Is Hybrid the New Modern?

We all know that beneath the promise and hype of AI is a critical dependence on data. How and where that data is and should be managed, especially for feeding and training AI models, is still a widely debated topic. Security and risk concerns around generative AI are seeing many organizations stick with on-prem environments, while others are aggressively leaning into the cloud.  

In our most recent market research, we asked 3,100 global IT leaders about where they are in their evolution towards a “modern data stack.” We defined a modern data stack as “a cloud-based infrastructure [like a cloud data lake] with flexible, modular tools, new data sources like IoT, and data generated in real-time.”

Spoiler alert: Only 10% of respondents reported having a modern data stack, as we defined it. The majority (73%) reported having either on-premise or hybrid environments.

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Defining the modern data stack

So, was our definition of “modern” off base? Or are we just getting ahead of ourselves?

Our data revealed a reality that most organizations are facing: Despite the attractive efficiencies, flexibility, and availability of the cloud, many organizations — and entire industry sectors — are still in various states of transition.

Our pool of IT leaders reported spending 3x more on cloud infrastructure compared to on-prem in the past year – and the majority responded that they expect to maintain their established level of spending on both cloud and on-prem into the next 12 months.

But here’s what a lot of cloud and tech vendors won’t tell you: It’s ok. Really.

For the purposes of AI and analytics, a hybrid environment is perfectly functional — and can provide the best of both worlds while you’re on your journey to the cloud. In fact, most organizations report running generative AI applications in hybrid environments.

“Hybrid analytics” has become a legitimate strategy approach because it offers a balanced solution that capitalizes on the strengths of both cloud-based and on-premises environments, where many mission-critical processes remain.

Organizations need to minimize disruption. They need to preserve the integrity of the analytics process they’ve already built, while also allowing these processes to operate successfully in the cloud.

So, what’s a hybrid organization to do with their analytical processes, while on their path to the cloud?

Hybrid analytics

Start by working with a platform that meets you — and your users — where you are: maximizing the best of your analytical processes in both environments. Transition the execution environment to the cloud, but continue to author on the desktop, where users have familiarity. Give them flexibility and adaptability by enabling them to seamlessly switch between local execution and cloud execution based on their workload requirements.

In essence, deliver a “create with desktop, manage and execute with cloud” experience. It’s possible, and given the reality of the modern hybrid organization, it’s a strategy that may be around a while.

Here are 3 additional reminders for organizations operating in a hybrid environment while transitioning to cloud: 

1.     Transition your analytics assets to the cloud: Move where your analytics assets are currently stored and executed from on-premises resources to cloud-based resources. Start by transitioning the execution environment to the cloud, but continue to author on the desktop, where your users are already familiar. Enable users to seamlessly switch between local execution and cloud execution based on their workload requirements, providing flexibility and adaptability.

2.     Manage and integrate data deliberately: Implement data integration that aligns with your cloud-native architecture by using centralized connection management that offers robust security features, seamless integration, and unparalleled flexibility and scalability. Manage and organize all your connections in one place, distribute connections across multiple users or teams, and seamlessly execute and manage your connectivity assets for cloud. Prioritize integration planning before the transition.

3.     Prioritize data security: Implement data integration that aligns with your cloud-native architecture by using centralized connection management that offers robust security features, seamless integration, and unparalleled flexibility and scalability. Manage and organize all your connections in one place, distribute connections across multiple users or teams, and seamlessly execute and manage your connectivity assets for cloud.

So, the hybrid and transitional states our market research shows us are perfectly workable for executing enterprise data, AI and analytics strategies — and even optimal for some.  But as author Dave Vellante said, “AI + data + volume economics will determine the fundamental structure of the industry in the coming years.”

Read more about hybrid strategies to accelerate your transition to the cloud.

Newsletter roundup – Accelerating to the cloud

The Data Stack in the Age of Generative AI

How to Build a Modern Data Stack That People Can Actually Use

Analytics Maturity Snapshot: Is Cloud Integration the Solution to Data Access Problems?

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