Data Fabrics Modernizes Existing Data Management
Data fabrics in unison with AI/ML technology can transform the way organizations manage and integrate data and ultimately improve the ease of doing business.
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Data management agility takes precedence among organizations with diverse, distributed, and disruptive environments. It is one of the most crucial deciding factors in determining a company’s potential to transform data into opportunities. But, managing data remains an uphill climb thanks to advancements in big data and the Internet of Things (IoT).
Data management is susceptible to errors and delays that can impact operational efficiency and value generation. Problems aggravate when traditional data management practices are used, and the overall performance of a company hits the skids.
To cut down errors and improve efficiency, data, and analytics (D and A) leaders need to adopt modern concepts like a data fabric to effectively pool, process, and act on their massive data inventories in an effort to better serve their constituents. Experts agree that data fabrics are the future of data management and integration.
Gartner recommends that “Data and analytics leaders must upgrade to a data fabric design that enables dynamic and augmented data integration in support of their data management strategy.”
Forrester states that “Enterprise Architecture (EA) pros should use data fabric to democratize data across the enterprise for various use cases.”
The emerging concept of data fabrics can overcome pertinent data management and data integration challenges, such as low-value, expensive integration cycles, emerging demand for real-time and event-driven data sharing, frequent maintenance of earlier integrations, and more.
Why Data Fabrics Are the Next Step
Data fabric is a design concept that allows companies to integrate data and processes. In other words, it serves as an integrated layer that employs continuous analytics over existing, discoverable, and inferences metadata assets to design, deploy and utilize integrated and reusable data across all environments, including hybrid and multi-cloud platforms. It utilizes human and AI and ML capabilities to consolidate data and enable access where appropriate.
Data fabric enables business users to connect data from myriad applications or sources and ultimately identify unique, business-relevant relationships between the available data points. Through rapid access and comprehension, users can easily garner useful insights and use them to drive decision-making and experiences.
To realize the data fabric design and ensure value, upcoming technologies like embedded machine learning, semantic knowledge graphs, and active metadata management are used. A full-fledged data fabric architecture optimizes repetitive tasks such as profiling datasets, discovering and aligning schema to new data sources (that consume too many manual hours) while freeing the leadership to focus on innovation.
How Data Fabric Architecture Can Be Strengthened to Deliver Value
A data fabric design is useful when leaders can ensure a solid technology base, identify the required core capabilities, and evaluate the existing data management and data integration solutions. According to Gartner, here are the pillars of a data fabric that users must know about:
- A mechanism should be in place that enables data fabric to identify, connect, and analyze all kinds of metadata. This lays a foundation for creating an agile data management architecture.
- Data fabric must convert passive data into actionable data to help users support AI/ML algorithms for churning out advanced predictions regarding data management and integration.
- Data fabric must curate knowledge graphs that allow easy access to data integration experts. By enriching data with semantics, users can deliver value.
- Data fabric must have compatibility with multiple methodologies such as ETL, streaming, replication, messaging, and more. Additionally, it should support all types of data users that include IT users (for complex integration requirements) and business users (for self-service data preparation and integration).
Data fabrics offer a new way to streamline data management and integration activities in an organization. Not only does it help D and A teams improve these processes (incorporating AI and ML), but it also eases doing business.
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