EFFECTIVE DISTRIBUTED DECISION-MAKING APPROACH FOR SMART BUSINESS INTELLIGENCE TECHNOLOGY

Jeshwanth Reddy Machireddy

Authors

Jeshwanth Reddy Machireddy Senior software Developer, IT, KForce, Madison, Wisconsin, USA Author

Keywords:

Availability, Business Intelligence, Business Performance, Data, Data-Driven, Decision-Making, Effectiveness

Abstract

The processing of many types of information is required for the various business decisions that are made at different levels. When seen from this perspective, the utilisation of appropriate tools will contribute to the process of making good business decisions. These kinds of decisions are intended to be supported in an efficient manner by the framework of the business intelligence system that has been presented. To find out how effective business intelligence (BI) is for companies, this study set out to identify the determinants. Findings from the study point to the need for a business intelligence (BI) efficacy model to enhance advisory services. Making sure decision-makers get the right information at the right time in the right format is the whole point of this paradigm. This study used a quantitative research approach and purposive sampling to recruit research participants from a telecoms company. Business intelligence (BI) departments are directly related to an organization's capacity for making sound decisions. The parts of the business intelligence effectiveness model propose focusing on certain areas to improve the organization's information flow, provide easier access to information, make better decisions, and increase productivity.

References

Aben TAE, van der Valk W, Roehrich JK, Selviaridis K (2021) Managing information asymmetry in public–private relationships undergoing a digital transformation: The role of contractual and relational governance. Int J Oper Prod Manag 41(7):1145–1191. https://doi.org/10.1108/IJOPM-09-2020-0675

Bokrantz J, Skoogh A, Berlin C, Wuest T, Stahre J (2020) Smart maintenance: an empirically grounded conceptualization. Int J Prod Econ 223:107534. https://doi.org/10.1016/j.ijpe.2019.107534

Côrte-Real N, Ruivo P, Oliveira T (2020) 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. https://doi.org/10.1016/j.im.2019.01.003

Joseph J, Gaba V (2020) Organizational structure, information processing, and decision-making: a retrospective and road map for research. Acad Manag Ann 14(1):267–302. https://doi.org/10.5465/annals.2017.0103

Khayer A, Talukder MdS, Bao Y, Hossain MdN (2020) Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: a dual-stage analytical approach. Technol Soc 60:101225. https://doi.org/10.1016/j.techsoc.2019.101225

Knauer T, Nikiforow N, Wagener S (2020) Determinants of information system quality and data quality in management accounting. J Manag Control 31(1):97–121. https://doi.org/10.1007/s00187-020-00296-y

Mandal P, Jain T (2021) Partial outsourcing from a rival: quality decision under product differentiation and information asymmetry. Eur J Oper Res 292(3):886–908. https://doi.org/10.1016/j.ejor.2020.11.018

Maroufkhani P, Wan Ismail WK, Ghobakhloo M (2020) Big data analytics adoption model for small and medium enterprises. J Sci Technol Policy Manag 11(4):483–513. https://doi.org/10.1108/JSTPM-02-2020-0018

Yu W, Zhao G, Liu Q, Song Y (2021b) Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: an organizational information processing theory perspective. Technol Forecast Soc Change 163:120417. https://doi.org/10.1016/j.techfore.2020.120417

Zelt S, Schmiedel T, vom Brocke J (2018) Understanding the nature of processes: an information-processing perspective. Bus Process Manag J 24(1):67–88. https://doi.org/10.1108/BPMJ-05-2016-0102

Zhu S, Song J, Hazen BT, Lee K, Cegielski C (2018) How supply chain analytics enables operational supply chain transparency: an organizational information processing theory perspective. Int J Phys Distrib Logist Manag 48(1):47–68. https://doi.org/10.1108/IJPDLM-11-2017-0341

Alaimo C, Kallinikos J (2022) Organizations decentered: data objects, technology and knowledge. Organ Sci 33(1):19–37. https://doi.org/10.1287/orsc.2021.1552

Dubey R, Gunasekaran A, Childe SJ, Fosso Wamba S, Roubaud D, Foropon C (2021) Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. Int J Prod Res 59(1):110–128. https://doi.org/10.1080/00207543.2019.1582820

European Commission (2022) Digital economy and society index (DESI) 2022 | Shaping Europe’s digital future. https://digital-strategy.ec.europa.eu/en/library/digital-economy-and-society-index-desi-2022

Hair JF, Hult GTM, Ringle CM, Sarstedt M (2021) A primer on partial least squares structural equation modeling (PLS-SEM) (Third Edition). SAGE Publications. https://uk.sagepub.com/en-gb/eur/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book270548

Kaplan SN, Sorensen M (2021) Are CEOs different? J Finance 76(4):1773–1811. https://doi.org/10.1111/jofi.13019

Karaboga T, Zehir C, Tatoglu E, Karaboga HA, Bouguerra A (2022) Big data analytics management capability and firm performance: the mediating role of data-driven culture. RMS. https://doi.org/10.1007/s11846-022-00596-8

Labro E, Lang M, Omartian JD (2022) Predictive analytics and centralization of authority. J Account Econ 101526. https://doi.org/10.1016/j.jacceco.2022.101526

Liberatore MJ, Wagner WP (2022) Simon’s decision phases and user performance: an experimental study. J Comput Inf Syst 62(4):667–679. https://doi.org/10.1080/08874417.2021.1878476

Qiao Y (2022) To delegate or not to delegate? On the quality of voluntary corporate financial disclosure. Rev Manager Sci, 1–36. https://doi.org/10.1007/s11846-022-00576-y.