Every year, Gartner identifies technology trends that are critical to business. This year, the list comprises 12 strategic trends that will enable CEOs to deliver growth, digitalization and efficiency — and position CIOs and IT executives as strategic partners in the organization. As part of the annual 2022 report, Gartner introduces data fabric as a leading trend.
So what is 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.
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.
Data fabric is not simply just a combination of traditional and contemporary technologies but rather a design concept that changes the focus of human and machine workloads. The design optimizes data management by automating repetitive tasks such as profiling datasets, discovering and aligning schema to new data sources, and at its most advanced, healing the failed data integration jobs.
Data fabric must collect and analyze all forms of metadata. 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.
Data fabric must convert passive metadata to active metadata. For frictionless sharing of data, it is important for enterprises to activate metadata. For this to happen, data fabric should continuously analyze available metadata for key metrics and statistics and then builds a graph model.
It should graphically depict metadata in an easy-to-understand manner, based on their unique and business-relevant relationships. And lastly, it should leverage key metadata metrics to enable AI/ML algorithms, that learn over time and churn out advanced predictions regarding data management and integration.