Integration Challenges For Analytics Business Intelligence And Data Mining
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Integration Challenges for Analytics, Business Intelligence, and Data Mining
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.
Real-world Data Mining
Annotation Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance.Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials.Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.
Adapting and Mitigating Environmental, Social, and Governance Risk in Business
Environmental, social, and corporate governance (ESG) risk considers the nonfinancial risks that could arise in a business, such as sustainability, brand reputation, legal aspects, ethics, and more. As businesses all have their own risk profiles, there is a need for risk management and mitigation that is unique for each company. Because of this variability, the study on ESG risk factors and motives of incorporating the ESG perspective into business models are crucial yet challenging. Therefore, it is important to understand how companies are adapting and mitigating ESG risk in diverse types of businesses. Adapting and Mitigating Environmental, Social, and Governance Risk in Business examines processes in enterprises that can increase the sustainability of business models and their coherence with the assumptions of the concept of sustainable development and ESG risk. Furthermore, the book explores how enterprises operating in different sectors are adapting their business models towards sustainability in order to create sustainable value. This book is a valuable tool for managers, executives, entrepreneurs, practitioners, academicians, researchers, and graduate students in finance, business, and management.