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Business Analytics The Art Of Modeling With Spreadsheets
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Received: April 4, 2019 / Revised: May 31, 2019 / Accepted: June 5, 2019 / Published: June 10, 2019
Big data and business analytics are trends that positively affect the business world. Previous studies have shown that the amount of data generated in today’s world is growing rapidly. These include structured and unstructured data that inundates organizations every day. Unstructured data includes much of the world’s digital data, including text files, online and social media messages, emails, images, audio, movies, and more. Unstructured data cannot be handled in a relational database management system (RDBMS). Therefore, the proliferation of data requires rethinking the ways of capturing, storing and processing data. This is the role that big data plays. This article therefore aims to increase the attention of organizations and researchers on the different uses and benefits of big data technology. The article discusses and discusses the latest trends, opportunities and shortcomings of big data, which enable organizations to develop successful business strategies and remain competitive. In addition, the review presents various applications of big data and business analytics, the data sources generated in these applications, and their main characteristics. Finally, this review not only highlights the challenges of effectively implementing big data projects, but also highlights open research trends in big data analytics that need more attention. The areas explored in big data show that using big data techniques and tools to effectively manage and use large data sets can provide viable insights that create business value.
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In the late 1980s, database technology, usually classified as online analytical processing (OLAP), was used by enterprise database management systems (RDBMS) to support business decisions and business intelligence. It was originally designed to store large amounts of data in production databases and make them compact and efficient. In the data warehouse, multiple copies exist on multiple database servers called data marks. Databases can be standalone or enterprise databases. From there, data is extracted and loaded into two data analytics systems. Here, data analysts build their own algorithms and manage their work. One of the data systems is related to the statistical analyst and the other to the business user. Although databases do not fail to create business value by providing detailed reports based on complex statistical models [1, 2], it is difficult to continuously deliver data to the Internet and it takes a long time to obtain results [3]. Additionally, there are limits to the amount of data that can be stored in the system. Furthermore, today’s data structures are constantly being created, which makes it difficult to process large data sets. Recently, big data has gained attention in the fields of government, industry, science, engineering, healthcare and medicine, finance and business [4]. Accordingly, the data generated in these areas are characterized by large volumes, the inability to separate them into related database management systems, and rapid data generation, capture, and processing [1]. Therefore, the main challenge for all organizations, industries and other business sectors is how to develop appropriate methods and process such large amounts of data to ensure effective and efficient decision-making.
Recently, big data and business analytics techniques have been developed and implemented to analyze large amounts of data generated by various business organizations. Therefore, every business needs instant insight into the ever-increasing amount of business data. Real-time data analytics help organizations see the past and predict the future. That’s the beauty of flow analysis, knowing what happened (describing), understanding why it happened (diagnosing), predicting what will happen next, and finally deciding how to influence future events (prescribing). These four analytical flavors described in Part 3 of this article have significant business benefits, but are difficult to implement and use step by step. Big data opportunities are not just for greater efficiency in business operations. There are also great opportunities to grow the economy and improve the quality of life in communities. There are many ways that big data analytics can improve business and business performance. These include improving health care, educational attainment, national security, and support for good governance [5, 6]. It also helps policy makers understand policies that provide a safe zone for investors, helps waste managers identify and share the types of waste that will be generated from a given region. Waste collection source. . In addition, the education manager is deploying big data and business analytics technologies to evaluate teacher performance and improve work efficiency. Furthermore, location data from mobile networks can be used in traffic management to avoid traffic jams in large cities or to better plan public transport systems.
The purpose of this study is to conduct a detailed review of big data and business analytics methods to improve business decisions, technology insights, applications, and open research challenges. Furthermore, this study attempts to explore the great benefits that data brings to businesses in developed countries and how they can be replicated by traditional business organizations. In addition, the study explores the various challenges facing big data analytics in terms of data security, governance, ownership, control and compliance.
Various researchers and industries have been studying big data analysis and implementation for decades. This is because big data is used in various fields such as healthcare systems, business decision making, educational development, Internet optimization, traffic forecasting and financial services. Therefore, many studies and reviews have been published recently on big data analysis, implementation, and related techniques. Imba et al. [7] investigated hardware and software parameters to develop better data analysis. In addition, Hashim et al. [4] proposed a taxonomy and intersection of cloud computing and big data analytics. However, these studies analyze big data such as data availability, transparency, and data size in cloud computing, software, and hardware parameters to implement big data analysis. The research does not discuss the main data analysis tools, their strengths and weaknesses. Recently, open source tools for big data analysis, big data implementation and iterative clustering algorithms for big data analysis have been proposed [8, 9, 10]. Sai et al. [8] described big data analysis techniques in terms of data mining and knowledge discovery. The authors discuss data mining algorithms that can be extended to analyze big data. However, big data analysis challenges, applications, current tools and data sources are not discussed in depth. Lancet and others. [9] presented open source tools for big data analysis, their advantages and disadvantages.
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However, research has been limited to tools when other dimensions of effective big data implementation have not been adequately addressed. Recently, a closely related study was presented by Muhammad et al. [11] and discuss big data technologies, software and open source tools for big data analysis. In contrast, our study differs from their review in several ways. First, the current review provides a broader perspective highlighting recent trends in big data and business analytics development. Second, we discuss platforms, open source tools, and their strengths and weaknesses. Third, this study identifies big data success factors for analytics teams, their core tasks, and the challenges of implementing analytics in organizations. Fourth, this study introduces the latest data sources and applications of big data and business analytics. Finally, this review highlights and discusses clear lines of research in big data and analytics. The show is a show for adults
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