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Data is a stumbling block for most multicloud deployments

Data issues are not new: data integration, data security, data management, and defining single sources of truth. However, what is new is combining these issues with multi-cloud deployments. Many of these problems can be avoided with a little planning ahead and using common data architecture best practices that have been understood for years.

The core problem, as I see it, is companies trying to lift and shift data to multi-cloud deployments without good foresight on the common issues that are likely to arise:

Formation of data silos. Using multiple cloud services can lead to isolated data silos, making it difficult to integrate and manage data across multiple platforms. This shouldn’t surprise anyone, but in many ways, multi-cloud has made data silos more numerous.

These need to be addressed using data integration approaches, such as leveraging data integration technology, data abstraction/virtualization, or other tricks that are now well understood. Or simply design your data storage systems so they’re not a silo.

Neglecting data security. Securing sensitive data across multiple cloud services can be complex and often increases security risks.

It is essential to have a strong data security strategy that addresses the unique security needs of each cloud service, but does not add to the complexity of managing data security. This often means abstracting away native security services by using a central security manager or other technology that exists on top of the public cloud provider, in other words, a supercloud or metacloud. This layer of logical technology exists above the clouds and is a concept that seems to be changing right now.

Not considering data portability. Data migration from one cloud service to another can be challenging. It is important to have a strong data portability strategy that takes into account data format, size, and dependencies.

Most multicloud movers can’t answer this question: “What would it take to migrate this data set from here to there?” This should be in your back pocket, as we are seeing some data sets move from single-cloud and multi-cloud deployments to on-premises. Options must be given.

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No centralized data management. Managing data across multiple cloud services can be a resource-intensive task if you try to do everything manually. It is essential to have a centralized data management system that can handle various data sources and ensure data consistency. Again, this needs to be centralized, abstracted on top of public cloud providers and native data management implementations. You must deal with the complexity of the data on its own terms, not in terms of the complexity of the data itself. Most opt ​​for the latter, which is a big mistake.

Lack of interoperability. The big problem is interoperability. It’s really a combination of the issues listed so far (data silos, data portability, and lack of centralized data management), but it’s worth mentioning on its own.

Ensuring the interoperability of different cloud services and cloud data can be a huge headache. It is important to have a clear understanding of the data sharing standards supported by each cloud service and a plan to close the gaps.

Most data is simply thrown into multi-cloud deployments without much thought and without interoperability mechanisms. Interoperability then becomes a tactical effort when it should be strategic and well understood before and after deployment.

The frustrating thing about all of these challenges is that they are so solvable, with well-established solution patterns and enabling technologies. Enterprises are making silly mistakes by jumping into multicloud deployments as fast as they can, and then not seeing the return on investment from multicloud or cloud migrations in general. Most of the damage is self-inflicted.

Do your homework. Plan. Leverage the right technology. It’s not that hard, and it will save you and your business a ton of time and money in the long run.

Copyright © 2023 IDG Communications, Inc.

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