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, data observability, 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.
Register for 2023 Gartner® Magic Quadrant™ for Data Integration Tools
Cloud Pak for Data 4.8 is here. Find out what’s new
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.
Enterprise AI requires trusted data built on the right data foundation. With IBM data fabric, clients can build the right data infrastructure for AI using data integration and data governance capabilities to acquire, prepare and organize data before it can be readily accessed by AI builders using watsonx.ai and watsonx.data.
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.
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.
IBM was named a Leader for the 18th year in a row in the 2023 Gartner® Magic Quadrant™ for Data Integration Tools.
See why IBM is recognized as a Leader for Data Quality Solutions in the 2022 Garner Magic Quadrant for Data Quality Solutions.
A data fabric architecture delivers governed data across hybrid and multi-cloud environments to fuel innovation and growth.
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. 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.