Data management in a multi-cloud world


To say that enterprise cloud computing has gone mainstream is an understatement. In fact, data insights company statistic estimates that this year, for the first time, the amount of business data stored in the cloud will exceed the amount of business data stored on-premises. And not by an insignificant margin: by 2022, 60% of business data will be stored in the cloud.

According to analyst firm GartnerBy 2025, cloud computing spend in the application software, infrastructure software, business process services, and system infrastructure technology segments will exceed spending on traditional IT solutions in the same categories.

Much of this transition to the cloud involves a concept called multi-cloud.

Infographic created by HotWire Networks

The evolution to multi-cloud

Enterprise cloud computing is a simple concept in itself: delivering computing services, such as applications (software-as-a-service, or SaaS), databases (platform-as-a-service, or PaaS), storage (infrastructure-as-a-service, or a-service, or IaaS), etc.—over the Internet (aka the cloud) rather than an organization hosting them physically on premises on local computers or in an on-premises data center.

The benefits may include: flexibility, including scalability and mobility; efficiency, including accessibility and speed to the market; and costs, including pay-as-you-go models and eliminating hardware costs.

From there it gets a little more complicated as the conversation changes from what it is to how an enterprise implements it.

While it is theoretically possible to move 100% to the cloud (more so for small to medium sized organizations), in most cases it is not practical, especially for enterprises. There will almost always be a reason to keep at least some computing resources on site, be it related to cost, security, compliance, etc.

That leads to the concept of the hybrid cloud: some computing resources are moved to the cloud while others are left on-premise. In its most basic form, the hybrid cloud model uses a single public cloud service provider. In a perfect world, that public cloud service provider could deliver best-in-class SaaS, PaaS, and IaaS at a reasonable cost.

But we don’t live in a perfect world.

Enter: multicloud. The multi-cloud approach to enterprise cloud computing takes the hybrid cloud model and introduces multiple public cloud service providers to meet different needs. A subcategory of multi-cloud is poly-cloud, meaning that the same level of care and control in deciding which computing resources to host in the cloud as those left on-premise is applied when choosing which public cloud service providers should be used for individual users. services are used based on their specialization, security, cost, etc.

The multi-cloud data management challenges

Even with a poly-cloud level of precision, data management in a multi-cloud infrastructure is much more difficult than an on-premises or basic hybrid cloud environment. Add to that the fact that the shift to multi-cloud is often done at least in part without that level of precision: IT can rely on carefully orchestrated primary cloud service providers, but individual business units haphazardly request or board themselves (AKA shadow IT) additional public cloud service providers based on their micro needs.

So, for all its benefits – and necessity – the multi-cloud approach can create new challenges in data management, including more:

• Complexity: I’ve said it before and I’ll say it again: “Far too many companies think they’re buying a solution when they move to the cloud. In reality, they are just buying infrastructure.” Essentially, the servers may not be yours, but the data and ultimate responsibility for them still are. “Managing one workload and its data in a cloud environment may be easy, but managing dozens in multiple cloud environments with different management tools is not.” As complexity increases, so does the difficulty of ensuring that the right data compliance and governance barriers are in place for any cloud environment.

• Cost: While a key benefit of enterprise cloud computing is potential cost savings, improper data management in multi-cloud environments can quickly add up to skyrocketing costs. Postpone again until Gartner, the analyst firm says organizations often overspend up to 70% on cloud services without getting the expected value. This includes data backup and recovery and archiving solutions that are not built for multi-cloud infrastructures.

• Vulnerability: As complexity increases, so does the number of potential vulnerabilities in an enterprise’s attack surface, increasing the risk of data breaches such as ransomware. Not only that, but recovering from an attack gets harder.

Embracing multi-cloud without sacrificing data management

So the dilemma is that multi-cloud is in many ways the ideal approach to enterprise cloud computing, but how do organizations overcome the challenges of data management?

First, consolidate data management. Having end-to-end visibility and control over your entire data domain — from your on-premises resources to all your public cloud service providers — through a single glass is essential to reducing the complexity of multi-cloud environments. However, the reality is that most of today’s data management technologies are not ideal for working in multi-cloud environments. Instead, deploy cloud-optimized, at-scale data management that applies web-scale technologies to deliver more cost-effective, efficient, and secure data management from the edge to the core to the cloud(s).

Second, automated data management. It is already difficult for IT teams to keep up with data management needs in today’s multi-cloud environments, and the challenge will only become more difficult as the number of cloud services businesses rely on grows, ransomware attacks and other threats to data integrity grow, and data privacy rules are getting stricter. In my last column I outlined automation, autonomy, artificial intelligence (AI) and machine learning in relation to data management. I emphasized the need for “AI-driven technology that can deliver, self-optimize, and self-heal data management services fully autonomously for the massive amounts of data in the multi-cloud environments that enterprises are migrating to. My company and others in the industry have already started working with these solutions.”

Just as it is an understatement to say that cloud computing has become mainstream for businesses, it is also an understatement to say that multi-cloud is the present and future of enterprise cloud computing implementation. Overcome the challenges it poses with unified, autonomous data management.