Brief description:
The objective of Best Practice Story 8 is to develop a modular procedure model to support the introduction of Data Science in companies in the triad of people, technology and organization. A procedure is presented, starting with the first idea up to the productive use of the gained knowledge.
Models such as the Knowledge discovery in databases (KDD) model or the Cross-industry standard process for data mining (CRISP-DM) are recognized in the literature. However, the implementation of data analysis projects requires specific IT systems (tools & infrastructure) as well as processes & roles in the companies, which require a significant structural change in many companies. However, with regard to the dimensions of people and organization, the standardized models lack a detailed understanding of which factors influence the success or failure of a Data Science project.
As a result, a cross-industry and cross-age qualitative interview study was conducted as part of the AKKORD research project. The focus of the study was to understand
- what experiences have been gathered on success factors and barriers in the implementation of Industrial Data Science (IDS) projects,
- What drives employees on topics related to IDS and artificial intelligence (AI),
- How employees experience these topics in their work environment,
- what impact employees expect technological advances to have on their work environment in the future,
- what the necessary roles and competencies are in terms of project scope
- which change management measures have already been implemented and are critical to success.
Based on this holistic view, recommendations for action for change management in the context of Data Science are derived and a concrete technical approach is presented that takes industry standards into account. The interview study is described in more detail in this news article.
The application of the procedure model assumes that the necessary roles and competencies (specialist integrator, data scientist, project manager, product owner, etc.) are available depending on the project scope and that change management is implemented in parallel.
Involved consortium partners:
Institute of Production Systems (IPS)