Data fabric supports better use of data as an enterprise asset by providing access to the right data just in time, regardless of where the data is stored. It is agnostic to deployment platform, data process, data use, architectural approach and geography while integrating core capabilities.  A data fabric architecture provides business-ready data for applications, analytics, AI and business process automation by dynamically orchestrating disparate data sources across organizational silos.

Technical teams can use a data fabric to radically simplify data management and governance in complex hybrid and multi-cloud data landscapes while significantly reducing costs and risk. Business users gain faster access to data to improve decision making and drive innovation.

Democratize data access

Data silos may occur when data that is stored in different environments – say, on-premises versus the cloud – could be difficult to integrate, and may be inaccessible to various users. Eliminate data silos and intelligently unify diverse data types and architectures — like data lakes, data catalogs, warehouses, and other data integration platforms — into one common data foundation without the need to copy or move information

Data fabric, simply put, is the answer to this problem. A data fabric is an environment that standardizes and unifies data from different sources, storage locations, and access points to make it usable, scalable, and integrated. It creates a unified framework that makes data seamless by design.  When implemented properly, data fabrics can solve challenges associated with data silos. These challenges include availability, scalability, and reliability.

Data fabric – the standardization and unification of data across an entire organization – was named one of the top 10 data trends by Gartner; who predicted that businesses would be forced to invest in creating data fabrics to improve business intelligence.

Here are few key benefits of Data fabric –

Intelligence Integration

A range of integration styles to extract, ingest, stream, virtualize and transform data, driven by data policies to maximize performance while minimizing storage and egress costs

Self-Service

A marketplace that supports self-service consumption, enabling users to find, collaborate and get access to high-quality data in a timely manner

High Quality – High Availability

Deliver high-quality, governed & secure, business-ready data to the right people at the right time across the enterprise to drive business outcomes at speed and scale. With a well-structured data fabric, information is made available to users regardless of the source of the data, the location where it is stored, or the point of user access.

Scalability

Data that is automatically standardized upon receipt can be processed at higher volumes than data that must be manually formatted, standardized and integrated. The greater the volume of data that can be applied, the better the business insights drawn from that data.

Readability

A data fabric improves data reliability in two different ways. First, data is more reliably available to a variety of users regardless of geographic distribution. Secondly, data is unified to a single source. This eliminates confusion which could come from having data stored on multiple platforms. By using a data fabric, businesses can make faster decisions because they can view data from multiple sources in one platform.

Usability

One of the main reasons to create a data fabric is to improve the usability of different types of data. A multi-faceted, multi-layered data analytics program is the best way to achieve valuable data insights – and stay one step ahead of the competition. A comprehensive data analytics program should include different types of data, each providing another level of insight to the business environment.

Designed for Hybrid Cloud and AI

Deliver a real-time view of sensitive data and AI assets such as personally identifiable information or AI models across hybrid multi-cloud environments and enforce protective policies automatically.