Improving Business Intelligence through Machine Learning Algorithms

Minsang D. Tamang, Vinod Kumar Shukla, S. Anwar, Ritu Punhani
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引用次数: 3

Abstract

Machine learning (ML) is the study of various algorithms that help in making computers learn and work on itself without any specific programming done beforehand. It is a branch of artificial intelligence in which devices are built or programmed in order to function mostly on its own. In order to be able to predict or make decisions, mathematical models are built by the algorithms. Since machine learning is close to computational statistics, it is useful in developing computers to access data and derive the required outputs. Business Intelligence (BI) consists of the technologies and applications that are used to collect, integrate, analyze raw data and present information of a particular business. These systems help in making strategies and decisions in order to get better results for the future by comparing the past and present performances. It also helps organizations identify the market trends and business problems that have to be looked upon. Along with many advantages like boosting productivity and accountability, it also has a few drawbacks such as it being costly, complex and takes a long time to be implemented. In this paper, we have discussed how combining machine learning with business intelligence would be a true game-changer for companies who can afford it. It can not only help improve operational processes and provide better customer services, but also analyze large amounts of data and achieve real-time data analysis, as well as prevent the number of cybercrimes as these systems can learn to differentiate between what is the threat and what is not. Paper also represents the algorithms that are being used by the company in the process, and if it has been beneficial to them or not. One of the algorithms of machine learning that is used is linear regression. It is easy to implement and is used for purposes such as predicting real estate prices, financial performances and traffic. Cluster analysis is also used for the purpose of understanding the relationship between consumers and the products they mostly search for. In this paper a case study has been presented to demonstrate the business operations that take place in a major airport retailer in the region, and how they take the advantage of Business Intelligence to get better outcomes.
通过机器学习算法改进商业智能
机器学习(ML)是对各种算法的研究,这些算法帮助计算机在没有事先进行任何特定编程的情况下自我学习和工作。它是人工智能的一个分支,在这个分支中,设备被建造或编程,以便主要依靠自己的功能。为了能够预测或做出决定,算法建立了数学模型。由于机器学习接近于计算统计学,因此它在开发计算机以访问数据并获得所需输出方面非常有用。商业智能(BI)由用于收集、集成、分析原始数据和呈现特定业务信息的技术和应用程序组成。这些系统有助于制定战略和决策,以便通过比较过去和现在的表现为未来取得更好的结果。它还可以帮助组织识别必须考虑的市场趋势和业务问题。除了提高生产力和问责制等许多优点之外,它也有一些缺点,比如成本高、复杂、需要很长时间才能实现。在本文中,我们讨论了如何将机器学习与商业智能相结合,这对那些负担得起的公司来说将是一个真正的游戏规则改变者。它不仅可以帮助改进操作流程和提供更好的客户服务,还可以分析大量数据并实现实时数据分析,以及防止网络犯罪的数量,因为这些系统可以学习区分哪些是威胁,哪些不是。纸张也代表了公司在这个过程中使用的算法,以及它是否对他们有益。使用的机器学习算法之一是线性回归。它很容易实现,并用于预测房地产价格、财务业绩和交通等目的。聚类分析还用于了解消费者与他们最常搜索的产品之间的关系。在本文中,一个案例研究展示了该地区一家主要机场零售商的业务运营,以及他们如何利用商业智能来获得更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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