Data Fabric
instead of
Data Silos
↷
Data Fabric
instead of
Data Silos
↷
IBM ranks second highest in the Data Fabric use case
See why in the 2021 Gartner Critical Capabilities for Data Integration Tools.
Overview
A data fabric is an information architecture that unifies data across an organization. Agnostic to data environments, processes, use and geography, this architecture helps automate data discovery, data governance and data consumption to deliver business-ready data for analytics and AI.
Top performing enterprises are data driven but research shows that up to 68%¹ of data isn’t analyzed in most organizations and up to 82%² of enterprises are inhibited by data silos. IBM can help you resolve these challenges and optimize the value of data by providing users access to the right data at the right time.
Use cases
Enable self-service data consumption
Access self-service, near real-time data quickly so users spend less time finding the right data and more time adapting to tangible market insights.
Automate governance and data security
Apply industry-specific governance rules quickly across enterprise data by using active metadata to enable automatic policy enforcement for data protection.
Integrate data across any cloud
Make trusted data available quickly in hybrid and multicloud data landscapes. Automate data engineering to simplify access to data. Re-use data management capabilities for greater efficiencies.
Use cases
Enable self-service data consumption
Access self-service, near real-time data quickly so users spend less time finding the right data and more time adapting to tangible market insights.
Automate governance and data security
Apply industry-specific governance rules quickly across enterprise data by using active metadata to enable automatic policy enforcement for data protection.
Integrate data across any cloud
Make trusted data available quickly in hybrid and multicloud data landscapes. Automate data engineering to simplify access to data. Re-use data management capabilities for greater efficiencies.
Features
Democratize quality data
Augmented knowledge
An abstraction layer that provides a common business understanding of the data and automation to act on insights.
Intelligent integration
A range of integration styles to extract, ingest, stream, virtualize and transform data, driven by data policies to maximize performance while minimizing storage and costs
Self-service
A marketplace that supports self-service consumption, letting users find, collaborate and access high-quality data
Unified data lifecycle
End-to-end lifecycle management for composing, building, testing and deploying the various capabilities of a data fabric architecture
Multimodal governance
Unified definition and enforcement of data policies, data governance and data stewardship for a business-ready data pipeline
Designed for AI and hybrid cloud
An AI-infused composable architecture built for hybrid cloud environments
Features
Democratize quality data
Augmented knowledge
An abstraction layer that provides a common business understanding of the data and automation to act on insights.
Intelligent integration
A range of integration styles to extract, ingest, stream, virtualize and transform data, driven by data policies to maximize performance while minimizing storage and costs
Self-service
A marketplace that supports self-service consumption, letting users find, collaborate and access high-quality data
Unified data lifecycle
End-to-end lifecycle management for composing, building, testing and deploying the various capabilities of a data fabric architecture
Multimodal governance
Unified definition and enforcement of data policies, data governance and data stewardship for a business-ready data pipeline
Designed for AI and hybrid cloud
An AI-infused composable architecture built for hybrid cloud environments
Why IBM?
Holistic view across a distributed data landscape
Intelligently integrate and unify data across hybrid and multicloud to deliver trusted data and speed time to business value.
Automated governance
Automate and enforce policies and rules automatically and consistently across data on any cloud with increased visibility and collaboration while reducing compliance risks.
Faster, more accurate insights
Consolidate data management tools and minimize data duplication for faster access to higher quality and more complete data that renders deeper insights.
The platform
Data fabric delivered on IBM Cloud Pak for Data
IBM Cloud Pak for Data provides a data fabric solution for faster, trusted AI outcomes by connecting the right data, at the right time, to the right people, from anywhere it’s needed. Use a unified platform that spans hybrid and multicloud environments to ingest, explore, prepare, manage, govern and serve petabyte-scale data for business-ready AI.
Next steps
Data management tools started with databases and evolved to data warehouses and data lakes as more complex business problems emerged. A data fabric is the next step in the evolution of these tools. With this architecture, you can continue to use the disparate data storage repositories you’ve invested in while simplifying data management. A data fabric helps you optimize your data’s potential, foster data sharing and accelerate data initiatives by automating data integration, embedding governance and facilitating self-service data consumption in a way that storage repositories don’t.
Data virtualization is one of the technologies that enables a data fabric approach. Rather than physically moving the data from various on-premises and cloud sources using the standard extract, transform, load (ETL) process, a data virtualization tool connects to different data sources, integrates only the metadata required and creates a virtual data layer. This allows users to use the source data in real time.
Data continues to compound and is often too difficult for organizations to access information. This data holds unseen insights, which results in a knowledge gap.
With data virtualization capabilities in a data fabric architecture, organizations can access data at the source without moving it, helping to accelerate time to value through faster, more accurate queries.
Next steps
Data management tools started with databases and evolved to data warehouses and data lakes as more complex business problems emerged. A data fabric is the next step in the evolution of these tools. With this architecture, you can continue to use the disparate data storage repositories you’ve invested in while simplifying data management. A data fabric helps you optimize your data’s potential, foster data sharing and accelerate data initiatives by automating data integration, embedding governance and facilitating self-service data consumption in a way that storage repositories don’t.
Data virtualization is one of the technologies that enables a data fabric approach. Rather than physically moving the data from various on-premises and cloud sources using the standard extract, transform, load (ETL) process, a data virtualization tool connects to different data sources, integrates only the metadata required and creates a virtual data layer. This allows users to use the source data in real time.
Data continues to compound and is often too difficult for organizations to access information. This data holds unseen insights, which results in a knowledge gap.
With data virtualization capabilities in a data fabric architecture, organizations can access data at the source without moving it, helping to accelerate time to value through faster, more accurate queries.
Try the IBM data fabric
Contact us now to schedule your 7-day, no-cost assisted trial.
Footnotes
¹Rethink Data: Put More of Your Business Data to Work – From Edge to Cloud (PDF, 8.3 MB, link resides outside ibm.com), Seagate Technology, July 2020
²“Are Data Silos Killing Your Business?”, a commissioned study conducted by Forrester Consulting, October 2020 (link resides outside ibm.com)