What is data fabric architecture?
Data fabric architecture is a virtual data layer that connects any number of external systems together to provide unified access and a complete, 360-degree view of an organization’s data. Whether your data lives in legacy systems or multi-cloud environments, a data fabric can connect these systems together for a unified view.
What makes the data fabric approach unique is that you access connected systems wherever they currently reside. That means no migration projects for you and access to real-time data for your business users. Governance capabilities within a data fabric solution also help organizations democratize data to business teams without risking breaches in compliance.
Watch this informative session and demo, Modernizing Your Data Fabric Architecture. It explains how you can democratize your data and increase the value of the data you have:
The case for data fabric
The explosion of data in today’s digital age is staggering. Every interaction, transaction, and sensor generates vast amounts of information, often in real-time. As data volumes skyrocket, your organization faces the daunting challenge of harnessing this flood of information effectively. Without the right tools and strategies, this data can quickly become overwhelming, scattered across various systems and formats, making it difficult to access, analyze, and leverage in business processes. The sheer scale and complexity of managing this data can hinder an organization’s ability to innovate, make informed decisions, achieve its business objectives, and stay competitive in an increasingly data-driven world.
As data continues to surge, organizations are urgently seeking ways to tame this deluge and turn it into actionable insights. From traditional data warehouses and data lakes to more modern approaches like data virtualization and cloud-based analytics platforms, companies have explored various methods to streamline data management. But these solutions often come with their own set of challenges—siloed data, complex integrations, and limited scalability.
This is where an advanced framework like data fabric architecture diverges from the pack. Data fabric offers a more holistic and agile approach, enabling an organization to seamlessly connect, govern, and utilize data across the entire ecosystem.
A data fabric creates a unified data management layer that simplifies access to data, regardless of where it resides. By providing a consistent and comprehensive view of data, a data fabric helps break down data silos. This streamlined access allows businesses to leverage their data more effectively and improve collaboration—enabling faster decision-making, improved analytics, and better customer experiences. Data fabric empowers your organization to be more agile, responsive, and competitive in a data-driven world.
Data fabric also enhances data governance, security, and compliance by embedding these capabilities into the architecture. This is crucial for organizations operating in highly regulated industries or handling sensitive data.
Integration of automation and AI within a data fabric further reduces the complexity of managing data, enabling organizations to automate routine tasks, improve data quality, and focus on strategic initiatives.
4 key facts about data fabric architecture.
1. A virtualized data layer simplifies data management
As mentioned above, one of the most prominent components of data fabrics is the virtualization of data. Also described as an abstraction layer, data fabric provides a unified, seamless interface for accessing and managing data across diverse sources and environments, hiding the underlying complexity of data storage and formats.
Data virtualization allows you to avoid data migration projects and access the data directly from wherever it currently resides. No additional data extraction, transformation, or loading back into another system eases the burden of making data available in applications. Data fabrics can act as a bridge between any data sources (warehouses, cloud platforms, etc.), allowing you to create a real-time, single source of truth for your enterprise data to meet business requirements faster.
2. Centralized data governance tools ensure data quality and accessibility
This single data model created with data fabrics also streamlines the data governance between the connected systems. Once established throughout your organization, this single source of truth will help to eliminate duplicative data and repetitive, manual updates to that data across several systems.
With a single data model, you can also easily manage who has access to create, read, update, and delete data. Whether you need to give an entirely new business unit access to read a field on a customer record for a customer experience initiative or allow a single user the ability to create a new record to initiate a business process, a data fabric gives you the visibility you need to sleep well at night knowing that your data is only being accessed from a secure data fabric architecture.
3. Data fabric integrates AI and process automation to revolutionize data management
Advanced technologies like artificial intelligence and process automation are integral to making data fabric work, enhancing its capabilities by automating data management tasks and enabling intelligent data operations. AI algorithms can automatically classify, organize, and cleanse data as it flows through the fabric, ensuring high data quality and consistency across the organization. They also power predictive analytics, enabling real-time insights and decision-making based on current and historical data patterns.
Process automation further streamlines data workflows by handling routine tasks such as data integration, migration, and governance. This reduces the manual effort required to manage complex data environments and allows IT teams to focus on more strategic initiatives. Together, AI and automation enable a more dynamic and self-optimizing data fabric, where data processes are continuously refined and optimized, leading to greater efficiency, accuracy, and agility in how organizations manage and leverage their data.
4. Data fabric architecture enables scalability and flexibility
Finally, data fabric architectures provide the adaptability needed in modern enterprises today. The data landscape is changing rapidly with both the proliferation of data sources and the increased scrutiny of data compliance policies and regulations. There is no “set it and forget it” model for data architectures for any use case. Organizations need to plan to make updates to models over time as business requirements change, and data fabric architectures are well equipped to do just that.
Low-code/no-code tools within a data fabric abstract away the layers of database programming that are often difficult to change and untangle over time. Data fabric technologies were built under the assumption that enterprises would continually need to add, remove, and extend data sources and users that need to access the data for business intelligence.
Data fabric architecture can modernize and future-proof your data approach
Enterprise data fabrics are more than just the latest trend in data management tools. They provide IT and development teams with the speed and agility needed to keep up with what seems like constant changes to the world of enterprise data, along with the guardrails to ensure data is still kept secure.
For enterprises that want to continue to digitally transform their organizations and lead the pack in their industries, data fabric architecture sets up a strong foundation. It democratizes access to data throughout an organization and can help uncover the business insights that can revolutionize the business. And its unified architecture enables seamless integration of advanced technologies like AI. From key customer insights to process improvements, data fabric is the new bedrock of business growth.
Want to learn more about how a data fabric can transform your organization's data management strategies? Watch the on-demand webinar, What Is a Data Fabric? The Next Best Thing in Enterprise Architecture.
This post was originally published in March 2023 and has been updated for comprehensiveness.
