Data fabric architecture
Transform your organization into a data-driven enterprise 
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What are your data challenges?

Your employees need to make data-driven decisions, but too often, data is in silos. With a deep understanding of your organization’s needs and use cases, you can design a data architecture that empowers your teams and works across the ecosystem.  

The most common data use cases and challenges? Data integration, data governance, AI governance, and data science and MLOps. Learn more about each and how a modern data architecture—like data fabric—can help shape a data-driven enterprise.

Elevate the value of your data

How to build your data architecture

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Data integration  Connect data from disparate sources in multicloud environments and deliver it to teams anytime, anywhere.  Learn more about data integration

Data governance   Create a business-ready data foundation and maximize ROI on your data investments.  Learn more about data governance

AI governance  Automate AI governance to create responsible, transparent and explainable AI workflows. Learn more about scaling trustworthy AI

Data science and MLOps   Simplify model building and deployment with automated tools and processes designed to deliver business outcomes. Learn more about automated data science tools

How can a modern data architecture help shape a data-driven enterprise? 

A modern data architecture ensures data is accessible to relevant data users
based on their unique workflows. Data fabric is an architectural approach that
simplifies data access in an organization and facilitates self-service data
consumption. Teams can use this architecture to automate data discovery,
governance and consumption, through integrated end-to-end data
management capabilities. Whether data engineers, data scientists or business
users are your intended audience, a data fabric delivers the data needed for
better decision-making.

See data fabric architecture as a solution (102 KB)

Data fabric architecture case studies Increased business pipeline

With a unified data and AI platform, the IBM® Global  Chief Data Office increased its business pipeline by USD 5 billion in three years.

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Accelerating innovation

Luxembourg Institute of Science and Technology built a state-of-the-art platform with faster data insights to empower companies and researchers.

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Customers first

State Bank of India transformed its customer experience by designing an intelligent platform with faster, more secured data integration.

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Key elements of a data fabric architecture 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.

AI and hybrid cloud-ready

An AI-infused composable architecture built for hybrid cloud environments.

The technology behind data fabric Using the IBM Cloud Pak® for Data platform, organizations can experience the benefits of data fabric in a unified platform that makes all data—spanning hybrid and multicloud environments—available for AI and data analytics. Known for data integration 

IBM was named a Leader for the 17th year in a row in the 2022 Gartner® Magic Quadrant™ for Data Integration Tools.

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Leader in delivering quality data 

See why IBM is recognized as a Leader for Data Quality Solutions in the 2022 Garner Magic Quadrant for Data Quality Solutions.

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Automated governance 

Automate and enforce policies automatically across data on any cloud, with increased visibility and collaboration while reducing compliance risks. 

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Frequently asked questions

A data fabric and data mesh can co-exist. A data fabric provides the capabilities needed to implement and take full advantage of a data mesh by automating many of the tasks required to create data products and manage the lifecycle of data products. By using the flexibility of a data fabric foundation, you can implement a data mesh, continuing to take advantage of a use case centric data architecture regardless of if your data resides on premises or in the cloud.

Read: Three ways a data fabric enables the implementation of a data mesh

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)

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.  

 

Read: The evolution of enterprise data architectures 


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