Data Fabrics: Six Top Use Cases

Data Fabrics are centralized 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 to users,” explains David Proctor, Senior Database Manager at Everconnect, a remote database management and support support group.

Data cloths are becoming increasingly popular as organizations shift to digital storage methods. As a company grows, warehousing can become more complex as data is stored in different locations that are inaccessible to other parts of the organization, Proctor notes. “Data Fabrics standardizes… and makes data accessible to everyone regardless of their position/location in the company.”

In short, data fabric technology is the glue that holds all enterprise data systems together in a cohesive, unified layer, says Shaun Knapp, founder and CEO of Ascend.io, which offers an independent data flow service. It allows data engineers to build, extend, and run continuously improved pipelines based on Apache Spark with less code. “The data fabric gives organizations the ability to maintain complex and disparate data systems while giving business users quick, self-service access to the data they need – no matter where it is located or how it was previously isolated,” he explains.

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

1. Enterprise innovation

Data fabric technologies can open up new avenues for innovation — particularly in accelerating the data and analytics lifecycle — for the success of AI, machine learning, and analytics initiatives, Knapp says. “For organizations looking to integrate multiple data sources, the cloud, engines, domains, and systems, implementing a data fabric architecture is not a question of if, but when.”

2. Preventive maintenance

Data texture technology can be used to perform preventive maintenance analysis, which helps reduce downtime. The data texture can access insights from different data points and predict in advance the preventive maintenance cycle. “This will also help chart the parts, equipment, people and materials needed in an orderly manner,” says Jared Stern, CEO of Uplift Legal Funding.

3. Slaughter of silos

Says Sayonara Sayonara to the silos. “The interlocking approach that gives the data texture 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 independent data fabric.

“We all know the downside,” Demers says. “Despite the benefits, silos hamper productivity, yet there was no acceptable alternative because there was no way to get around application-dependent databases.” Companies work on data, not applications, yet the two areas are closely related. Which is why point-to-point integration remains a huge problem in the data structure – any effort to remove silos has led to larger silos and done nothing to address the fundamental problem of application-centric databases, he explains. “The Autonomous Data Fabric separates data from the application, making it possible to embrace a data-centric philosophy and escape the buy/build/integrate model.”

4. Deeper Insights for Customers

Data fabrics show organizations how customers use their services. “This way, they can collect, analyze, and use different sets of customer-related data to create strategies that enhance the overall customer experience,” Proctor says.

Shimon Klarman, chief knowledge engineer at BlackSwan Technologies, explained that the data texture can be used to achieve a single view, or source of truth, for enterprise customers. “Organizations can use the data texture to cross-reference data points and make inferences; to obtain an intuitive view and analysis of entity relationship networks; and to align with other profiles the organization may already maintain.” This ‘customer 360’ view covers departmental needs ranging from compliance to marketing/retail and risk/underwriting.

5. Strengthening regulatory compliance

Data privacy and security requirements are becoming increasingly important and become a major risk to mitigate, says John Wells, chief technology officer at Alation, a company that provides machine learning technology that helps users find, understand, and trust data. organizations. He notes that “mitigation depends on enterprise risk management that includes data governance.” “[A] Data management software relies on the data texture because it is the recording system for all data assets, dependencies, quality, risk profiles, classification, and usage policy implementation. “

6. Improving data accessibility across healthcare and academic institutions

Data tissues are influential in these enterprise areas because they are rich in data, need to store vast amounts of valuable content, and rely heavily on knowledge sharing to support research and drive innovation. Says Christopher Bouton, CEO and founder of Vyasa, which provides deep learning and analytics software for life sciences and healthcare organizations. “Data fabrics provide the secure and resilient environment these industries need…without the need for massive reworking of the IT infrastructure.”

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