Published: 17 December 2019
Summary
Data management teams are under constant pressure to provide faster access to integrated data across increasingly distributed landscapes. Data and analytics leaders must upgrade to a data fabric design that enables dynamic and augmented data integration in support of their data management strategy.
Included in Full Research
- ML-Augmented Data Integration Is Making Active Metadata Analysis and Semantic Knowledge Graphs Pivotal Parts of the Data Fabric
- Data Fabric Must Have the Ability to Collect and Analyze All Forms of Metadata
- Data Fabric Must Have the Ability to Analyze and Convert Passive Metadata to Active Metadata
- Data Fabric Must Have the Ability to Create a Knowledge Graph That Can Operationalize the Data Fabric Design
- Data Fabric Must Enable Business Users to Enrich the Data Models With Semantics
- Extreme Levels of Distribution, Scale and Diversity of Data Assets Add Complexity to Data Integration Design and Delivery
- A Strong Data Integration Backbone Is Necessary for Versatile Data Sharing in Support a Data Fabric Design
- Core Data Fabric Functionalities Now Appear in Many Separate Data Management Tools; Distinction Among Them Is Blurring
- Delivering the Data Fabric With a Combination of Tools and Capabilities