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.

Technological developments in recent years have been tremendous. This evolution is visible in companies through technological equipment, computerized procedures, and management practices associated with technologies. One of the management practices that is visible is related to business intelligence and analytics (BI&A). Concepts such as data warehousing, key performance indicators (KPIs), data mining, and dashboards are changing the business arena. This book aims to promote research related to these new trends that open up a new field of research in the small and medium enterprises (SMEs) area. Features Focuses on the more recent research findings occurring in the fields of BI&A Conveys how companies in the developed world are facing today's technological challenges Shares knowledge and insights on an international scale Provides different options and strategies to manage competitive organizations Addresses several dimensions of BI&A in favor of SMEs

Smart watches, autonomous vehicles, and talking robots are now an everyday part of life for many regions of the world. The digital revolution has now permeated nearly every facet of our existence. This surge in technological advancement has ushered in what economists term an "innovation economy." In this era, the synergy between technology and business intelligence propels groundbreaking innovations, fostering entrepreneurial ventures across various sectors. These ventures encompass an array of industries, including agriculture, fast-moving consumer goods, hospitality, cultural and indigenous products, to name just a few. While these entrepreneurial endeavors bring forth creativity and contribute to circular economies within communities, not all manage to navigate the challenging environment successfully. This raises a pressing concern – how can entrepreneurs harness the power of business intelligence and innovation to achieve sustainability and a competitive edge in today's complex business landscape? Applying Business Intelligence and Innovation to Entrepreneurship brings a definitive solution to academicians, researchers, and students who seek a comprehensive understanding of the dynamic interplay between entrepreneurship, business intelligence, and innovation. By dissecting various types of organizations, from small and medium-sized enterprises (SMEs) to technology-based startups like Fintech, digital marketing, and community-driven initiatives, this book paints a vivid picture of the entrepreneurial ecosystem. It delves deep into the symbiotic relationship between local wisdom and innovation, shedding light on how entrepreneurs can seize opportunities presented by disruptive and unpredictable phenomena.

The convergence of modern technology and social dynamics have shaped the very fabric of today’s organizations, making the role of Business Intelligence (BI) profoundly significant. Data-Driven Business Intelligence Systems for Socio-Technical Organizations delves into the heart of this transformative realm, offering an academic exploration of the tools, strategies, and methodologies that propel enterprises toward data-driven decision-making excellence. Socio-technical organizations, with their intricate interplay between human and technological components, require a unique approach to BI. This book embarks on a comprehensive journey, revealing how BI tools empower these entities to decipher the complexities of their data landscape. From user behavior to social interactions, technological systems to environmental factors, this work sheds light on the multifaceted sources of information that inform organizational strategies. Decision-makers within socio-technical organizations leverage BI insights to discern patterns, spot trends, and uncover correlations that influence operations and the intricate social dynamics within their entities. Research covering real-time monitoring and predictive analytics equips these organizations to respond swiftly to demands and anticipate future trends, harnessing the full potential of data. The book delves into their design, development, and architectural nuances, illuminating these concepts through case studies. This book is ideal for business executives, entrepreneurs, data analysts, marketers, government officials, educators, and researchers.

Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings incorporates emerging concepts, methods, models, and relevant applications of business intelligence systems within problem contexts of healthcare and other organizational boundaries. Featuring coverage on a broad range of topics such as rise of embedded analytics, competitive advantage, and strategic capability, this book is ideally designed for business analysts, investors, corporate managers, and entrepreneurs seeking to advance their understanding and practice of business intelligence.

Global Business Intelligence refers to an organization’s ability to gather, process and analyze pertinent international information in order to make optimal business decisions in a timely manner. With a challenging economic and geopolitical environment, companies and executives need to be adept at information gathering in order to manage emerging challenges and gain competitive advantages. This book Global Business Intelligence assembles a cast of international experts and thought leaders and explores the implications of business intelligence on contemporary management. Global Business Intelligence will be a key resource for researchers, academics, students and policy makers alike in the fields of International Business & Management, Business Strategy, and Geopolitics as well as related disciplines like Political Science, Economics, and Geography.

Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.

This book highlights interdisciplinary insights, latest research results, and technological trends in Business Intelligence and Modelling in fields such as: Business Intelligence, Business Transformation, Knowledge Dissemination & Implementation, Modeling for Logistics, Business Informatics, Business Model Innovation, Simulation Modelling, E-Business, Enterprise & Conceptual Modelling, etc. The book is divided into eight sections, grouping emerging marketing technologies together in a close examination of practices, problems and trends. The chapters have been written by researchers and practitioners that demonstrate a special orientation in Strategic Marketing and Business Intelligence. This volume shares their recent contributions to the field and showcases their exchange of insights.