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Business Intelligence As An Essential Technique For Advancement Companies
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By Ashraf Bany Mohammad Ashraf Bany Mohammad Scilit Preprints.org Google Scholar 1, Manaf Al-Okaily Manaf Al-Okaily Scilit Preprints.org Google Scholar 2, * , Mohammad Al-Majali Mohammad Al-Majali Scilit Preprints.org Google Scholar 1 and Ra ‘ed Masa’deh Ra’ed Masa’deh Scilit Preprints.org Google Scholar 1
Received: September 11, 2022 / Revised: October 13, 2022 / Accepted: October 14, 2022 / Published: October 17, 2022
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(This article belongs to the special issue New directions of open innovation and business model with digital transformation)
This study aims to investigate the factors that influence the use of business intelligence and analytics (BIA) in the banking industry. Based on an extensive literature review, 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 the Jordanian Arab Bank. The results revealed not only the critical impact of the existence of a data and technology infrastructure, but also the importance and availability of management and human resources support and capabilities. This study suggests that, most importantly, successful planning for business intelligence and analytics must go beyond the technological aspects to realize the full benefits of such technology, especially in the banking sector. Yet, we argue that more research needs to be done, 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 a competitive advantage, while improving business operations and product development and improving customer relationships are strengthened [1, 2]. BIA plays an even more important role in the banking sector by enabling experts and managers to make better, accurate, timely and relevant decisions to increase the bank’s productivity and profitability and meet the various regulatory and environmental dimensions. of this sector .
BIA today is a trendy topic and a mandatory condition for creating an excellent corporate image, which is in line with the implementation of a successful plan regarding the intensive use of technology. This thus supports business decisions and gains a competitive advantage in today’s dynamic environment, which requires extraordinary efforts to spend huge budgets on research and development (R&D). Data is a focal point and is considered the fuel of the future as it can be efficiently processed and used effectively in supporting risk events and decisions that can be highly reflected in companies’ performance [ 4 , 5 ].
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Business Intelligence (BI) is an umbrella term that includes structures, tools, databases, applications and methodologies to analyze data by transforming raw data into meaningful and useful information to support the decisions of business managers . Banking areas such as branch performance, sales, risk assessment, electronic banking, customer segmentation and retention are generally excellent for applying various business concepts and analyses, technologies and tools including data mining (DM), data warehousing and decision making. support systems (DSS). Therefore, top management must continuously focus on solving challenging problems and exploiting opportunities for the banking industry to succeed and excel in today’s business environment. This requires automated support for managerial decision making, driving the need for decision support, business intelligence and analytics systems . Business Intelligence systems (BIS) evolved from technical solutions that provide data integration, analytical capabilities and data mining to provide stakeholders at different levels with valuable information to make their decisions effectively and successfully.
In this regard, data analytics can contribute to solving and developing banking problems and achieving the best results for decision-making . Managers cannot see the correlation between different variables in business data because the amount of data is constantly increasing and significant. Moreover, managers need additional work to reach a conclusion regarding customer behavior patterns and wants and needs. In addition, a lot of additional work is needed to understand the right customers, retain them and recruit new ones; Consequently, through data analytics, business intelligence helps executives and product managers to identify different categories of customers, develop products or services tailored to customer needs, define competitive and pricing strategy, improve revenue management, increase sales and expand the customer segment .
Researchers have defined business intelligence as the ability of companies to think, plan, predict, solve problems, understand, come up with new ways to appropriately improve business and decision-making processes, enable effective actions and achieve business goals to help create and achieve . Accordingly, the processes, technologies, tools, applications, data, databases, dashboards, scorecards, and online analytical processing (OLAP) are argued to play a role in enabling the capabilities that define business intelligence . BIA practices and tools are seen as critical enablers of data-driven decision making and provide the framework and support the needs of banks to make accurate and fact-based decisions and conduct successful and distinctive business .
Even though research interest in the use of business intelligence and analytics in the banking sector is increasing, previous studies supporting this area cannot be considered very developed. Furthermore, the existing literature does not provide satisfactory evidence on what factors influence the use of business intelligence and analytics, in addition to the inconsistencies in the results. At the same time, the use of business intelligence and analytics by banks remains high, especially now that vast data sets on customers are available that can help with better decision-making in this context. Thus, this study attempts to explore the factors influencing the use of business intelligence and analytics in the banking sector, in an effort to help this sector plan for better use and adoption of business intelligence and analytics.
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The next section presents an overview of the key related literature, and then section 3 explains the theoretical framework and hypothesis development. Data collection and methodology are presented in Chapter 4, while Chapter 5 presents the research results. After discussing the results in Chapter 6, Chapter 7 presents the research contributions, and the last section presents the conclusion along with limitations and directions for further research.
The term business intelligence (BI) became popular in the 1990s and can be considered a term that encompasses a wide range of processes and software used to collect, analyze and disseminate data in the interest of better decision making [12 ], including infrastructures, tools, technologies, databases, applications and methodologies. BI has been used as an umbrella term to describe concepts and methods for improving business decision making using evidence-based support systems .
The main goals of business intelligence are to enable interactive and easy access to diverse data, data processing and transformation to create meaningful and valuable information that can support business managers and analysts in decision making [13, 14]. Technically, BI includes several software solutions and technologies, from extract transform and load (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 it in DW and the generated reports using OLAP, data mining. Other reporting tools are accessible to end users through the user interface .
In the extraction process (ETL), ETL packages extract data from internal and external sources, eliminate data errors and redundancies, and provide customized data for access and analysis that can be loaded into the data warehouse . The 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 help make decisions within the organization . Later, and depending on the organization’s required OLAP technologies – which are multi-dimensional models that include relational databases and report writing – data mining will deliver business reporting for
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