Abstract
Business intelligence and data analytics projects often involve low-level, ad hoc data wrangling and programming, which increases development effort and reduces usability of the resulting analytics solutions. Conceptual modeling allows to move data analytics onto a higher level of abstraction, facilitating the implementation and use of analytics solutions. In this chapter, we provide an overview of the data analytics landscape and explain, along the (big) data analysis pipeline, how conceptual modeling methods may benefit the development and use of data analytics solutions. We review existing literature and illustrate common issues as well as solutions using examples from cooperative research projects in the domains of precision dairy farming and air traffic management. We target practitioners involved in the planning and implementation of business intelligence and analytics projects as well as researchers interested in the application of conceptual modeling to business intelligence and analytics.
Keywords: Business intelligence • Business analytics • Data analytics • Conceptual modeling
| Original language | English |
|---|---|
| Title of host publication | Digital Transformation |
| Subtitle of host publication | Core Technologies and Emerging Topics from a Computer Science Perspective |
| Editors | Birgit Vogel-Heuser, Manuel Wimmer |
| Place of Publication | Berlin |
| Publisher | Springer Vieweg |
| Pages | 311-336 |
| Number of pages | 26 |
| ISBN (Electronic) | 9783662650042 |
| ISBN (Print) | 978-3-662-65003-5 |
| DOIs | |
| Publication status | Published - 2023 |
Fields of science
- 102 Computer Sciences
- 102010 Database systems
- 102015 Information systems
- 102016 IT security
- 102025 Distributed systems
- 102027 Web engineering
- 102028 Knowledge engineering
- 102030 Semantic technologies
- 102033 Data mining
- 102035 Data science
- 509026 Digitalisation research
- 502050 Business informatics
- 502058 Digital transformation
- 503008 E-learning
JKU Focus areas
- Digital Transformation