Intersection of AI and business intelligence in data-driven decision-making:
"In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and s...
Saved in:
| Other Authors: | , , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
2024.
|
| Series: | Advances in computational intelligence and robotics (ACIR) book series.
|
| Subjects: | |
| Online Access: | DE-863 |
| Summary: | "In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive.Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success."-- |
| Physical Description: | 22 PDFs (xix, 492 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9798369352892 |
| Access: | Restricted to subscribers or individual electronic text purchasers. |
Staff View
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| 245 | 0 | 0 | |a Intersection of AI and business intelligence in data-driven decision-making |c Arul Kumar Natarajan, Mohammad Gouse Galety, Celestine Iwendi, Deepthi Das, Achyut Shankar, editors. |
| 246 | 3 | 3 | |a Intersection of artificial intelligence and business intelligence in data-driven decision-making |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c 2024. | |
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| 490 | 1 | |a Advances in computational intelligence and robotics (ACIR) book series | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Foreword -- Preface -- Chapter 1. Evolution of AI in Business Intelligence -- Chapter 2. IoT and Blockchain Integration for Enhanced AI-Driven Business Intelligence -- Chapter 3. Enhancing Data-Driven Decision-Making: The Role of Decision Tree Algorithm at the Intersection of AI and Business Intelligence -- Chapter 4. Harnessing Sustainable Innovation: Integrating Business Intelligence Into Entrepreneurial Practices -- Chapter 5. Fraud Detection and Risk Management Using AI in Business Intelligence -- Chapter 6. Leveraging Natural Language Processing for Enhanced Text Analysis in Business Intelligence -- Chapter 7. Scrutinizing Consumer Sentiment on Social Media and Data-Driven Decisions for Business Insights: Fusion of Artificial Intelligence (AI) and Business Intelligence (BI) Foster Sustainable Growth -- Chapter 8. Sentiment Analysis With NLP: A Catalyst for Sales in Analyzing the Impact of Social Media Ads and Psychological Factors Online -- Chapter 9. Leveraging Unsupervised Machine Learning to Optimize Customer Segmentation and Product Recommendations for Increased Retail Profits -- Chapter 10. Social Media Insights Into Consumer Behavior -- Chapter 11. Analyzing Public Concerns on Mpox Using Natural Language Processing and Text Mining Approaches -- Chapter 12. Elevating Medical Imaging: AI-Driven Computer Vision for Brain Tumor Analysis -- Chapter 13. Exploring Artificial Intelligence Techniques for Diabetic Retinopathy Detection: A Case Study -- Chapter 14. Unraveling the Complexity of Thyroid Cancer Prediction: A Comparative Examination of Imputation Methods and ML Algorithms -- Chapter 15. Unveiling the Potential of Large Language Models: Redefining Learning in the Age of Generative AI -- Compilation of References -- About the Contributors -- Index. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive.Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success."-- |c Provided by publisher. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 08/30/2024). | ||
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 0 | |a Business intelligence |x Data processing. | |
| 650 | 0 | |a Decision making |x Statistical methods. | |
| 653 | |a AI, ML for Predictive Analytics in BI. | ||
| 653 | |a Big Data in AI-Powered BI. | ||
| 653 | |a Business Intelligence. | ||
| 653 | |a Case Studies, Best Practices in AI-Powered BI. | ||
| 653 | |a Concepts, Frameworks, and Apps of AI-Powered BI. | ||
| 653 | |a Consumer Sentiment Analysis on Social Media. | ||
| 653 | |a ESG Metrics Integration into BI. | ||
| 653 | |a Ethics in AI-Powered BI: Privacy, Fairness, Transparency. | ||
| 653 | |a Fraud Detection, Risk Management with AI. | ||
| 653 | |a Impact of Social Media Ads on Online Shopping. | ||
| 653 | |a NLP for Text Analytics in BI. | ||
| 653 | |a Optimizing Supply Chains with AI. | ||
| 653 | |a Personalization, CRM in AI-Powered BI. | ||
| 653 | |a Real-time Analytics with AI in BI. | ||
| 653 | |a Social Media Analysis for Consumer Behavior. | ||
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Das, Deepthi |e editor. | |
| 700 | 1 | |a Galety, Mohammad Gouse |d 1976- |e editor. | |
| 700 | 1 | |a Iwendi, Celestine |e editor. | |
| 700 | 1 | |a Natarajan, Arul Kumar |e editor. | |
| 700 | 1 | |a Shankar, Achyut |e editor. | |
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| contents | Foreword -- Preface -- Chapter 1. Evolution of AI in Business Intelligence -- Chapter 2. IoT and Blockchain Integration for Enhanced AI-Driven Business Intelligence -- Chapter 3. Enhancing Data-Driven Decision-Making: The Role of Decision Tree Algorithm at the Intersection of AI and Business Intelligence -- Chapter 4. Harnessing Sustainable Innovation: Integrating Business Intelligence Into Entrepreneurial Practices -- Chapter 5. Fraud Detection and Risk Management Using AI in Business Intelligence -- Chapter 6. Leveraging Natural Language Processing for Enhanced Text Analysis in Business Intelligence -- Chapter 7. Scrutinizing Consumer Sentiment on Social Media and Data-Driven Decisions for Business Insights: Fusion of Artificial Intelligence (AI) and Business Intelligence (BI) Foster Sustainable Growth -- Chapter 8. Sentiment Analysis With NLP: A Catalyst for Sales in Analyzing the Impact of Social Media Ads and Psychological Factors Online -- Chapter 9. Leveraging Unsupervised Machine Learning to Optimize Customer Segmentation and Product Recommendations for Increased Retail Profits -- Chapter 10. Social Media Insights Into Consumer Behavior -- Chapter 11. Analyzing Public Concerns on Mpox Using Natural Language Processing and Text Mining Approaches -- Chapter 12. Elevating Medical Imaging: AI-Driven Computer Vision for Brain Tumor Analysis -- Chapter 13. Exploring Artificial Intelligence Techniques for Diabetic Retinopathy Detection: A Case Study -- Chapter 14. Unraveling the Complexity of Thyroid Cancer Prediction: A Comparative Examination of Imputation Methods and ML Algorithms -- Chapter 15. Unveiling the Potential of Large Language Models: Redefining Learning in the Age of Generative AI -- Compilation of References -- About the Contributors -- Index. |
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| illustrated | Not Illustrated |
| indexdate | 2025-03-07T12:04:12Z |
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| isbn | 9798369352892 |
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| spelling | Intersection of AI and business intelligence in data-driven decision-making Arul Kumar Natarajan, Mohammad Gouse Galety, Celestine Iwendi, Deepthi Das, Achyut Shankar, editors. Intersection of artificial intelligence and business intelligence in data-driven decision-making Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global 2024. 22 PDFs (xix, 492 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier Advances in computational intelligence and robotics (ACIR) book series Includes bibliographical references and index. Foreword -- Preface -- Chapter 1. Evolution of AI in Business Intelligence -- Chapter 2. IoT and Blockchain Integration for Enhanced AI-Driven Business Intelligence -- Chapter 3. Enhancing Data-Driven Decision-Making: The Role of Decision Tree Algorithm at the Intersection of AI and Business Intelligence -- Chapter 4. Harnessing Sustainable Innovation: Integrating Business Intelligence Into Entrepreneurial Practices -- Chapter 5. Fraud Detection and Risk Management Using AI in Business Intelligence -- Chapter 6. Leveraging Natural Language Processing for Enhanced Text Analysis in Business Intelligence -- Chapter 7. Scrutinizing Consumer Sentiment on Social Media and Data-Driven Decisions for Business Insights: Fusion of Artificial Intelligence (AI) and Business Intelligence (BI) Foster Sustainable Growth -- Chapter 8. Sentiment Analysis With NLP: A Catalyst for Sales in Analyzing the Impact of Social Media Ads and Psychological Factors Online -- Chapter 9. Leveraging Unsupervised Machine Learning to Optimize Customer Segmentation and Product Recommendations for Increased Retail Profits -- Chapter 10. Social Media Insights Into Consumer Behavior -- Chapter 11. Analyzing Public Concerns on Mpox Using Natural Language Processing and Text Mining Approaches -- Chapter 12. Elevating Medical Imaging: AI-Driven Computer Vision for Brain Tumor Analysis -- Chapter 13. Exploring Artificial Intelligence Techniques for Diabetic Retinopathy Detection: A Case Study -- Chapter 14. Unraveling the Complexity of Thyroid Cancer Prediction: A Comparative Examination of Imputation Methods and ML Algorithms -- Chapter 15. Unveiling the Potential of Large Language Models: Redefining Learning in the Age of Generative AI -- Compilation of References -- About the Contributors -- Index. Restricted to subscribers or individual electronic text purchasers. "In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive.Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success."-- Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 08/30/2024). Artificial intelligence Data processing. Business intelligence Data processing. Decision making Statistical methods. AI, ML for Predictive Analytics in BI. Big Data in AI-Powered BI. Business Intelligence. Case Studies, Best Practices in AI-Powered BI. Concepts, Frameworks, and Apps of AI-Powered BI. Consumer Sentiment Analysis on Social Media. ESG Metrics Integration into BI. Ethics in AI-Powered BI: Privacy, Fairness, Transparency. Fraud Detection, Risk Management with AI. Impact of Social Media Ads on Online Shopping. NLP for Text Analytics in BI. Optimizing Supply Chains with AI. Personalization, CRM in AI-Powered BI. Real-time Analytics with AI in BI. Social Media Analysis for Consumer Behavior. Electronic books. Das, Deepthi editor. Galety, Mohammad Gouse 1976- editor. Iwendi, Celestine editor. Natarajan, Arul Kumar editor. Shankar, Achyut editor. IGI Global, publisher. Print version: 9798369352885 Advances in computational intelligence and robotics (ACIR) book series. |
| spellingShingle | Intersection of AI and business intelligence in data-driven decision-making Advances in computational intelligence and robotics (ACIR) book series. Foreword -- Preface -- Chapter 1. Evolution of AI in Business Intelligence -- Chapter 2. IoT and Blockchain Integration for Enhanced AI-Driven Business Intelligence -- Chapter 3. Enhancing Data-Driven Decision-Making: The Role of Decision Tree Algorithm at the Intersection of AI and Business Intelligence -- Chapter 4. Harnessing Sustainable Innovation: Integrating Business Intelligence Into Entrepreneurial Practices -- Chapter 5. Fraud Detection and Risk Management Using AI in Business Intelligence -- Chapter 6. Leveraging Natural Language Processing for Enhanced Text Analysis in Business Intelligence -- Chapter 7. Scrutinizing Consumer Sentiment on Social Media and Data-Driven Decisions for Business Insights: Fusion of Artificial Intelligence (AI) and Business Intelligence (BI) Foster Sustainable Growth -- Chapter 8. Sentiment Analysis With NLP: A Catalyst for Sales in Analyzing the Impact of Social Media Ads and Psychological Factors Online -- Chapter 9. Leveraging Unsupervised Machine Learning to Optimize Customer Segmentation and Product Recommendations for Increased Retail Profits -- Chapter 10. Social Media Insights Into Consumer Behavior -- Chapter 11. Analyzing Public Concerns on Mpox Using Natural Language Processing and Text Mining Approaches -- Chapter 12. Elevating Medical Imaging: AI-Driven Computer Vision for Brain Tumor Analysis -- Chapter 13. Exploring Artificial Intelligence Techniques for Diabetic Retinopathy Detection: A Case Study -- Chapter 14. Unraveling the Complexity of Thyroid Cancer Prediction: A Comparative Examination of Imputation Methods and ML Algorithms -- Chapter 15. Unveiling the Potential of Large Language Models: Redefining Learning in the Age of Generative AI -- Compilation of References -- About the Contributors -- Index. Artificial intelligence Data processing. Business intelligence Data processing. Decision making Statistical methods. |
| title | Intersection of AI and business intelligence in data-driven decision-making |
| title_alt | Intersection of artificial intelligence and business intelligence in data-driven decision-making |
| title_auth | Intersection of AI and business intelligence in data-driven decision-making |
| title_exact_search | Intersection of AI and business intelligence in data-driven decision-making |
| title_full | Intersection of AI and business intelligence in data-driven decision-making Arul Kumar Natarajan, Mohammad Gouse Galety, Celestine Iwendi, Deepthi Das, Achyut Shankar, editors. |
| title_fullStr | Intersection of AI and business intelligence in data-driven decision-making Arul Kumar Natarajan, Mohammad Gouse Galety, Celestine Iwendi, Deepthi Das, Achyut Shankar, editors. |
| title_full_unstemmed | Intersection of AI and business intelligence in data-driven decision-making Arul Kumar Natarajan, Mohammad Gouse Galety, Celestine Iwendi, Deepthi Das, Achyut Shankar, editors. |
| title_short | Intersection of AI and business intelligence in data-driven decision-making |
| title_sort | intersection of ai and business intelligence in data driven decision making |
| topic | Artificial intelligence Data processing. Business intelligence Data processing. Decision making Statistical methods. |
| topic_facet | Artificial intelligence Data processing. Business intelligence Data processing. Decision making Statistical methods. Electronic books. |
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