Business leaders need to stay ahead of emerging trends to gain a competitive advantage and be impactful in advancing their critical business strategy. According to Gartner, these trends are integral to the continued survival of any business.

They classified data fabric as one of the top trends for data and analytics leaders to address. Specifically, data fabric enables companies to surmount the challenges associated with data silos. On that note, if you’re unsure if data fabric will benefit your company, continue reading.

Data Fabric Defined


There’s a clear line between data virtualization and data fabric. The term data fabric refers to an end-to-end solution that consists of data management and data integration tools for the efficient management of an organization’s data assets. The technology is designed to ease the complexities associated with data management.

Data fabric architecture supports frictionless data access and data sharing in a distributed network environment. In addition to supporting seamless data access, the data fabric framework is designed to allow processing across various data stores.

Furthermore, data fabric accurately defines the model and syncs various software and technologies without adding their complexities and connects various software and systems. The main objective of data fabric is to provide a consolidated, consistent user experience and real-time data access for members of an organization.

Purpose of Data Fabric

There’s a need for data-centric or data-intensive organizations to develop a holistic approach to solving complex data problems. The ultimate aim of using data is to help decision-makers make informed decisions. Data must be accessible to data users, not locked away in a data lake or data silo. Furthermore, data analysts need an operational and flexible platform to support the analyses they used to derive meaningful and predictive insights from data. The insights will come in handy in extracting business value from data.

In today’s world, most organizations store their data either on-premises or in cloud storage. The data types include data contained in data stores, data lakes, relational databases, and flat files. Before now, data scientists used batch ETL and stream processing to manage data workloads.

Traditional data integration methods no longer meet self-service demands, real-time connectivity, automation, and digital transformation. This is because data fabric eliminates manual methods of processing diverse data sources and technologies across multiple storage locations.

Due to the increasing number of data types, applications, and platforms, big data has become humanly impossible to manage and can prove disruptive to the business in the long run. Data fabric unifies new types of data, platforms, and new technologies introduced into a system. Meanwhile, data scientists can’t sustain changing their data management techniques to accommodate new technology or data types.

All in all, data fabric employs artificial intelligence (AI) and machine learning (ML) to create a highly flexible data management workplace.

Six Features of a Data Fabric Solution


Here are some features of a typical data fabric solution:

1. Autonomous Data Engineering

A robust data fabric architecture features a real-time query that optimizes speed and usage consumption. Users can leverage this tool to predict customer behavior in a single workspace. Data fabric provides solutions to complex data problems while responding to the ever-changing needs of data consumers.

2. Unified Data Environment

Data fabric excels in creating a workplace that allows users to define policies, roles, and practices, and processes. The unified data environment promotes a cohesive analytics experience.

3. Centralized Data Security & Governance

A data fabric solution should provide a single, secured, and governed workspace for shared data that powers advanced analytics initiatives. Additionally, it helps to decentralize data access throughout an organization.

4. Data Management Enhancement

A data fabric solution measures data responsiveness, reliability, risk, and availability in a centralized environment.

5. Platform and Application Integration

Data fabric allows for seamless integration with data platforms, business intelligence (BI) applications, and machine learning applications. It gives data managers and data consumers freedom of choice to use whatever platform or application they like.

6. Future-Proof Infrastructure

Like we mentioned earlier, continuous changing of data management methods can prove disruptive for business. Data fabric reduces the rate of disruption caused by these new data types and technologies while automating legacy systems to increase ROI.

Users can integrate new infrastructure without negatively impacting existing infrastructure.