Artificial Intelligence for Business Analytics Algorithms, Platforms and Application Scenarios /
While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitization,...
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| 団体著者: | |
| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Wiesbaden :
Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg,
2023.
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| 版: | 1st ed. 2023. |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-3-658-37599-7 |
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| 100 | 1 | |a Weber, Felix. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Artificial Intelligence for Business Analytics |h [electronic resource] : |b Algorithms, Platforms and Application Scenarios / |c by Felix Weber. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Wiesbaden : |b Springer Fachmedien Wiesbaden : |b Imprint: Springer Vieweg, |c 2023. | |
| 300 | |a 1 online resource (XI, 136 p. 38 illus.). |b 33 illus. in color. | ||
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| 505 | 0 | |a Business Analytics -- Artificial Intelligence -- AI and BA platforms -- Technology framework and process model as reference -- Case studies on the use of AI-based business analytics. | |
| 520 | |a While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies. Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods. This book provides a concise introduction to the essential aspects of using artificial intelligence methods for business analytics, presents machine learning and the most important algorithms in a comprehensible form based on the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company. The Content Business Analytics Artificial Intelligence AI and BA platforms Technology framework and procedure model as reference Case studies on the use of AI-based business analytics The Author Felix Weber is a researcher at the University of Duisburg-Essen with a focus on digitalization, artificial intelligence, price, promotion, assortment management, and transformation management. At the Chair of Business Informatics and Integrated Information Systems, he foundedthe Retail Artificial Intelligence Lab (retAIL). At the same time, he also worked on various jobs as a consultant for SAP systems in retail, Head of Data Science and as Head of ERP. He thus combines current practice with scientific research in this subfield. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Application software. | |
| 650 | 0 | |a Business information services. | |
| 650 | 0 | |a Big data. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a IT in Business. |
| 650 | 2 | 4 | |a Big Data. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783658375980 |
| 776 | 0 | 8 | |i Printed edition: |z 9783658376000 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-658-37599-7 |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||