Data-driven business intelligence systems for socio-technical organizations:
"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 tr...
Saved in:
| Other Authors: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
2024.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | "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. "-- |
| Physical Description: | 29 PDFs (490 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9798369312117 |
| Access: | Restricted to subscribers or individual electronic text purchasers. |
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| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c 2024. | |
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Section 1. Introduction to digital transformation and business intelligence. Chapter 1. Navigating the fourth industrial revolution: empowering socio-technical organizations with data-driven business intelligence systems ; Chapter 2. Digital transformation and business intelligence (BI) in the industry 4.0 (i 4.0) age -- Section 2. Artificial intelligence and machine learning in business. Chapter 3. How business intelligence and data analytics can leverage business ; Chapter 4. Scalable textile business intelligence with operational data collection and analytical processing ; Chapter 5. Synergizing success: harnessing AI-infused business intelligence to propel exponential business growth ; Chapter 6. Software engineering approach for designing apparel business data analytics ; Chapter 7. The adoption of business intelligence as a competitive strategy among SMEs: a developing country study -- Section 3. Innovations in healthcare intelligence. Chapter 8. ADHD healthcare intelligence: a synergistic approach with big data and AI for better screening, diagnosis, treatment, and monitoring ; Chapter 9. Intersecting minds and machines: a review of behavioral intentions towards health intelligence systems ; Chapter 10. Transforming medical tourism through an integrated business intelligence platform: data-driven insights and healthcare industry impact -- Section 4. AI-augmented technological applications in specific fields. Chapter 11. Deep-mental workload intelligent system: an AI-augmented system to predict employee mental workload based on EEG data using deep learning ; Chapter 12. Inventory classification and management system using machine learning and analytical dashboard: a case study of a manufacturing industry ; Chapter 13. Narrative threads and cinematic connections using intelligent systems to enhance movie recommendations with market basket analysis and advanced algorithms -- Section 5. Social and ethical considerations in technology. Chapter 14. Individual privacy should be an institutional value: socio-technical design of university data collection systems ; Chapter 15. Sentiment analysis of user reactions to meta's threads launch and Twitter's X renaming: a comparative study using distilbert and machine learning -- Section 6. Sustainability initiatives. Chapter 16. Green theatre index: measuring the effectiveness of climate-smart approaches and benchmarking | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "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. "-- |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 04/10/2024). | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Business intelligence. | |
| 653 | |a Artificial Intelligence. | ||
| 653 | |a Behavioral Analytics. | ||
| 653 | |a Big Data Management. | ||
| 653 | |a Business Intelligence. | ||
| 653 | |a Data Analytics. | ||
| 653 | |a Data-driven Decision Making. | ||
| 653 | |a Decision-Making. | ||
| 653 | |a Deep Learning. | ||
| 653 | |a Future of BI in Organizations. | ||
| 653 | |a Machine Learning. | ||
| 653 | |a Predictive Analytics. | ||
| 653 | |a Real-Time Business Intelligence. | ||
| 653 | |a Recommender Systems. | ||
| 653 | |a Socio-Technical Dynamics. | ||
| 653 | |a Socio-Technical Organizations. | ||
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Keikhosrokiani, Pantea |d 1982- |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
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| contents | Section 1. Introduction to digital transformation and business intelligence. Chapter 1. Navigating the fourth industrial revolution: empowering socio-technical organizations with data-driven business intelligence systems ; Chapter 2. Digital transformation and business intelligence (BI) in the industry 4.0 (i 4.0) age -- Section 2. Artificial intelligence and machine learning in business. Chapter 3. How business intelligence and data analytics can leverage business ; Chapter 4. Scalable textile business intelligence with operational data collection and analytical processing ; Chapter 5. Synergizing success: harnessing AI-infused business intelligence to propel exponential business growth ; Chapter 6. Software engineering approach for designing apparel business data analytics ; Chapter 7. The adoption of business intelligence as a competitive strategy among SMEs: a developing country study -- Section 3. Innovations in healthcare intelligence. Chapter 8. ADHD healthcare intelligence: a synergistic approach with big data and AI for better screening, diagnosis, treatment, and monitoring ; Chapter 9. Intersecting minds and machines: a review of behavioral intentions towards health intelligence systems ; Chapter 10. Transforming medical tourism through an integrated business intelligence platform: data-driven insights and healthcare industry impact -- Section 4. AI-augmented technological applications in specific fields. Chapter 11. Deep-mental workload intelligent system: an AI-augmented system to predict employee mental workload based on EEG data using deep learning ; Chapter 12. Inventory classification and management system using machine learning and analytical dashboard: a case study of a manufacturing industry ; Chapter 13. Narrative threads and cinematic connections using intelligent systems to enhance movie recommendations with market basket analysis and advanced algorithms -- Section 5. Social and ethical considerations in technology. Chapter 14. Individual privacy should be an institutional value: socio-technical design of university data collection systems ; Chapter 15. Sentiment analysis of user reactions to meta's threads launch and Twitter's X renaming: a comparative study using distilbert and machine learning -- Section 6. Sustainability initiatives. Chapter 16. Green theatre index: measuring the effectiveness of climate-smart approaches and benchmarking |
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| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| genre | Electronic books. |
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| id | ZDB-98-IGB-00326939 |
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| indexdate | 2025-03-18T14:30:37Z |
| institution | BVB |
| isbn | 9798369312117 |
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| spelling | Data-driven business intelligence systems for socio-technical organizations Pantea Keikhosrokiani, editor. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global 2024. 29 PDFs (490 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Section 1. Introduction to digital transformation and business intelligence. Chapter 1. Navigating the fourth industrial revolution: empowering socio-technical organizations with data-driven business intelligence systems ; Chapter 2. Digital transformation and business intelligence (BI) in the industry 4.0 (i 4.0) age -- Section 2. Artificial intelligence and machine learning in business. Chapter 3. How business intelligence and data analytics can leverage business ; Chapter 4. Scalable textile business intelligence with operational data collection and analytical processing ; Chapter 5. Synergizing success: harnessing AI-infused business intelligence to propel exponential business growth ; Chapter 6. Software engineering approach for designing apparel business data analytics ; Chapter 7. The adoption of business intelligence as a competitive strategy among SMEs: a developing country study -- Section 3. Innovations in healthcare intelligence. Chapter 8. ADHD healthcare intelligence: a synergistic approach with big data and AI for better screening, diagnosis, treatment, and monitoring ; Chapter 9. Intersecting minds and machines: a review of behavioral intentions towards health intelligence systems ; Chapter 10. Transforming medical tourism through an integrated business intelligence platform: data-driven insights and healthcare industry impact -- Section 4. AI-augmented technological applications in specific fields. Chapter 11. Deep-mental workload intelligent system: an AI-augmented system to predict employee mental workload based on EEG data using deep learning ; Chapter 12. Inventory classification and management system using machine learning and analytical dashboard: a case study of a manufacturing industry ; Chapter 13. Narrative threads and cinematic connections using intelligent systems to enhance movie recommendations with market basket analysis and advanced algorithms -- Section 5. Social and ethical considerations in technology. Chapter 14. Individual privacy should be an institutional value: socio-technical design of university data collection systems ; Chapter 15. Sentiment analysis of user reactions to meta's threads launch and Twitter's X renaming: a comparative study using distilbert and machine learning -- Section 6. Sustainability initiatives. Chapter 16. Green theatre index: measuring the effectiveness of climate-smart approaches and benchmarking Restricted to subscribers or individual electronic text purchasers. "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. "-- Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 04/10/2024). Big data. Business intelligence. Artificial Intelligence. Behavioral Analytics. Big Data Management. Business Intelligence. Data Analytics. Data-driven Decision Making. Decision-Making. Deep Learning. Future of BI in Organizations. Machine Learning. Predictive Analytics. Real-Time Business Intelligence. Recommender Systems. Socio-Technical Dynamics. Socio-Technical Organizations. Electronic books. Keikhosrokiani, Pantea 1982- editor. IGI Global, publisher. Print version: 9798369312100 |
| spellingShingle | Data-driven business intelligence systems for socio-technical organizations Section 1. Introduction to digital transformation and business intelligence. Chapter 1. Navigating the fourth industrial revolution: empowering socio-technical organizations with data-driven business intelligence systems ; Chapter 2. Digital transformation and business intelligence (BI) in the industry 4.0 (i 4.0) age -- Section 2. Artificial intelligence and machine learning in business. Chapter 3. How business intelligence and data analytics can leverage business ; Chapter 4. Scalable textile business intelligence with operational data collection and analytical processing ; Chapter 5. Synergizing success: harnessing AI-infused business intelligence to propel exponential business growth ; Chapter 6. Software engineering approach for designing apparel business data analytics ; Chapter 7. The adoption of business intelligence as a competitive strategy among SMEs: a developing country study -- Section 3. Innovations in healthcare intelligence. Chapter 8. ADHD healthcare intelligence: a synergistic approach with big data and AI for better screening, diagnosis, treatment, and monitoring ; Chapter 9. Intersecting minds and machines: a review of behavioral intentions towards health intelligence systems ; Chapter 10. Transforming medical tourism through an integrated business intelligence platform: data-driven insights and healthcare industry impact -- Section 4. AI-augmented technological applications in specific fields. Chapter 11. Deep-mental workload intelligent system: an AI-augmented system to predict employee mental workload based on EEG data using deep learning ; Chapter 12. Inventory classification and management system using machine learning and analytical dashboard: a case study of a manufacturing industry ; Chapter 13. Narrative threads and cinematic connections using intelligent systems to enhance movie recommendations with market basket analysis and advanced algorithms -- Section 5. Social and ethical considerations in technology. Chapter 14. Individual privacy should be an institutional value: socio-technical design of university data collection systems ; Chapter 15. Sentiment analysis of user reactions to meta's threads launch and Twitter's X renaming: a comparative study using distilbert and machine learning -- Section 6. Sustainability initiatives. Chapter 16. Green theatre index: measuring the effectiveness of climate-smart approaches and benchmarking Big data. Business intelligence. |
| title | Data-driven business intelligence systems for socio-technical organizations |
| title_auth | Data-driven business intelligence systems for socio-technical organizations |
| title_exact_search | Data-driven business intelligence systems for socio-technical organizations |
| title_full | Data-driven business intelligence systems for socio-technical organizations Pantea Keikhosrokiani, editor. |
| title_fullStr | Data-driven business intelligence systems for socio-technical organizations Pantea Keikhosrokiani, editor. |
| title_full_unstemmed | Data-driven business intelligence systems for socio-technical organizations Pantea Keikhosrokiani, editor. |
| title_short | Data-driven business intelligence systems for socio-technical organizations |
| title_sort | data driven business intelligence systems for socio technical organizations |
| topic | Big data. Business intelligence. |
| topic_facet | Big data. Business intelligence. Electronic books. |
| work_keys_str_mv | AT keikhosrokianipantea datadrivenbusinessintelligencesystemsforsociotechnicalorganizations AT igiglobal datadrivenbusinessintelligencesystemsforsociotechnicalorganizations |