Intelligent Governance In The Big Data Era
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About this topic
Intelligent governance in the big data era focuses on the intersection of data analytics and public administration. As organizations increasingly rely on vast amounts of data to inform decision-making, the principles of governance are evolving. This topic encompasses how data can enhance transparency, accountability, and efficiency in governance. Readers interested in this area will explore the implications of data-driven strategies in both public and private sectors, as well as the ethical considerations that arise from such practices.
Key Topics to Explore
- Data Analytics in Governance
- Ethics of Big Data
- Public Policy and Technology
- Transparency and Accountability
- Smart Cities and Digital Governance
What You Will Find
Books on intelligent governance in the big data era typically cover a range of styles, from academic analyses to practical guides. Readers can expect to find discussions on how big data influences policy-making, improve administrative functions, and the challenges associated with data privacy and security. The material caters to both professionals in the field and those with a general interest in how technology shapes governance today.
Common Questions
What is intelligent governance?
Intelligent governance refers to the use of data-driven insights to enhance decision-making processes in public administration.
How does big data impact governance?
Big data impacts governance by providing insights that can improve efficiency, transparency, and responsiveness in public services.
What are the ethical concerns surrounding big data in governance?
Ethical concerns include data privacy, surveillance, and the potential for bias in data interpretation and decision-making.
Intelligent Governance in the Big Data Era
This book reveals how AI can enhance fiscal accountability, combat financial fraud, expand inclusive finance, and empower citizens—without requiring advanced infrastructure, through real-world case studies and interdisciplinary analysis. In an age defined by data, this book offers a groundbreaking roadmap for leveraging artificial intelligence, machine learning, and big data analytics to transform public finance and drive sustainable development in resource-constrained economies.This timely book brings together leading scholars and practitioners from Palestine, Jordan, the UK, Canada, and beyond to explore how intelligent systems—from predictive budgeting models to blockchain-based transparency tools—are reshaping governance in emerging markets.Designed for researchers, policymakers, development practitioners, and graduate students, this is not just a theoretical treatise—it is a practical guide to building ethical, scalable, and data-driven public institutions. With chapters on healthcare analytics, smart education, cybersecurity, and ethical AI, it bridges the gap between technological potential and real-world impact in the Global South.A vital contribution to Springer’s Studies in Big Data series, this book is essential reading for anyone committed to equitable, intelligent, and sustainable governance in the 21st century.
2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City
Author: Mohammed Atiquzzaman
language: en
Publisher: Springer Nature
Release Date: 2022-01-01
This book gathers a selection of peer-reviewed papers presented at the third Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2021) conference, held in Shanghai, China, on Nov. 27, 2021. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.
Recent Trends in Decision Science and Management
This book discusses an emerging field of decision science that focuses on business processes and systems used to extract knowledge from large volumes of data to provide significant insights for crucial decisions in critical situations. It presents studies employing computing techniques like machine learning, which explore decision-making for cross-platforms that contain heterogeneous data associated with complex assets, leadership, and team coordination. It also reveals the advantages of using decision sciences with management-oriented problems. The book includes a selection of the best papers presented at the 2nd International Conference on Decision Science and Management (ICDSM 2019), held at Hunan International Economics University, China, on 20–21 September 2019.