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KM leverages data mesh

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Keeping data secure in a distributed environment

The federated nature of data mesh architecture can lead to challenges with data security. Each application may have its own security measures, but reconciling them to produce a coherent policy can be difficult. Immuta is a security platform that protects sensitive data from unauthorized access, data loss, and noncompliance. “The idea of data mesh is to go to a decentralized model,” said Claude Zwicker, senior product manager at Immuta. “On a small scale, security can be manageable, but doing it at scale is a different matter.”

Achieving data security in the context of data mesh is in large part comparable to doing so in any data environment. “The first step is to discover all the data the enterprise has,” noted Zwicker, “and tag it with the appropriate metadata, such as PII. The second step is to build policies that secure the data. Finally, the solution needs to have audit capability to detect and mitigate suspicious activity.” Immuta can be used with any architecture, whether centralized, distributed, or hybrid.

“In a centralized architecture, the core team needs to have knowledge of all domains,” Zwicker observed. “In a federated setting, those who sit close to the data are more knowledgeable about it. On the other hand, making sure everyone is compliant can be difficult in a distributed environment because there are many roles, often across many platforms. Creating a common data plan helps build policies in a virtualized data layer.”

Usually, organizations adopting data mesh do not do it all at once. According to Zwicker, “It evolves over time and should be considered a journey. One business unit will be the early adopter, and others will look to them for insights into how knowledge sharing can take place in the data mesh environment.”

The biggest barrier is usually organizational readiness. “It’s necessary to identify local champions, but there are also some shifts required in the centralized security department, which may have concerns about security in a distributed environment.” New channels of communication are required during a transition. “Developing an overarching view is important,” concluded Zwicker. “Even though the data itself is distributed, implementing data mesh requires people to think at an enterprise level.”

Finding meaning in data

The benefits to KM are significant when a model is used in which data owners manage data that they know well and govern carefully, consumable data products have been created, and the products are then made available through self-service. Additional advantages can be gained through presentation of the data in a knowledge graph that provides a semantic layer. Just as raw data can be federated, normalized, and mapped into an analytics solution, it can be transformed using the Resource Description Framework (RDF) to represent it and then semantically described using Web Ontology Language (OWL) to harmonize its meaning. These transformations can then represent the data as a triple that contains a subject, predicate, and object that form a statement about that data item, with links to other triples, to be used in a knowledge graph.

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