InformationWeek: Data Fabric, Six Top Use Cases

Originally published on InformationWeek, January 20, 2022

A data fabric management architecture optimizes access to distributed data while intelligently curating and orchestrating it for self-service delivery. Here's a look at some ways a data fabric may be able to help your organization.

John Edwards, Technology Journalist & Author

Data fabrics are central data management frameworks that allow organizations to access their data from any endpoint within a hybrid cloud environment. “They use technologies and services to enrich the data and make it more useful for users,” explains David Proctor, senior database manager at Everconnect, which remote database administration and support.

Data fabrics are becoming increasingly popular as organizations turn to digital storage methods. As a company grows, storage can become more complex as data is stored in different locations that are inaccessible to other parts of the organization, Proctor observes. “Data fabrics standardize … and make data accessible for everyone regardless of their location/position in the company.”

In a nutshell, data fabric technology is the glue that binds all an organization’s data systems together into a cohesive and uniform layer, says Sean Knapp, founder and CEO of Ascend.io, which offers an autonomous dataflow service. It allows data engineers to build, scale, and operate continuously optimized, Apache Spark-based pipelines with less code. “A data fabric gives organizations the ability to maintain complex and disparate data systems while giving business users fast, self-service access to the data they need -- no matter where it sits or how it’s previously been siloed,” he explains.

How can data fabric adoption help your organization? Consider the following six use cases:

1. Enterprise innovation

Data fabric technologies can open new paths to innovation -- particularly in accelerating the data and analytics lifecycle -- success for their AI, ML, and analytics initiatives, Knapp says. “For organizations looking to integrate multiple data sources, clouds, compute engines, domains, and systems, implementing a data fabric architecture is not a question of if, but when.”

2. Preventative maintenance

Data fabric technology can be used to conduct preventive maintenance analysis, helping to reduce downtime. Data fabric can access insights from various data points and predict, in advance, the preventive maintenance cycle. “This will also help plot spares, equipment, personnel, and materials required in an organized manner,” says Jared Stern, CEO of Uplift Legal Funding.

3. Slaying silos

Say sayonara to silos. “The interwoven approach that gives data fabric its name also makes it the first technology that can truly end data silos,” says Dan DeMers, CEO and co-founder of Cinchy, which claims to offer the world's first autonomous data fabric.

“We all know the downside,” DeMers says. “Despite the benefits, silos hamper productivity, yet there hasn’t been an acceptable replacement because there was no way to get around app-dependent databases.” Enterprises run on data, not applications, yet the two areas are inextricably connected. “That’s why point-to-point integration remains a big problem in the data architecture -- any effort to remove silos led to bigger silos and did nothing to address the fundamental problem of app-centric databases,” he explains. “The autonomous data fabric decouples data from the application, making it possible to embrace a data-centric philosophy and escape the buy/build/integrate paradigm.”

4. Deeper customer insights

Data fabrics show enterprises how customers use their services. “In this way, they can compile different sets of customer-related data, analyze it, and use it to create strategies that enhance a customer’s overall experience,” Proctor says.

A data fabric can be used to achieve a single view, or source of truth, of enterprise customers, explained Szymon Klarman, chief knowledge architect at BlackSwan Technologies. “Organizations can use a data fabric to cross-reference data points and make inferences; gain an intuitive view and analysis of entity relationship networks; and align with other profiles the organization may already maintain,” he says. “This ‘Customer 360’ view spans the needs of departments ranging from compliance to marketing/segmentation and risk/underwriting.”

5. Enhanced regulatory compliance

The requirements for data privacy and security are increasingly important and have become a key corporate risk that must be mitigated, says John Wills, field CTO at Alation, a company offering a machine learning technology that helps users find, understand, and trust data across their organizations. “Mitigation is dependent on enterprise risk management that includes data governance,” he notes. “[A] data governance program is dependent on the data fabric because it’s the system of record for all data assets, dependencies, quality, risk profiles, classification, and usage policy enforcement.”

6. Improving data accessibility across healthcare organizations

Data fabrics are impactful in these enterprise areas because they are data-rich, need to store massive amounts of valuable content, and rely heavily on the sharing of knowledge to fuel research and drive innovation. “The challenge is that both of these industries have a strong legacy of operating in silos, which has made the sharing of this knowledge exceptionally difficult in today’s digital world,” says Christopher Bouton, CEO and founder of Vyasa, which offers deep learning and software analytics to life sciences and healthcare organizations. “Data fabrics provide the secure and flexible environment these industries need ... without requiring a massive rework of their IT infrastructure.”