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.

"The chapters in this volume offer useful case studies, technical roadmaps, lessons learned, and a few prescriptions to ‘do this, avoid that.’" —From the Foreword by Joe LaCugna, Ph.D., Enterprise Analytics and Business Intelligence, Starbucks Coffee Company With the growing barrage of "big data," it becomes vitally important for organizations to make sense of this data and information in a timely and effective way. That’s where analytics come into play. Research shows that organizations that use business analytics to guide their decision making are more productive and experience higher returns on equity. Big Data and Business Analytics helps you quickly grasp the trends and techniques of big data and business analytics to make your organization more competitive. Packed with case studies, this book assembles insights from some of the leading experts and organizations worldwide. Spanning industry, government, not-for-profit organizations, and academia, they share valuable perspectives on big data domains such as cybersecurity, marketing, emergency management, healthcare, finance, and transportation. Understand the trends, potential, and challenges associated with big data and business analytics Get an overview of machine learning, advanced statistical techniques, and other predictive analytics that can help you solve big data issues Learn from VPs of Big Data/Insights & Analytics via case studies of Fortune 100 companies, government agencies, universities, and not-for-profits Big data problems are complex. This book shows you how to go from being data-rich to insight-rich, improving your decision making and creating competitive advantage. Author Jay Liebowitz recently had an article published in The World Financial Review. www.worldfinancialreview.com/?p=1904

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

The main purpose of this book is to investigate, explore and describe approaches and methods to facilitate data understanding through analytics solutions based on its principles, concepts and applications. But analyzing data is also about involving the use of software. For this, and in order to cover some aspect of data analytics, this book uses software (Excel, SPSS, Python, etc) which can help readers to better understand the analytics process in simple terms and supporting useful methods in its application.

As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.

Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians.

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.

A famous Information Techonology´s phrase said: … the computing created soluctions for problem its own computing created. Once thing is true. Day by day new vocabulary is brought for business´world by Marketers, CIO, Programmers, so son.. I created this Official Dictionary to keep you updated to be able to build bridge among corporation´s teams. Let´s cross it.. Peter Druck said: don´t fight against Marketing. You will lose. With that in mind, I am preparing you to talk the same language to get the best result for your career and business. I presented clear definition for this new vocabulary for a new digital world. It covers the following areas: ERP CRM UX (User experience) & Usability Business Intelligence Data Warehouse Analytics Big Data Customer Experience Call Center & Customer service Digital Marketing and in the Third edition (Mar/2019) I added terms for Telecommunication This book is part of the CRM and Customer Experience Trilogy called CX Trilogy which aims to unite the worldwide community of CX, Customer Service, Data Science and CRM professionals. I believe that this union would facilitate the contracting of our sector and profession, as well as identifying the best professionals in the market. The CX Trilogy consists of 3 books and one Dictionary: 1st) 30 Advice from 30 greatest professionals in CRM and customer service in the world 2nd) The Book of all Methodologies and Tools to Improve and Profit from Customer Experience and Service 3rd) Data Science and Business Intelligence - Advice from reputable Data Scientists around the world and plus, the book: The Official Dictionary for Internet, Computer, ERP, CRM, UX, Analytics, Big Data, Customer Experience, Call Center, Digital Marketing and Telecommunication: The Vocabulary of One New Digital World

The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.

Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. What You Will Learn See machine learning in OBIEE Master the fundamentals of machine learning and how it pertains to BI and advanced analytics Gain an introduction to Oracle R Enterprise Discover the practical considerations of implementing machine learning with OBIEE Who This Book Is For Analytics managers, BI architects and developers, and data scientists.