We use cookies on this site to enhance your user experience. By using this site, you are giving your consent for us to set cookies.

Proposal for Developing an Official Guideline on Data Architecture Standardization: Integration and Communication of (Generated) Knowledge in a Knowledge Layer of a Data-Centric Architecture


Guideline for Integrating and Communicating (Generated) Knowledge in the Knowledge Layer of a Data-Centric Architecture


The automotive industry, along with many other sectors, is currently undergoing a significant shift from application-centric to data-centric architectures. In this context, the need for a Knowledge Layer as part of the architecture becomes increasingly evident. A Knowledge Layer enables the effective integration and communication of generated knowledge - for example with the help of Machine Learning (ML) or Semantic Reasoning (SR) -, or knowledge which is explicitly or implicitly hidden in data models or logic like rules. However, it is necessary to outline how such a Knowledge Layer can be defined and implemented in a data-centric architecture and the impact it has on architecture decisions.


When developing a data-centric architecture with a Knowledge Layer, the following aspects are of particular importance and should be considered in the guideline:

  • How can the Knowledge Layer be defined and designed in a data-centric architecture?
  • What role does the Knowledge Layer play in handling and communicating generated knowledge?
  • In what form and based on which methodology can knowledge be shared with other stakeholders in the Knowledge Layer?
  • What are the implications of the Knowledge Layer on architecture decisions?
  • What is the interaction of a Knowledge Layer, as found in the DIKW pyramid, with the other layers of the pyramid (Data, Information and Wisdom)?


As part of our initiative for data architecture standardization, we propose developing a comprehensive guideline that addresses the integration and communication of generated knowledge in the Knowledge Layer of a data-centric architecture. This guideline aims to provide practical guidelines and cover the following aspects:

  1. Definition and design of the Knowledge Layer:

    • Examination of the concepts and principles that constitute an effective Knowledge Layer in a data-centric architecture.
    • Identification of the necessary components and functions to successfully implement the Knowledge Layer.
  2. Handling and communication of knowledge:

    • Development of methods for detecting and evaluating generated knowledge in the Knowledge Layer.
    • Best practices and techniques for effective communication and dissemination of knowledge to other stakeholders in the Knowledge Layer.
  3. Implications on architecture decisions:

    • Analysis of how the Knowledge Layer influences decision-making and architecture development.
    • Consideration of the DIKW pyramid perspective to identify and prioritize relevant data and knowledge flows across different layers of the architecture.


Through this guideline, we aim to provide architects, data experts, and industry decision-makers with a comprehensive foundation to enhance the handling of generated knowledge in the Knowledge Layer of a data-centric architecture and understand its impact on architecture decisions.


  • Presentation to the VSSo group 29th August 2023

  • No labels