Business Intelligence Developer Basic Electric Motors Task Summary

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Business Intelligence Developer Basic Electric Motors Task Summary – Open Access Policy Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publishing Ethics Article Processing Fees Awards Testimonials

All articles he publishes are immediately available worldwide under an open access license. Reuse of all or part of the article, including figures and tables, does not require special permission. For articles published under the Creative Commons CC BY open access license, any part of the article may be reused without permission, provided the original article is clearly cited. See https:///openaccess for more information.

Business Intelligence Developer Basic Electric Motors Task Summary

The articles represent state-of-the-art research with great potential for major impact in the field. The submission must be a comprehensive original article that incorporates multiple techniques or approaches, provides an outlook on future research directions, and describes possible research applications.

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Papers are submitted by individual invitation or recommendation of scientific editors and must receive positive feedback from reviewers.

Editor’s Choice articles are based on recommendations from scientific journal editors from around the world. The editors select a small number of articles recently published in the journal that they believe will be of particular interest to readers or relevant to a particular research area. The aim is to provide a brief overview of some of the most exciting work published in the various research areas of the journal.

By Ashraf Bany Mohammad Ashraf Bany Mohammad Scilit Google Scholar 1, Manaf Al-Okaily Manaf Al-Okaily Scilit Google Scholar 2, * , Mohammad Al-Majali Mohammad Al-Majali Scilit Google Scholar 1 and Ra ‘ed Masa’deh Ra’ed Masa’deh Scilit Google Scholar 1

Received: 11 September 2022 / Revised: 13 October 2022 / Accepted: 14 October 2022 / Published: 17 October 2022

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(This article belongs to the special issue New directions of open innovation and business model with digital transformation)

The purpose of this study is to examine the factors influencing the use of business intelligence and analytics (BIA) in the banking sector. Based on a comprehensive review of the literature, a theoretical model was developed to investigate the impact of three key factors on the adoption and use of business intelligence and analytics in the banking sector, namely technological, organizational and environmental factors. The study used the Statistical Package for the Social Sciences (SPSS) to analyze data collected from 120 employees of a Jordanian Arab bank. The results revealed the critical impact of not only the existence of data and technology infrastructure, but also the importance and availability of management and human resource support and capacity. This study suggests that, more importantly, successful business intelligence and analytics planning should go beyond technological aspects to fully reap the benefits of such technology, particularly in the banking sector. Nevertheless, we argue that more research needs to be conducted, especially in the context of developing countries, to fully understand how banking sectors can successfully implement and use business intelligence and analytics.

Business intelligence and analytics (BIA) is considered one of the most critical technologies, systems, practices, and applications that help organizations develop a deeper understanding of business data and gain competitive advantage while improving operations and product development and strengthening customer relationships [1]. , 2]. BIA plays an even more important role in the banking sector, as it enables professionals and managers to make better, accurate, timely and appropriate decisions to increase the productivity and profitability of the bank and to meet various regulatory and environmental dimensions. of this sector [3].

BIA is a trending topic these days and a necessary prerequisite for creating an outstanding holistic image that is consistent with the implementation of a successful plan on the large-scale use of technology. Thus, it supports business decisions and gains a competitive edge in today’s dynamic environment, which requires extraordinary efforts to devote huge budgets to research and development (R&D). Data is the focal point and is considered the fuel of the future, as it can be effectively processed and used to support risk events and decisions that can have a strong impact on the performance of companies [4, 5].

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Business Intelligence (BI) is an umbrella term that includes structures, tools, databases, applications and methodologies for data analysis by converting raw data into meaningful and useful information to support the decisions of business managers [6]. Banking areas such as branch performance, sales, risk assessment, e-banking, customer segmentation and retention are generally excellent for applying various business concepts and analytics, technologies and tools, including data mining (DM), data warehousing and decision making. support systems (DSS). Therefore, top management must constantly focus on solving challenging problems and seizing opportunities for the success and success of the banking sector in today’s business environment. This requires computer support for managerial decision making, which means the need for decision support, business intelligence and analytical systems [7]. Business Intelligence Systems (BIS) have evolved from technical solutions that provide data integration, analytical capabilities, and data mining to provide stakeholders at various levels with valuable information for effective and efficient decision-making.

In this regard, data analysis can contribute to solving and developing banking problems and achieving the best results in decision-making [8]. Managers are unable to see correlations between different variables in business data, as the amount of data is constantly increasing, and it is significant. What’s more, managers need additional work to come to a conclusion regarding the behavior pattern and the wants and needs of the customers. In addition, a lot of additional work is required to understand and retain the right customers and acquire new ones; as a result, business intelligence through data analysis helps managers and product managers identify different categories of customers, develop products or services that are aligned with customer needs, define competition and pricing strategy, improve revenue management, increase sales and expand the customer segment [5].

Researchers have defined business intelligence as the ability of companies to think, plan, predict, solve problems, understand, design new ways to adequately improve business and decision-making processes, enable effective actions and help create and achieve business goals [9]. Accordingly, processes, technologies, tools, applications, data, databases, dashboards, indicators, and online analytical processing (OLAP) are expected to play a role in enabling the capabilities that define business intelligence [10]. BIA practices and tools are considered key enablers of data-driven decision-making, providing the framework and supporting banks’ needs to make accurate and fact-based decisions and operate successfully and distinctly [11].

Although research interest in the application of business intelligence and analytics in the banking sector is growing, past studies supporting this area cannot be considered highly developed. In addition, the existing literature does not provide satisfactory evidence on what factors influence the use of business intelligence and analytics, in addition to inconsistencies in results. At the same time, the use of business intelligence and analytics in banks remains high, especially with the availability of huge sets of customer data that can help make better decisions in this context. Therefore, this study seeks to examine what factors influence the use of business intelligence and analytics in the banking sector in order to help the sector plan for better use and adoption of business intelligence and analytics.

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The next section presents a review of key related literature, and then Section 3 explains the theoretical framework and hypothesis development. Data collection and methodology are presented in Chapter 4, and Chapter 5 presents the results of the research. After discussing the results in Chapter 6, Chapter 7 presents the research contributions, and the last chapter presents the conclusions along with limitations and directions for further research.

The term business intelligence (BI) was popularized in the 1990s and could be considered as a term that covers a wide range of processes and software used to collect, analyze and disseminate data in the interest of better decision making [12], which it includes infrastructures, tools, technologies, databases, applications and methodologies. BI is used as an umbrella term to describe concepts and methods for improving business decision-making using fact-based support systems [5].

The main goals of business intelligence are to enable interactive and easy access to diverse data, processing and transformation of data to create meaningful and valuable information that can support business managers and analysts in making decisions [13, 14]. BI technically includes various software solutions and technologies from Extraction Transformation and Loading (ETL) tools, data warehouse, OLAP technology, data extraction, reporting applications and an interface that supports user and web access [1, 15]. In its basic form, the BIA process will extract the necessary data using ETL technologies, store them in DW, and generate reports using OLAP, data mining. Other reporting tools are accessed by end users through a user interface [16].

In process extraction (ETL), ETL packages extract data from internal and external sources, eliminate data errors and redundancies, and provide tailored data for access and analysis that is loaded into a data warehouse [17]. A data warehouse is a type of database in which data is collected from different databases in different business units and then organized and validated to aid decision-making within the organization [18]. Later, and according to the required OLAP technologies in organizations – which are multidimensional models that include relational databases and report writing – data mining will generate business reporting for

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