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Journal of Information System Engineering and Business Intelligence (JISEBI) focuses on Information System Engineering and its implementation, Business Intelligence, and its application. JISEBI is an international, peer review, electronic, and open access journal. JISEBI is seeking an original and high-quality manuscript. Information System Engineering is a multidisciplinary approach to all activities in the development and management of information system aiming to achieve organization goals. Business Intelligence (BI) focuses on techniques to transfer raw data into meaningful information for business analysis purposes, such as decision making, identification of new opportunities, and the implementation of business strategy. The goal of BI is to achieve a sustainable competitive advantage for businesses.
Technology and entrepreneurship converge in the digital era, presenting many possibilities and hurdles. One of the most pressing issues facing entrepreneurs is the ability to harness the power of artificial intelligence (AI) to drive innovation and create sustainable businesses. While AI holds immense potential for transforming entrepreneurial ideas across various fields, many individuals and organizations need help understanding its practical applications and implications. Generating Entrepreneurial Ideas With AI offers a comprehensive solution to this challenge. By examining the intersection of AI and entrepreneurship from a multidisciplinary perspective, we provide readers with invaluable insights and strategies for leveraging AI to enhance their entrepreneurial endeavors. This book is designed for students, entrepreneurs, policymakers, and academics. It is a practical guide and roadmap for integrating AI into entrepreneurial practices. Through a series of in-depth analyses and case studies, we demonstrate how AI can effectively identify new business opportunities, optimize operations, and enhance the overall competitiveness of ventures.
This book examines the managerial dimensions of business intelligence (BI) systems. It develops a set of guidelines for value creation by implementing business intelligence systems and technologies. In particular the book looks at BI as a process – driven by a mix of human and technological capabilities – to serve complex information needs in building insights and providing aid in decision making. After an introduction to the key concepts of BI and neighboring areas of information processing, the book looks at the complexity and multidimensionality of BI. It tackles both data integration and information integration issues. Bodies of knowledge and other widely accepted collections of experience are presented and turned into lessons learned. Following a straightforward introduction to the processes and technologies of BI the book embarks on BI maturity and agility, the components, drivers and inhibitors of BI culture and soft BI factors like attention, sense and trust. Eventually the book attempts to provide a holistic view on business intelligence, possible structures and tradeoffs and embarks to provide an outlook on possible developments in BI and analytics.
A very large proportion of commercial and industrial concerns in the UK find their business competitiveness dependent on huge quantities of already installed, legacy IT. Often the nature of their business is such that, to remain competitive, they have to be able to change their business processes. Sometimes the required change is radical and revolutionary, but more often the required change is incremental. For such incremental change, a major systems engineering problem arises. The cost and delay involved in changing the installed IT to meet the changed business requirements is much too high. In order to address this issue the UK Engineering and Physical Science Research Council (EPSRC) set up, in 1996, a managed research programme entitled Systems Engineering for Business Process Change (SEBPC). I was appointed as co-ordinator of the programme. The overall aim of this new managed research programme was to release the full potential of IT as an enabler of business process change, and to overcome the disabling effects which the build-up of legacy systems has on such change. As such, this aim addressed a stated objective of the Information Technology and Computer Science (IT&CS) part of EPSRC to encourage research at a system level.
This book recapitulates the major developments in Decision Support Systems (DSS) over the last 30 years in order to evaluate the research areas of decision making and in which direction the field should proceed. As it attempts to find a consensus about the next steps for the future of DSS research, the book also enforces the trends and new technologies currently in use. The book examines topics such as decision analysis for enterprise systems and non-hierarchical networks, integrated solutions for decision support and knowledge management in distributed environments, decision support system evaluation and analysis through social networks, and e-learning and its application to real environments. It clearly presents the evidence to support their cases and attempts to promote an extensive and objective discussion. In addition, the book also reflects on approaches to dead-end ideas and failures in DSS to better understand the lessons learned. The contributions for this book have been written by thought leaders and influential researchers from the EURO Working Group of Decision Support Systems (EWG-DSS).
This book presents an extensive collection of the recent findings and innovative research in the information system and knowledge engineering domain. Knowledge engineering is a field within artificial intelligence that develops in particular systems that use knowledge, rather than data, to solve many computing problems, that would usually require high levels of human expertise.
Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students.
Business intelligence (BI) has evolved over several years as organizations have extended their online transaction processing (OLTP) capabilities and applications to support their routine operations. With online analytical processing (OLAP), organizations have also established the capability to extract internal and external data from a variety of sources to specifically obtain intelligence about non-routine and often less-structured arrangements. BI therefore refers to applications and technologies that are used to gather, provide access to, and analyze data and information about the operations of an organization. It has the capability of providing comprehensive insight into the more volatile factors affecting the business and its operations, thereby facilitating enhanced decision-making quality and contributing to the creation of business value. Larger and more sophisticated organizations have long been exploiting these capabilities. Business Intelligence for Small and Medium-Sized Enterprises (SMEs) guides SMEs in replicating this experience to provide an agile roadmap toward business sustainability. The book points out that successful BI implementations have generated significant increases in revenue and cost savings, however, the failure rates are also very high. More importantly, it emphasizes that a full range of BI capabilities is not the exclusive purview of large organizations. It shows how SMEs make extensive use of BI techniques to develop the kind of agility endowing them with the organizational capability to sense and respond to opportunities and threats in an increasingly dynamic business environment. It points to the way to a market environment in which smaller organizations could have a larger role. In particular, the book explains that by establishing the agility to leverage internal and external data and information assets, SMEs can enhance their competitiveness by having a comprehensive understanding of the key to an agile roadmap for business sustainability.
Business intelligence applications are of vital importance as they help organizations manage, develop, and communicate intangible assets such as information and knowledge. Organizations that have undertaken business intelligence initiatives have benefited from increases in revenue, as well as significant cost savings.Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications highlights the marriage between business intelligence and knowledge management through the use of agile methodologies. Through its fifteen chapters, this book offers perspectives on the integration between process modeling, agile methodologies, business intelligence, knowledge management, and strategic management.