Archive Number: 4.605
Status of Thesis: Open
Data is the fuel for IT services of the 21st century. New business models emerge, that are based on the identification, aggregation, and refinement of raw data to valuable information. This information can be used to facilitate the company’s own business decision-making or may be sold to third parties. The whole process, which starts from a data source and may end with salable information or data products, is called a data driven value chain.
With increasing worldwide connectivity companies are not limited to their own data sources or refinement-services. The usage of other companies data and service-capabilities can be of mutual benefit for all involved parties. To allow data driven value chains to be established across company borders, multiple, previous independent platforms and services need to be connected as an ecosystem. Economy that is based on this concept is called platform economy.
To create a smooth working data driven value chain in an ecosystem of platforms is no easy task. The federal nature of the ecosystem leads to decentralized decision-making and a very heterogeneous technology stack.
The goal of this thesis is to get an overview of current technologies, that are used or are promising to establish and manage data driven value chains in platform economics. To foster our understanding of those technologies challenges and chances of their usage needs to be identified and assessed. For this purpose existing literature can be used.
- H. G. Miller and P. Mork, “From Data to Decisions: A Value Chain for Big Data,” IT Professional, vol. 15, no. 1, pp. 57–59, Jan. 2013, doi: 10.1109/MITP.2013.11.
- E. Curry, “The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches,” in New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe, J. M. Cavanillas, E. Curry, and W. Wahlster, Eds. Cham: Springer International Publishing, 2016, pp. 29–37.
- M. Kenney and J. Zysman, “The Rise of the Platform Economy,” Issues Sci. Technol., vol. 32, no. 3, pp. 61–69, SPR 2016.