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.
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What is data fabric?
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