Business organizations generate multiple types of data from different sources daily. Accessing the power of data is key to gaining business insights that aid decision-making and gaining a competitive advantage. The more data sources, silos, complexity, and change an organization has, the more challenging it is for technical teams to keep up. Implementing data integration tools is a smart way to undergo a digital transformation. One of the best practices for data-driven organizations is to implement data fabric to support data management and advanced analytics.
Data fabric is a modern distributed data architecture including shared data assets and optimized data fabric pipelines. Data fabric design supports complex data types, data sources, and use cases. Data fabric provides timely, consistent, and quality data for a range of advanced analytics, operational, transactional, and self-service use cases. Data fabric can access, combine, and transform data from different sources using metadata, models, and pipelines. Data fabric offers the flexibility to span data distributed on-premises, hybrid, and multi-cloud environments.
Data Fabric Builds Business Value
Implementing data fabric to access the power of data comes with plenty of business value. It supports multiple, diverse business users and use cases using a modern distributed data environment, shared data assets, and optimized data fabric processes to fuel data-driven business. Business users can accelerate value realization with rapid access to distributed on-premises, cloud, and hybrid cloud business data regardless of where it’s stored. Data fabric democratizes data access to empower business users with timely, uncompromised data. Data fabric design allows organizations to embrace new data and analytics technologies like data science, real-time data, and the cloud faster to maintain a competitive edge.
Streamlined data and metadata management, data integration, and delivery processes and pipelines through the use of artificial intelligence (AI) and machine learning (ML) saves organizations time and money. Data fabric ensures proper data governance and control so the right data is delivered at the right time securely and in compliance with data regulations.
A data fabric solution is the best way to address modern data challenges in a unified way. A holistic approach to an enterprise data fabric strategy offers optimized data integration and management capabilities that offer simplicity, automation, and rapid access to data pipelines. It also offers shared data assets that support all business users and use cases. The agility and ease of deployment of a data fabric solution make it ideal for a complex, adapting technological landscape. The right data fabric solution has key capabilities including data virtualization, data integration, master data management, metadata management, data streaming, and data quality.
The Benefits of Data Fabric
Implementing data fabric is a good idea when faced with collecting different types of data from disparate data sources while avoiding compliance risks. It’s a permanent data solution that offers the scalability to manage all business data in a single source of data. A single environment provides frictionless access to different data types regardless of where it’s stored, thus eliminating data silos.
The simplicity of unified data management, data integration, data quality, and data governance provides more rapid access to trustworthy, healthy data. Data fabric makes it easier to leverage cloud environments by supporting on-premise, hybrid cloud, and multi-cloud environments and faster data migration between them. It reduces reliance on legacy infrastructures and solutions while future-proofing the data management infrastructure as new data sources and endpoints are added onto the data fabric.
Data Fabric vs Other Data Architectures
Data fabric architecture has the right capabilities to embrace distributed, complex environments and leverages modern data management and integration capabilities. A distributed data environment supports more use cases such as advanced analytics, operational, transactional, automation, and data governance than other data architectures. It spans more data types and methods including data-in-motion and data-at-rest from on-premises, cloud, Internet of Things (IoT) devices, and third-party sources. It offers pipeline optimization capabilities including intelligent ML algorithms, converged data and metadata management, integration, and sharing of data.
A data fabric architecture provides greater deployment flexibility across a distributed data environment compared to other data architectures.
