The top 6 use cases for a data fabric architecture
Enterprises are turning to data fabric architectures to provide a holistic view of their data. There are different visions of how exactly to go about this, but at its core, everyone seems to agree it goes beyond data lakes, data catalogs and data virtualization to provide a more coherent integration tier.
Gartner cited data fabric architectures as one of the top 10 trends in 2019 because it enables seamless access and data sharing in a distributed data environment.
“A data fabric is an integrative approach to collecting and connecting enterprise data that stresses singularity in managing, distributing and securing data,” said Brian Platz, co-CEO and co-founder of Fluree, a blockchain data management platform.
A data fabric architecture provides a step forward in maximizing the value of information spread across data silos. It also provides a goal that the organization can align to provide streamlined and secure access to data in an otherwise complex distributed network environment.
A data fabric architecture promises a way to deal with many of the security and governance issues being raised by new privacy regulations and the rise in security breach incidents. “By far the largest positive impact of a data fabric for organizations is the focus on enterprise-wide data security and governance as part of the deployment, establishing it as a fundamental, ongoing process,” said Wim Stoop, director of product marketing at Cloudera. Data governance is often seen in isolation, tied to a use case like tackling regulatory compliance needs or departmental requirements in isolation. With a data fabric, organizations are required to take a step back and consider data management holistically. This delivers the self-service access to data and analytics businesses demand to experiment and quickly drive value from data. Such a degree of management, governance and security of data then also makes proving compliance — both industry and regulatory — more or less a side effect of having implemented the fabric itself. Although this is not a full solution, it greatly reduces the effort associated with adhering to compliance requirements.
Platz cautioned that there is a wide gulf between a vision for a perfect data fabric and what is practical today. “In practice, many first versions of data fabric architectures look more like just another data lake,” Platz said. Folks that are building a data fabric for the first time do not account for the need for inherent data interoperability. Disparate systems will format data differently. Data that does not adhere to a global enterprise schema will not inherently speak the same language. This lack of native interoperability will add friction to the time-to-value for data stakeholders and introduce the need for harmonizing, deduplicating and cleansing data. An organization needs to be able to understand its data consumption and regulatory and compliance needs in order to make proper use of its data fabric. “Not understanding one or all of those areas often creates challenges or points of failure,” said Morten Bagai, CTO at Grax, a data backup, archive and recovery service. Once organizations sort out these issues, they can begin exploring new data fabric use cases such as the following.
A data fabric architecture can provide AI engineers with access to broad, integrative data for better-informed decisions. “Because AI needs broad access to high-integrity data, a data fabric can support the efficient delivery of information to AI applications for quick, well-informed decisions,” Platz said.
