Data Analytics Engine (DAE)


Many data space use cases allow analysis of multi-source, multi-stakeholder data based on methods like statistical analysis, machine learning, deep learning, and other data-mining techniques (e.g. for demand forecasting in an industrial use case, which must synthesise and analyse multiple data flows coming from different platforms the data space is comprised of). A function like that requires analysis of multiple data flows, which is why it needs to be supported by a ‘Data Analytics Engine’ building block. Depending on the nature of the data, this building block can take different forms (such as streaming analytics, cloud-based analytics, machine learning, or complex event processing [CEP]).

Role and Scope

Supports execution of data analytics with regard to data shared and exchanged over the data space


Components and Technologies

Design Principles Position Paper
  • Statistical analysis

  • Machine learning

  • Deep learning

  • Data-mining techniques

Technical Reference Implementation

In a data space in the field of digital finance, banks and other financial organisations can make credit risk assessments of participants (e.g. citizens, businesses, financial institutions) using statistical methods. To this end, they leverage machine- learning techniques and AI algorithms, the latter being implemented through the data analytics engine.

Business Use Cases Implementation

Best practices identification and recommendations

Gap or what is missing?



Additional Information

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