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Organizational business intelligence and decision making using big data analytics

机译:使用大数据分析的组织商业智能和决策

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Business intelligence helps gather essential information from a wide variety of unstructured data and convert them into actionable information that allows firms to make informed policy decisions and improve business efficiency and productivity. The challenges facing any organization in business intelligence and decision-making include plan failure, lack of preparation, resource failure, and risk-taking capability.This paper proposes a framework of Optimized Data Management using Big Data Analytics (ODM-BDA) to increase the intelligent organizational effectiveness and decision-making analysis in organizations. A backtracking method is introduced in business intelligence and decision-making environments to enhance plan failure and risk-taking ability. The steep optimized strategy is incorporated in the ODM-BDA framework to improve the training plan and manage money. The research data provided an essential set of acceptance research for these models in increasing the relative level of effectiveness and performance.The simulation analysis is performed based on true positive analysis, performance analysis, error analysis, and accuracy analysis, proving the proposed framework's reliability.
机译:商业智能帮助从各种非结构化数据中收集基本信息,并将其转换为可操作的信息,允许公司进行知情政策决策,提高业务效率和生产力。任何组织在商业智能和决策中面临的挑战包括计划失败,缺乏准备,资源故障和风险的能力。本文提出了使用大数据分析(ODM-BDA)的优化数据管理框架来增加组织中智能组织效率和决策分析。在商业智能和决策环境中引入了回溯方法,以提高计划失败和承担风险的能力。陡峭的优化策略纳入ODM-BDA框架,以改善培训计划并管理资金。该研究数据为这些模型提供了一系列基本的验收研究,即在增加有效性和性能的相对水平。基于真正的阳性分析,性能分析,误差分析和准确性分析来执行仿真分析,证明了提出的框架可靠性。

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