A Holistic Data Architecture: From Source to Insight [English spoken]

Many organizations struggle with complex data challenges. Examples include tracking data usage (both transactional and analytical), properly managing and maintaining historical data, synchronizing source systems, reconstructing events (operational lineage), making data and reports accessible via metadata, streamlining data exchange, and preparing data for AI applications.
Often, the solution is sought in reference architectures based on, for example, a data warehouse, data lake, data lakehouse, or data fabric. While valuable, these architectures do not fully address the challenges mentioned above. They focus only on part of the data journey and fail to solve the core problems.
To truly tackle these challenges, a data architecture must cover the entire data journey: from source to insight. Only a holistic approach can achieve this. During this session, we will discuss a data architecture that spans the full data journey. The previously mentioned architectures may play a role within that architecture, but only as components of a larger whole.

This session will cover, among other topics:

• Three types of IT systems: source systems, compensation systems and analytical systems
• The positioning of data warehouses, data lakes, and data lakehouses as compensation systems
• An overview of the Delta data architecture
• How source systems can be made future-proof by “wrapping” them with additional modules
• The importance of abstraction and data minimization within a data architecture
• The role of metadata as the driving force behind a modern data shop.