Data analytics for smart decision-making and resilient systems
- In a networked world, companies depend on fast and smart decisions, especially when it comes to reacting to external change. With the wealth of data available today, smart decisions can increasingly be based on data analysis and be supported by IT systems that leverage AI. A global pandemic brings external change to an unprecedented level of unpredictability and severity of impact. Resilience therefore becomes an essential factor in most decisions when aiming at making and keeping them smart. In this chapter, we study the characteristics of resilient systems and test them with four use cases in a wide-ranging set of application areas. In all use cases, we highlight how AI can be used for data analysis to make smart decisions and contribute to the resilience of systems.
| Author of HS Reutlingen | van Dinther, Clemens |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-34949 |
| DOI: | https://doi.org/10.1007/978-3-030-66661-3_13 |
| ISBN: | 978-3-030-66660-6 |
| ISBN: | 978-3-030-66661-3 |
| Erschienen in: | Market engineering : insights from two decades of research on markets and information |
| Publisher: | Springer |
| Place of publication: | Cham |
| Editor: | Henner Gimpel, Jan Krämer, Dirk Neumann, Jella Pfeiffer, Stefan Seifert, Timm Teubner, Daniel Veit, Anke Weidlich |
| Document Type: | Book chapter |
| Language: | English |
| Publication year: | 2021 |
| Edition: | 1st ed. |
| Page Number: | 19 |
| First Page: | 221 |
| Last Page: | 239 |
| PPN: | Im Katalog der Hochschule Reutlingen ansehen |
| DDC classes: | 004 Informatik |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
