May 11, 2021
May 11, 2021
Contributor: Ashutosh Gupta
D&A leaders should understand the key pillars of data fabric architecture to realize a machine-enabled data integration.
Data management agility has become a mission-critical priority for organizations in an increasingly diverse, distributed, and complex environment. To reduce human errors and overall costs, data and analytics (D&A) leaders need to look beyond traditional data management practices and shift toward modern solutions like AI-enabled data integration
Download now: Essential Guide to Understanding the Role of Data Fabric
“The emerging design concept called “data fabric” can be a robust solution to ever-present data management challenges, such as the high-cost and low-value data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing and more,” says Mark Beyer, Distinguished VP Analyst at Gartner.
Download eBook: 2022 Leadership Vision for Data and Analytics Leaders
Gartner defines data fabric as a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable and inferenced metadata assets to support the design, deployment and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms.
Data fabric leverages both human and machine capabilities to access data in place or support its consolidation where appropriate. It continuously identifies and connects data from disparate applications to discover unique, business-relevant relationships between the available data points. The insight supports re-engineered decision-making, providing more value through rapid access and comprehension than traditional data management practices.
For example, a supply chain leader using a data fabric can add newly encountered data assets to known relationships between supplier delays and production delays more rapidly, and improve decisions with the new data (or for new suppliers or new customers).
Read More: Gartner Top 10 Data and Analytics Trends for 2022
Consider two scenarios. In the first, the driver is active and paying full attention to the route, and the car’s autonomous element has minimum or no intervention. In the second, the driver is slightly lazy and loses focus, and the car immediately switches to a semi-autonomous mode and makes the necessary course corrections.
Both scenarios sum up how data fabric works. It monitors the data pipelines as a passive observer at first and starts suggesting alternatives that are far more productive. When both the data “driver” and the machine-learning are comfortable with repeated scenarios, they complement each other by automating improvisational tasks (that consume too many manual hours), while leaving the leadership free to focus on innovation.
Read more: Data Sharing Is a Business Necessity to Accelerate Digital Business
Discover Gartner BuySmart™: Reduce risk and optimize spend on your next tech purchase.
To deliver business value through data fabric design, D&A leaders should ensure a solid technology base, identify the required core capabilities, and evaluate the existing data management tools.
Here are the key pillars of a data fabric architecture D&A leaders must know.
Contextual information lays the foundation of a dynamic data fabric design. There should be a mechanism (like a well-connected pool of metadata) that enables data fabric to identify, connect, and analyze all kinds of metadata such as technical, business, operational, and social.
Read More: How DataOps Amplifies Data and Analytics Business Value
For frictionless sharing of data, it is important for enterprises to activate metadata. For this to happen, data fabric should:
Download eBook: The Future of Decisions
Knowledge graphs enable data and analytics leaders to derive business value — by enriching data with semantics.
The semantic layer of the knowledge graph makes it more intuitive and easy to interpret, making the analysis easy for D&A leaders. It adds depth and meaning to the data usage and content graph, allowing AI/ML algorithms to use the information for analytics and other operational use cases.
Integration standards and tools used regularly by data integration experts and data engineers can ensure easy access to - and delivery from - a knowledge graph. D&A leaders should leverage that; otherwise, the adoption of data fabric can face many interruptions.
Data fabric should be compatible with various data delivery styles (including, but not limited to, ETL, streaming, replication, messaging, and data virtualization or data microservices). It should support all types of data users – including IT users (for complex integration requirements) and business users (for self-service data preparation).
Mark A. Beyer is a Research Vice President and Distinguished Analyst in the ITL Data and Analytics group. Mr. Beyer covers broad data architecture solutions including data management and its intersection with use cases and data governance issues.
Join the world's most important gathering of data and analytics leaders along with Gartner experts and adapt to the changing role of data and analytics.
Recommended resources for Gartner clients*:
Data Fabrics Add Augmented Intelligence to Modernize Your Data Integration
Emerging Technologies: Data Fabric Is the Future of Data Management
*Note that some documents may not be available to all Gartner clients.