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Design a data architecture that empowers your organization and works across the ecosystem.
Data matters. To maximize business value from artificial intelligence and ensure trust, a strong data strategy is critical. IBM's data fabric provides organizations with a trusted data foundation, enabling clients to leverage automation for data discovery, enrichment and protection with our data governance and quality capabilities, employing various data integration styles to deliver reliable data for AI workflows. This architecture is composable, allowing IBM to meet clients wherever they are in their data journey.
The most common data use cases and challenges? Data integration, data governance, data observability, data security, data catalog, data orchestration, and master data management. Learn more about each and how a modern data architecture—like data fabric—can help shape and unify a data-driven enterprise.
Read the guide to building a data-driven organization
Deliver quality data for generative AI
Key features of a data fabric architecture
An abstraction layer that provides a common business understanding of the data processing and automation to act on insights.
A range of integration styles to extract, ingest, stream, virtualize and transform unstructured data, driven by data policies to maximize performance while minimizing storage and costs.
A marketplace that supports self-service consumption, letting users find, collaborate and access high-quality data.
End-to-end lifecycle management for composing, building, testing, optimization and deploying the various capabilities of a data fabric architecture.
Unified definition and enforcement of data policies, data governance, data security and data stewardship for a business-ready data pipeline.
An AI-infused composable architecture built for hybrid cloud environments.
A data fabric is an architectural approach, designed to simplify data access and facilitate self-service data consumption for an organization's unique workflows. End-to-end data fabric capabilities include data matching, observability, master data management, data quality, real-time-data integration, and more, all of which can be implemented without ripping and replacing current tech stacks. Whether it's to simplify the day-to-day for data producers, or to provide to data engineers, data scientists and business users self-service access to data, a data fabric prepares and delivers the data needed for insights and better decision-making.
A strong data foundation is critical for the success of AI implementations.
With a unified data and AI platform, the IBM® Global Chief Data Office increased its business pipeline by USD 5 billion in three years.
Luxembourg Institute of Science and Technology built a state-of-the-art platform with faster data delivery to empower companies and researchers.
State Bank of India transformed its customer experience by designing an intelligent platform with faster, more secured data integration.
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.
Data management tools started with databases and evolved to data warehouses and data lakes across clouds and on-premises as more complex business problems emerged. But enterprises are consistently constrained by running workloads in performance and cost-inefficient data warehouses and lakes and are inhibited by their ability to run analytics and AI use cases. The advent of new, open-source technologies and the desire to reduce data duplication and complex ETL pipelines is resulting in a new architectural approach known as the data lakehouse, which offers the flexibility of a data lake with the performance and structure of a data warehouse, along with shared metadata and built-in governance, access controls, and security. But in order to continue to access all of this data now optimized and locally governed by the lakehouse across your organization, a data fabric is required to simplifying data management and enforce access globally. 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.
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.