Let’s create data fabric instead of data silos Explore the guide Read the case studies
IBM named a Leader for the 17th year in a row in the 2022 Gartner® Magic Quadrant™ for Data Integration Tools

What is a data fabric?

A data fabric is an architectural approach to simplify data access in an organization to facilitate self-service data consumption. This architecture is agnostic to data environments, processes, utility and geography, all while integrating end-to-end data-management capabilities. A data fabric automates data discovery, governance and consumption, enabling enterprises to use data to maximize their value chain. With a data fabric, enterprises elevate the value of their data by providing the right data, at the right time, regardless of where it resides. 

Elevate the value of your data

How to build your data architecture

Download the latest Forrester Wave™: Enterprise Data Fabric, Q2 2022 report

Learn more about data fabric (934 KB)

Data fabric use cases

Integrate data across any cloud Make trusted data available quickly in hybrid and multicloud environments and connect the right data to the right people for accelerated innovation. Learn more about data integration

Automate governance and data security Create a trusted, business-ready data foundation to automate the enforcement of data protection policies. Learn more about data governance

Create a comprehensive view of clients Build a 360-degree view of customer data to help business users unlock deeper insights for personalized customer experiences. Learn more about Customer 360

Automate AI governance Unify tools process and talent to build and automate trustworthy AI models at scale. Learn more about scaling trustworthy AI

Intelligently manage your data reliability Automatically and proactively manage your data to deliver reliable and quality data products for trusted business outcomes. Learn more about data observability

Features

Key elements of a data fabric Augmented knowledge graph

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 data usage

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.

Read the report
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.

Read the blog post
Faster, more accurate insights

Consolidate data management tools and minimize data duplication for faster access to higher quality, more complete data that renders deeper insights.

Read the blog post

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.

 

IBM’s approach to a data fabric

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Dive deeper

Data fabric versus data lake versus data warehouse

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 fabric versus data virtualization

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.

Watch: Data virtualization in a data fabric (4:42)

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“The Total Economic Impact Of IBM Garage”, a commissioned study conducted by Forrester Consulting, October 2020 (link resides outside ibm.com)