03187nam a22005655i 4500 978-3-031-55885-6 DE-He213 20251030111816.0 cr nn 008mamaa 240412s2024 sz | s |||| 0|eng d 9783031558856 978-3-031-55885-6 10.1007/978-3-031-55885-6 doi TA345-345.5 UN bicssc COM018000 bisacsh UN thema Wang, Liang. author. aut http://id.loc.gov/vocabulary/relators/aut Strategic Blueprint for Enterprise Analytics [electronic resource] : Integrating Advanced Analytics into Data-Driven Business / by Liang Wang, Jianxin Zhao. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVII, 245 p. 64 illus., 34 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Big Data, 2197-6511 ; 150 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. Engineering Data processing. Computational intelligence. 64052 Big data. 57216 Artificial intelligence. 58441 Data Engineering. 66059 Computational Intelligence. 64052 Big Data. 57216 Artificial Intelligence. 58441 Zhao, Jianxin. author. aut http://id.loc.gov/vocabulary/relators/aut 75086 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031558849 Printed edition: 9783031558863 Printed edition: 9783031558870 Studies in Big Data, 2197-6511 ; 150 68724 https://doi.org/10.1007/978-3-031-55885-6 ZDB-2-INR ZDB-2-SXIT 59099 59099