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Technical contribution
Data Fabric provides a structural framework for the management and integration of data that creates a consistent environment for managing data across distributed systems. The goal is to create a unified data infrastructure that enables efficient organization, integration and management of data, regardless of where it is physically stored. This approach helps companies to connect different data landscapes and ensure the smooth exchange of information.
Data intelligence goes beyond the mere collection of data. It encompasses the continuous recording of the status and relevance of data and systems, with metadata playing a key role. role. Accurately capturing the semantics of metadata enables a deeper understanding of the technical and business meaning of data, leading to better collaboration between IT and business departments and more informed decisions.
The automation of data processes and the active use of metadata are crucial in order to keep pace with the dynamics of business development. This enables companies to react quickly to changes and optimize data management.
A modern data fabric architecture enables access to all types of data - both operational and analytical - without restrictions to specific data marts, data warehouses or lakehouses. An abstraction layer allows the various components to communicate with each other and adapt dynamically. For the end user, it does not matter where the data is stored, be it in applications such as SAP or in databases. This flexibility creates the basis for self-service and can be regulated by federated governance as part of the data mesh concept.
Automation is crucial for managing the multitude of activities in compliance with governance guidelines, both at a technical level with Data Fabric and at an organizational level with Data Mesh. Important tasks include analyzing events in the data mesh, making decisions, defining workflows and creating and implementing blueprints.
The concept of the data mesh emphasizes the importance of thinking in terms of data products. The key to success lies in self-service and a regulated approach to data, including the assumption of responsibility. Data products arise from the combination of business and technical components and require the ability to be designed and created directly from the business departments, with effective governance being essential.
The combination of Data Fabric, Data intelligence, data mesh, automation and data products forms the basis for future-proof data management. Companies that implement these concepts are optimally positioned for the data-based future. Now is the right time to act - are you ready?
