering, (Springer, Berlin, 2007), pp. 203–217

    Chapter  Google Scholar 

  8. M.-R. Bragge, Nurmi, Tanner, A repeatable e-collaboration process based on thinklets for multi-organization strategy development. Group Decis. Negot. 16(4), 363–379 (2007). https://doi.org/10.1007/s10726-006-9055-5

    Article  Google Scholar 

  9. F. Yean, K. Yahya, The influence of human resource management practices and career strategy on career satisfaction of insurance agents. Int. J. Business Soc. 14(2), 193 (2013)

    Google Scholar 

  10. Tomlin, Wang, Operational strategies for managing supply chain disruption risk, in The Handbook of Integrated Risk Management in Global Supply Chains, (Wiley, Oxford, 2011), pp. 79–101. https://doi.org/10.1002/9781118115800

    Chapter  Google Scholar 

  11. J. Teece, A capability theory of the firm: An economics and (strategic) management perspective. N. Z. Econ. Pap. 53(1), 1–43 (2019). https://doi.org/10.1080/00779954.2017.1371208

    Article  Google Scholar 

  12. P. Rony, Florinda, Knowledge management as a factor for the formulation and implementation of organization strategy. J. Knowl. Manage. 21(2), 308–329 (2017). https://doi.org/10.1108/JKM-02-2016-0068

    Article  Google Scholar 

  13. Gurel, Tat, SWOT analysis: A theoretical review. J. Int. Soc. Res. 10(51), 51–66 (2017). https://doi.org/10.17719/jisr.2017.1832

    Article  Google Scholar 

  14. M. Abdel-Basset, Smarandache, An extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-making. Symmetry 10, 4 (2018). https://doi.org/10.3390/sym10040116

    Article  Google Scholar 

  15. C. Merino, S. Rivas, Piattini, A data quality in use model for big data. Futur. Gener. Comput. Syst. 63, 123–130 (2016). https://doi.org/10.1016/j.future.2015.11.024

    Article  Google Scholar 

  16. Kahlawi, An ontology driven ESCO LOD quality enhancement. Int. J. Adv. Comp. Sci. Appl. 11, 60 (2020). https://doi.org/10.14569/IJACSA.2020.0110308

    Article  Google Scholar 

  17. M. Smith, A. Roster, L. Golden, S. Albaum, A multi-group analysis of online survey respondent data quality: Comparing a regular USA consumer panel to MTurk samples. J. Bus. Res. 69(8), 3139–3148 (2016). https://doi.org/10.1016/j.jbusres.2015.12.002

    Article  Google Scholar 

  18. Choi, Luo, Data quality challenges for sustainable fashion supply chain operations in emerging markets: Roles of blockchain, government sponsors and environment taxes. Transp. Res. 131, 139–152 (2019). https://doi.org/10.1016/j.tre.2019.09.019

    Article  Google Scholar 

  19. R. CĂ´rte-Real, Oliveira, Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value? Inf. Manag. 57(1), 103141 (2020). https://doi.org/10.1016/j.im.2019.01.003

    Article  Google Scholar 

  20. G. Juddoo, Duquenoy, Windridge, Data governance in the health industry: investigating data quality dimensions within a big data context. Appl. Syst. Innov. 1, 4 (2018). https://doi.org/10.3390/asi1040043

    Article  Google Scholar 

  21. T. Taleb. A. El Kassabi. Serhani, Dssouli, and Bouhaddioui, Big data quality: A quality dimensions evaluation, in 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 2016, pp. 759–765

    Google Scholar 

  22. Z. Fang, J. Elmore, A. Chien, UDP: A programmable accelerator for extract-transform-load workloads and more, in Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, 2017, pp. 55–68

    Google Scholar 

  23. A. Yulianto, Extract transform load (ETL) process in distributed database academic data warehouse. J. Comp. Sci. Inform. Technol. 4(2), 61–68 (2019). https://doi.org/10.11591/aptikom.j.csit.36

    Article  Google Scholar 

  24. E. Pearlson, S. Saunders, F. Galletta, Managing and Using Information Systems: A Strategic Approach (Wiley, New York, 2019)

    Google Scholar 

  25. S. Tohir, Kusrini, Sudarmawan, On-Line Analytic Processing (OLAP) modeling for graduation data presentation, in 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2017, pp. 132–135

    Google Scholar 

  26. Quiceno et al., Scenario analysis for strategy design: A case study of the Colombian electricity industry. Energ. Strat. Rev. 23, 57–68 (2019). https://doi.org/10.1016/j.esr.2018.12.009

    Article  Google Scholar 

  27. Cairns, Wright, in Evaluating the Effectiveness of Scenario Interventions Within Organizations BT-Scenario Thinking: Preparing Your Organization for the Future in an Unpredictable World, ed. by G. Cairns, G. Wright, (Springer, Cham, 2018), pp. 247–255. https://doi.org/10.1007/978-3-319-49067-0_11

    Chapter  Google Scholar 

  28. F. Hartmann, R. Moawad, L. Traon, GreyCat: Efficient what-if analytics for data in motion at scale. Inf. Syst. 83, 101–117 (2019). https://doi.org/10.1016/j.is.2019.03.004

    Article  Google Scholar 

  29. C. Carvalho, Cazarini, Gerolamo, Manufacturing in the fourth industrial revolution: A positive prospect in sustainable manufacturing. Proc. Manuf. 21, 671–678 (2018). https://doi.org/10.1016/j.promfg.2018.02.170

    Article  Google Scholar 

  30. Malik, Creating competitive advantage through source basic capital strategic humanity in the industrial age 4.0. Int. Res. J. Adv. Eng. Sci. 4(1), 209–215 (2019)

    Google Scholar 

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Correspondence to Adham Kahlawi .

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Kahlawi, A. (2022). Amalgamation of Business Intelligence with Corporate Strategic Management. In: Jeyanthi, P.M., Choudhury, T., Hack-Polay, D., Singh, T.P., Abujar, S. (eds) Decision Intelligence Analytics and the Implementation of Strategic Business Management. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-82763-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-82763-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82762-5

  • Online ISBN: 978-3-030-82763-2

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