A multi-cloud strategy is not the same as a collection of data silos in several clouds.

Instead, a shared data repository makes data available across clouds at the same time, eliminating the need to store and manage numerous copies of the same data. According to most accounts, IT innovation is accelerating at an unprecedented rate. The pandemic sparked a surge in cloud adoption and digital transformation.

According to McKinsey, digitization advanced by 3–4 years, with major investments made for rapid cloud migrations. However, as the use of numerous clouds is increasing (and often into silos), data gravity is decreasing. Lock-in with a single cloud provider, stranded data, and high prices are some of the drawbacks of data gravity. A shared data repository, on the other hand, makes the same data available for use by numerous cloud services across clouds at the same time, eliminating the need to keep several (and potentially out-of-sync) copies of the same data. It’s worth taking a deeper look at data gravity’s consequences and how new ways are attempting to minimize them.

Data that is siloed in the cloud should be avoided.

A multi-cloud strategy is not the same as a collection of data silos in several clouds. Organizations frequently wind up with a disconnected cloud data implementation as a result of failing to address the requirement for a coordinated strategy to integrate cloud initiatives. When jobs are separated into silos, it is difficult to manage and use data effectively.

According to Gillian Tett’s book The Silo Effect: The Peril of Expertise and the Promise of Breaking Down Barriers, the fragmentation generated by silos can result in wasted resources, “tunnel vision,” and “information bottlenecks.” These dangers are unavoidable when employing numerous clouds without a well-defined and well-executed strategy. A successful multi-cloud strategy requires more than depending on a mixture of several clouds in 2021, which IDC predicts will be “the year of multi-cloud.” As part of a comprehensive strategy to integrate across an organization’s chosen clouds, multi-cloud necessitates a single data repository, which gives practical benefits to your cloud data storage operations.

A Disjointed Approach is a Problem

When companies migrate to several clouds, the process usually proceeds like this:
Take a look at an application. Put its data in the most popular public cloud, such as Amazon Web Services (AWS). AWS computation, network, and storage are all available.
Consider a different application. Put it on a second public cloud this time, such as Azure, and use Azure computing, network, and storage.
Create a new silo in another cloud and repeat the process.

This strategy serves a purpose at first. Users can choose from hundreds of public cloud services thanks to the continually rising number of options. Users choose the cloud service that best serves their urgent needs. This could be due to multiple reasons, including ensuring high availability, meeting a global, regional availability requirement, a desire to employ a cloud’s hardware and software offerings, or delivering a specific financial benefit.

They then migrate the workload to the appropriate cloud. Large, file-based data collections, as well as artificial intelligence and machine learning (AI/ML) workloads, necessitate a lot of storage and compute power. Once data is in a cloud, data gravity — the result of attracting and utilizing other apps and data — is so strong that moving it out of that cloud becomes extremely difficult (or perhaps impossible), thus trapping that data in a silo. This task is difficult enough when dealing with 100 terabytes (TB) of storage, but it becomes far more difficult when dealing with 20-30 petabytes (PB).

For compute-intensive high-performance workload projects utilized by numerous industries, such as predictive financial analytics, driverless vehicles, and genetic sequencing, the amount of organized and unstructured data is easily in the PB-zone. When attempting to relocate a huge data collection out of its present silo, costs quickly mount. When you opt to repatriate data to process it, you will be charged egress fees (which vary by cloud and are determined by the gigabytes of data being transported out of the cloud). You could make a second duplicate of the data instead of taking it out of the cloud, but this strategy increases the time, complexity, and cost of data management.

Duplicated sets of data that are each accessible exclusively by a single cloud present additional obstacles such as numerous IPs, numerous volumes, and complex management with inflated costs and sync difficulties. As you move forward, these issues limit your ability to use your data in another cloud, preventing you from fully utilizing cloud service offerings and the new potential they give. A common data repository is required to enable the use of multiple cloud services at the same time — a genuine multi-cloud strategy — while minimizing overall costs and optimizing performance and availability for a specific application.

You may access your data without having to move it.

It’s critical not to paint yourself into a hole when it comes to harnessing the power of your data and future-proofing your organization. You can construct a flat network that connects to several public clouds and your on-premises environment by using a single copy of data that is available across many public clouds. Determine the optimum location for your workload to execute and deploy it to any cloud without the need to move or replicate data. This multi-cloud architecture enables business users and developers to choose their preferred cloud services, maximize the performance and availability of each workload, reduce overall costs, and break free from vertical silos.

Separating the application management layer and creating a single common data management plane:

Flexibility and fluidity are aided: Access to the location where you want each application to operate can be turned on or off. This eliminates the need to transport data between clouds and instance types when accessing best-in-class cloud data services from each cloud. You can migrate more workloads to the cloud and scale to take advantage of available services since you can activate a workload and access the data. This similar technique provides for mobility with containerized settings as the popularity of container-based apps like Kubernetes develops.

Enhances data analysis: To unleash the value of your data, make the most of data analytics platforms (such as Azure Databricks) and optimized instances (such as those supplied by Yellowbrick Data).

Secures and strengthens data sovereignty: When you hold your data in a central data lake that you can utilize with any cloud provider, you can know exactly where it is and keep control of it. This makes life easier for you to meet a variety of regulatory and compliance needs.

Conquers data gravity: Data gravity leads to vendor lock-in, stranded data, and expensive bills with a single cloud provider. You won’t be weighed down by data gravity if you have a single copy of data accessible from any cloud provider.

Assists in the acquisition of a measurable budget: When employing a single data set instead of native file storage with the common data set on various clouds, storage costs are reduced by 89 percent (Enterprise Storage Group, February 2021 paper). These large cost savings allow you to better manage your overall cloud spending and free up funds for other important objectives.

Unnecessary segmentation and waste resulting from a verticalized approach to data management in the public cloud remains one of the most dissatisfactory factors. So, the best suggestion is – to move away from silos and toward a real multi-cloud data services strategy.

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