More Use Cases
A data fabric is a way to design data management systems that are flexible, reusable, and can be easily changed. It supports a variety of data integration styles and uses active metadata, knowledge graphs, semantics, and machine learning to make data integration design and delivery more efficient.
Data fabric encourages augmented data management and cross-platform orchestration to reduce the amount of human work needed to design, deploy, and maintain data systems.
Data fabric has become increasingly popular as a way to simplify an organization’s data integration infrastructure and create a scalable architecture which reduces the technical debt that can accumulate in D&A teams due to the challenges of data integration.
The goal of data fabric is not only to reduce costs and create elegant designs, but also to gradually introduce new use cases for data utilization, context analysis, and alignment.
illumex’s proprietary generative AI engine activates the metadata of both data and analytics sources, and automatically maps and interprets it without human intervention.
This is called Semantic AI-driven Data Fabric implementation. Semantic AI unifies enterprises’ business data language to connect their isolated data silos and to enable data producers and consumers to find, understand and trust their data and analytics.
User persona
Data engineers, data analysts, data architects, data scientists.
Use case objectives
- Automatically and continuously track all data and analytics assets, their creators and users.
- Make it easy for non-technical users to find, access, integrate, and share data.
- Allow business experts a say in how data is modeled.
- Help users find relevant data faster.
- Save money by avoiding the purchase of overlapping tools.
- Improve the return on investment and performance of data investments by tightly integrating data and continuously analyzing usage and use cases.
- Improve communication between data teams and their business users.
- Get an automated Knowledge Graph without the need for graph modeling skills.
- Scale data products derived from use.
