Organizational business intelligence and decision making using big data analytics

This chapter introduces the implementation of Business Intelligence (BI), thus explaining the application overview of BI, the components of BI, the practical implementation of BI, the business value of BI, the trends in implementing BI, and the guidelines for implementing BI. BI is a broad category of business applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping business enterprise users make better business decisions. BI enlarges business performance, thus leading to higher level of efficiency, better quality outputs, better marketing decisions, and lessened risk of business failure in order to gain a competitive advantage in the global business environments. It is important to create and develop a BI system to enable the useful transformation of information into the valuable knowledge for enhancing BI in organizations. Implementing BI will increase organizational performance and achieve business goals in modern business.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using B usiness intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords : Mobile business intelligence, business intelligence, big data, analytic

In this study, we deploy review centric research of User Satisfaction, Cost Reducing, and Time Optimization impacting decision making on whether to implement Business Intelligence in organizations. The purpose of this paper is to present a literature review to examine the association between Business Intelligence and these three different factors. The primary goal of this study was to investigate the user’s satisfaction, cost reducing, and time optimization implications in order to choose Business Intelligence as a business tool. A comprehensive literature review of business intelligence created a theoretical foundation for this paper. Using the grounded theory, a model was developed and evaluated based on how User Satisfaction, Cost Reducing and Time Optimization impacts managerial decision-making using Business Intelligence. Using data from JSTOR and Academic Search Premier databases a new model is presented to encapsulate the highly dynamic interaction of user satisfaction, cost reducing, and time optimization with business intelligence to provide elements to consider the implementation of BI in a firm. This model highlights three key aspects that administrators consider in order to determine the possibility of taking Business Intelligence as an instrument in their daily duties. The theoretical model is limited to three factors only, which could be extended in future studies on this topic. Moreover, this model has been discussed using a theoretical perspective whereas practical contributions has been given less attention. This study provides an exciting opportunity to advance our knowledge of business intelligence to efficiently and flexibly help companies make the right decisions in real time, grasp business opportunities, and gain competitive advantage.

This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture (BASOA), and applying BASOA to business intelligence, where our surveyed data analysis showed that the proposed BASOA is viable for developing business intelligence and enterprise information systems. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.KeywordsBig data analyticse-commerceBusiness intelligenceIntelligent agents

This paper examines big data and the opportunities it presents for improved business intelligence and decision making. Big data comes in multiple forms. It can be structured, semi-structured, or unstructured. The opportunity it presents is that there is so much of it and it is readily available to organizations. Organizations use big data for business intelligence (BI). They can apply analytics in BI activities to assess big data in order to gain new insights and opportunities for decision making. The problem is that oftentimes the data is of poor quality and it contains personal information. This paper explores these issues and examines the importance of effective data management in facilitating sound business intelligence. The Master Data Management methodology is reviewed and the importance of management support in its deployment is emphasized. With the advent of new sources of big data from IoT devices, the need for even more management involvement is stressed to ensure that organizational BI yield sound decisions and that use of data are in compliance with new regulations.

ABSTRACTThis article provides valuable information on the chances and the possibilities of Business Intelligence applying in organizations. Three theories—the Resource-Based View, Maturity Models, and Critical Success Factors—were used to investigate Business Intelligence issues. They provided a comprehensive view on Business Intelligence. Using a semi-structured interview method, the results from 20 organizations applying Business Intelligence are presented. Finally, based on the analysis of the literature and on the qualitative surveys, conclusions for future research in Business Intelligence are provided.

Purpose Textile and contemporary apparel manufacturers are adopting and integrating cutting-edge technologies to reduce their impact on the environment and gain an advantage in the marketplace. Most previous studies have ignored business intelligence systems (BIS), notably in the textile and apparel industry (T&A), in favor of looking at the larger picture of how big data would affect retail and distribution in a company. This is especially true for the T&As.Design/methodology/approach The authors report that they conducted 14 semi-structured interviews with 12 international luxury tourism service providers. In this case, researchers use snowball features and systematic techniques to select participants. A qualitative content analysis strategy is used to capture the focus of the interviews.Findings Problems with T&A company sustainability, opportunities to increase value creation via use of industry-leading business intelligence (BI) solutions and perceived roadblocks to BIS adoption were all found by the poll. Garment retail and distribution sector has benefited greatly from the increased use of Industry 4.0 technologies, especially those that provide better BI solutions. Determine the extent to which industry participation slows down or speeds up the process. The Company Information System (BIS) will help convince non-tech-savvy business owners of the financial, economic and environmental benefits of adopting certain technologies developed as part of the industry 4.0 movement.Research limitations/implications The authors of this research claim theirs is one of the first to investigate what variables affect the uptake of BIS, ultimately hoping to find out how BIS may be used by T&A businesses to tackle environmental issues through the use of Industry 4.0 technologies. The purpose of this study was to see whether BIS might aid T&A firms with their sustainability issues.Practical implications In the last several years, there has been a meteoric rise in interest in big data and business analytics among firms and educational institutions alike. This paper tries to introduce readers to the concept of business analytics in a way that is both academic and accessible, considering both the present and future of the field. This paper begins with a quick introduction, followed by a summary of the three distinct forms of predictive modeling discussed.Originality/value In an effort to help aspiring analytics professionals, they have identified, categorized and evaluated the nine distinct players that are now active in the analytics market. Following this, they will provide a high-level summary of the many different research projects currently being worked on by their group.

The application of artificial intelligence in business is increasing as it enables the automation of business processes and faster decision-making. Artificial intelligence can be used in marketing, production, sales, and finance. The key advantage of its use lies in improving business efficiency and reducing costs. Business automation leads to faster operations and cost reduction. In the field of customer relations, artificial intelligence can analyze large amounts of user behavior data to suggest personalized products and services, thereby creating a better user experience. However, the use of artificial intelligence in business also comes with challenges. One of the biggest challenges is the ethical use of artificial intelligence in business, as well as the shortage of experts in this field. Additionally, many aspects of AI implementation in business remain unexplored and unclear, creating opportunities for various types of fraud. Whenever a new technology like artificial intelligence emerges, there will always be individuals who seek to exploit it for personal gain through fraudulent activities.

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

The growing importance of big data has headed enterprises to advance their big data analytics capability to strengthen their firm performance. This study tests how big data capability impact on business intelligence infrastructure to achieve firm performance measures such as operational performance and marketing performance. This study is based on the recent literature on the knowledge-based view, big data capability, IT capability, and business intelligence. The primary survey of 272 responses from Chinese firms’ IT managers and big data analysts are used to uncover the relationship in the proposed model. The finding shows that the big data analytics capability significantly impacts on business intelligence infrastructure that in turn positively impact on operational performance and marketing performance. Further, the business intelligence infrastructure partially mediates between big data analytics capability and operational performance, and fully mediates between big data analytics capability and marketing performance. This research contributes to the information systems literature such as big data analytic capability, business intelligence, and firm performance measures, and thus offers grounds to extend more widespread studies in this field. This study adds to the literature on the theory and practical bases for big data capability and business intelligence infrastructure.

In the era of big data, organizations are increasingly leveraging advanced analytics to extract valuable insights from vast and complex datasets. Management Information Systems (MIS) play a crucial role in collecting, processing, and analyzing data to support decision-making. Integrating big data analytics into MIS can enhance business intelligence and improve organizational performance. This study aims to investigate the integration of big data analytics into MIS and its impact on business intelligence. Specifically, the study seeks to identify the challenges and opportunities associated with this integration and explore best practices for implementation. A qualitative research approach was adopted for this study. Data was collected through semi-structured interviews based on a survey of over 312 information technology (IT) professionals from 21 industries with IT managers and business analysts from January 2022 to December 2023. Thematic analysis was used to analyze the data and identify key themes related to integrating big data analytics into MIS. The findings indicate that integrating big data analytics into MIS can significantly improve business intelligence. According to the respondents, on average, there was a 30% increase in the accuracy of decision-making processes after the integration. Additionally, organizations reported a 25% reduction in operational costs and a 20% increase in revenue as a result of the integration. Moreover, 70% of the respondents agreed that integrating big data analytics into MIS improved their organization's overall performance. Integrating big data analytics into MIS offers numerous benefits, including improved decision-making, cost savings, and revenue growth. However, organizations must overcome challenges such as data privacy and security concerns and the need for skilled personnel to manage and analyze big data. Overall, this study highlights the importance of integrating big data analytics into MIS for enhancing business intelligence and achieving organizational success.

Nowadays, most tax authorities in the world are utilizing various technologies, including big data analytics to be more efficient and effective in their works. Utilization of big data analytics and business intelligence by tax authorities in developing countries can be used to help identify the non-compliance taxpayers and support other activities in order to explore potential tax revenues. Directorate General of Taxes, Indonesian Tax Autbority, uses combination of compliance risk management application and business intelligence. There are nine types of compliance risk management applications. Currently, seven types of compliance risk management applications have been launched and used by the directorate general of taxes. The last two compliance risk management applications will be released in the near future. The series of nine compliance risk management applications will be supported by four business intelligence. Those compliance risk management and business intelligence applications will lead directorate general of taxes to be data driven organization.

Nowadays a lot of massive data is stored and typically, the data itself contains a lot of non-trivial but useful information. Data mining techniques can be used to discover this information which can help the companies for decision-making. However, in real life applications, data is massive and is stored over distributed sites. One of my major research topics is to protect privacy over this kind of data. Previously, the important characteristics, issues and challenges related to management of the large amount of data has been explored. Various open source data analytics frameworks that deal with large amount of Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed. Digital universe is flooded with huge amount of data generated by number of users worldwide. These data are of diverse in nature, come from various sources and in many forms. To keep with the desire to store and analyze ever larger volumes of complex data, relational databases vendors have delivered specialized analytical platforms that come in many shapes and sizes from software only to analytical services that run in third party hosted environments. In addition new technologies have emerged to address exploding volumes of complex data, including web traffic, social media content and machine generated data including sensor data, global positioning system data.Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data has become a prominent research field, especially when it comes to decision making and data analysis. However, Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. Another challenge is that data collection may not have enough accuracy which will lead to a non-consistent analysis that can critically affect the decision based on this analysis. Moreover, it is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is; current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Since data is growing at a huge speed making it difficult to handle such large amount of data (Exabyte). The main difficulty in handling such large amount of data is because that the volume is increasing rapidly in comparison to the computing resources. The Big data term which is being used now a days is kind of misnomer as it points out only the size of the data not putting too much of attention to its other existing properties.If data is to be used to make accurate decisions in time it becomes necessary that it should be available in accurate, complete and timely manner. This makes the data management and governance process bit complex adding the necessity to make Data open and make it available to government agencies in standardized manner with standardized APIs, metadata and formats thus leading to better decision making, business intelligence and productivity improvements.This paper presents a discussion and evaluation for the most prominent techniques used in the processes of data collection and analysis in order to identify the privacy defects in them that affects the accuracy of big data. Depending on the results of this analysis, recommendations were provided for improving data collection and analysis techniques that will help to avoid if not all then most of the problems facing the use of big data in decision making. Keywords: Big Data, Big Data Challenges, Big Data Accuracy, Big Data Collection, Big Data Analytics.

The global Business Intelligence (BI) market grew by 7.3% in 2016 according to the Gartner report (2017). Today, organizations require better use of data and analytics to support their business decisions. Internet power and business trend changes have provided a broad term for data analytics - Big Data. To be able to handle it and leverage a value of having access to Big Data, organizations have no other choice than to get proper systems implemented and working. However, traditional methods are not efficient for changing business needs. Long time between project start and go-live causes a gap between initial solution blueprint and actual user requirements at the end of the project. This article presents the latest market trends in BI systems implementation by comparing agile with traditional methods. It presents a case study provided in a large telecommunications company (350 BI users) and the results of a pilot research provided in the three large companies: media, digital, and insurance. Both studies prove that agile methods might be more effective in BI projects from an end-user perspective and give first results and added value in a much shorter time compared to a traditional approach.