Preparing Data Science Projects – Between Economic Aspects and Requirements Analysis

Abstract: With the increasing availability of data in enterprises of all industry sectors, new date-based ideas arise, including artificial intelligence (AI) solutions. For those enterprises which have no experience in the implementation of such projects, data science process models can assist in structuring them. We have observed that the majority of the available models do not involve people, their activities, and the associated processes in detail. A possible reason for this is, that many of these models were created with a focus on the data processing and not necessarily to introduce ongoing data-based applications. To close this gap, this paper analyses the aspects which should be included in project preparation, especially requirements analysis, and which methods and tools are adequate to support these steps. These considerations are even more important for AI projects, since it is not necessarily clear from the beginning to which extent the required information is contained in the available data and whether the data is sufficient for the project goals. In addition, it should also be checked strategically whether the idea fits the company's goals and thus offers added value for the company. During the requirements analysis, affected users, their activities and processes are specifically focussed. During these steps, some conceptual information such as formalised current and target processes can be documented, which in turn can help when the implementation is done and the solution is brought to operation.

Keywords: Data Science, Process Model, Data Analytics, Project Management, Methodology, Project Team, Economics, Industrial Science

DOI: 10.54941/ahfe1003104

Cite this paper:

​