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Data Fabric 101: What You Need to Know

Laurie McLaughlin, Appian
May 24, 2023

What can data fabric do for your organization and how does it differ from other data management approaches? This primer will get you up to speed on data fabric essentials and help you explain the basics of this top tech trend. 

Data fabric in simple terms.

What is a data fabric? A data fabric connects data sets across disparate software systems, whether they’re on-premises or in the cloud, and creates a complete view. It’s both a tool set and an unified architecture layer (also called a virtualized data layer).

[ Want to learn about how data fabric works and its real-world benefits? Get the ebook: Data Fabric Guide. ]

What is a data fabric approach? 7 basics.

1. Data fabric keeps data sets in their source systems.

Thanks to the virtualized data layer, you don’t need to move data out of its current home, such as a database, enterprise resource planning (ERP) system, or customer relationship management (CRM) application, in order to work with it. People organization wide get access to real-time data, enabling better and faster data-driven decisions and innovations and giving IT teams a break from time-consuming, expensive data integration work. 

2. Data fabric enables composable design.

By using a data fabric approach, your organization empowers people to combine business data in new ways. Gartner calls this idea “composable design,” and it’s one reason Gartner named data fabric its top strategic technology trend for 2022.

3. Data fabric speeds up development work.

By connecting data directly from wherever it lives, data fabric gives you the ability to spin up applications faster and dramatically reduce time to insight for business leaders. That’s critical for digital transformation work that demands speed and agility.

4. Data fabric democratizes data access and analysis.

By providing a simplified data modeling experience, data fabric democratizes data analysis, giving access not only to skilled data engineers and developers but also to citizen developers and line-of-business employees for better business intelligence.

5. Data fabric ends data silo headaches.

At the same time as it expands data access, the data fabric approach creates a single, secure, and complete view of data across the enterprise. This significantly reduces enterprise data management challenges. Data fabric can stitch together modern and legacy data sets, putting an end to data silos that inevitably impede both information visibility and development speed. Cutting through data silos drives digital innovation and better business decisions.

6. Data fabric improves security, compliance, and governance.

Data fabric improves and centralizes security and compliance as you scale automation efforts. A data fabric can also improve document management and help you show audit trails on the compliance front, which is important for industries like financial services, insurance, healthcare, and life sciences.

A data fabric gives IT a centralized picture of who can view, update, and delete specific data sets. As organizations share more data, both internally with employees and externally with customers and partners, data fabric gives IT teams confidence that they have a governed, secure data architecture. That’s important as regulatory demands continue to increase.

7. A data fabric is not the same as a data lake.

A data lake and a data fabric have distinct differences. Like a data warehouse, a data lake’s primary goal is only to collect data in a single repository. A data fabric’s goal is to connect it. Data lakes can collect large sets of unstructured data; data warehouses collect structured data.

But with a data lake, you must lift all the data out of each system, then load it all into the lake, where it sits until you do transformation and analysis work at a later date. Developers can’t spin up a new application until all that work is done. A data lake suits analytical work but doesn’t support transactional systems that require real-time data, such as CRM applications. Data lake upkeep often creates technical debt for engineering teams over time.

[ Want to learn more about data fabric vs. older data integration technologies? Read our related article: Data fabric vs. data mesh vs. data lake.]

Data fabric tools and requirements: Process automation platforms.

Data fabric tools are a must-have component of a platform for process automation, also known as hyperautomation. That’s because in order to succeed with automation at scale, you need a strong data architecture that connects disparate data sources. With data silos scattered across the organization, you will not be able to automate an entire process end to end. 

We’re talking about complex business processes such as managing the customer lifecycle in banking, optimizing supply chain operations, or speeding up insurance underwriting—processes that involve multiple people, departments, and systems.

A process automation platform combines an array of technologies to help you optimize and streamline those entire processes, including robotic process automation (RPA), intelligent document processing (IDP), workflow orchestration, artificial intelligence (AI), system integrations, and business rules. Data fabric plays an important role here as it connects data sets across the various systems, on-premises or in cloud environments. 

In addition to data fabric tools, look for a process automation platform that provides pre-built connectors, so you can link those systems (like CRMs, ERPs, and database applications) without building the connections from scratch, and a workflow orchestration layer, which directs and smoothly passes tasks between software bots and humans.

Data fabric tutorials.

Want a data fabric tutorial that you can download and share? Download our PDFs:

  • Learn about how data fabric works and real-world benefits. Get the Data Fabric Guide

Want to see data fabric in action? Discover how you can eliminate data silos: Watch our on-demand webinar