Overview
- Provides a systematic methodology that blends both technological and managerial aspects of enterprise analytics systems
- Delves into real world use cases to illustrate how to navigate complexities in the modern business landscape
- Caters to a diverse range of professionals, such as business executive, enterprise architect, analytics, and academics
Part of the book series: Studies in Big Data (SBD, volume 150)
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About this book
This book is a comprehensive guide for professionals, leaders, and academics seeking to unlock the power of data and analytics in the modern business landscape. It delves deeply into the strategic, architectural, and managerial aspects of implementing enterprise analytics (EA) systems in large enterprises. The book is meticulously structured into three parts. Part 1 lays the foundation for adaptable architecture in EA. Part 2 explores technical considerations: data, cloud platforms, and AI solutions. The final part focuses on strategy execution, investment, and risk management. Acting as a comprehensive guide, the book enables the creation of robust EA capabilities that foster growth, optimize operations, and keep pace with EA's dynamic world. Whether readers are leaders harnessing data's potential, practitioners navigating analytics, or academics exploring this evolving domain, this book provides insights and knowledge to guide readers toward a thriving, data-driven future.
Keywords
Table of contents (10 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Strategic Blueprint for Enterprise Analytics
Book Subtitle: Integrating Advanced Analytics into Data-Driven Business
Authors: Liang Wang, Jianxin Zhao
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-031-55885-6
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-55884-9Published: 13 April 2024
Softcover ISBN: 978-3-031-55887-0Due: 18 May 2024
eBook ISBN: 978-3-031-55885-6Published: 12 April 2024
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XVII, 245
Number of Illustrations: 30 b/w illustrations, 34 illustrations in colour
Topics: Data Engineering, Computational Intelligence, Big Data, Artificial Intelligence