Organizations are adopting multi-cloud analytics to unify data, reduce silos, and leverage best-in-class tools for faster, compliant, and cost-efficient insights.
Single-cloud analytics can’t keep up with the scale, speed, reach, and complexity of today’s data.
Analytics teams need the freedom to choose the right tools, run workloads closer to where data is generated, and meet region-specific compliance. That’s why more organizations are turning to multi-cloud.
Multi-cloud is a strategy that uses multiple public cloud platforms to run each workload where it delivers the most value. This flexibility is quickly becoming essential. A 2024 Forrester survey found that 79% of enterprises already have, or are planning to have, multi-cloud deployments. As data volumes grow and use cases multiply, relying on a single provider increasingly limits your ability to scale, optimize, and adapt.
In this blog, we’ll go over how multi-cloud unlocks smarter analytics. We’ll also explain how to build a strategy that’s ready for what’s next.
Single-cloud environments rely entirely on one public cloud vendor for storage, compute, and analytics. Analytics thrives on access, agility, and control. But when organizations rely on a single cloud provider, they often face structural limitations that block the path to timely, actionable insight.
According to IBM, 68% of enterprise data remains unanalyzed because it’s locked in silos.
Analytics only works as well as the data it's built on. When critical data is fragmented across environments or trapped in proprietary systems, teams can’t generate a complete picture of what’s happening or what to do next.
A Gartner Peer Community poll found that 30% of tech leaders already feel “locked in,” and another 67% say switching providers would be difficult.
Every cloud provider offers strengths, but no single one excels at everything. Sticking with just one often means compromising on features, speed, or cost. For analytics teams, this translates into slower access to emerging tools, fewer options for optimization, and higher long-term costs.
Forrester notes that companies pursuing real-time insights often target a data-access threshold under 15 minutes, a target that single-cloud architectures can potentially miss.
Organizations collect data from globally distributed sources, such as IoT devices, remote branches, and edge applications. For real-time, predictive analytics, they must ensure analytics is deployed closest to where data is generated.
But with a single-cloud setup, data often travels long distances to reach centralized systems, introducing delays.
PwC’s 2024 Cloud & AI Business Survey reveals that only 52% of executives actively monitor compliance across their cloud providers, and one-third of large companies have no risk-management plan for cloud-provider threats.
These gaps can easily become vulnerabilities that lead to violating compliance requirements.
Analytics environments are tightly regulated and becoming more so with the introduction of new laws like the AI Act. Yet governance is often applied unevenly when teams concentrate analytics workloads and applications in a single cloud and rely on just one provider’s tools and controls.
Multi-cloud, by contrast, supports a more agile and resilient analytics strategy. It empowers organizations to select the best tools, adapt to regional requirements, and maintain control over both costs and performance.
This unlocks faster insights, smarter tool choices, and the ability to grow analytics without compromise.
Here’s how:
Analytics workloads vary widely between organizations. Some require high-speed warehousing for large datasets, others depend on low-latency streaming, and many are increasingly using advanced machine learning. A single cloud rarely delivers best-in-class performance across all these needs.
Multi-cloud solves this by letting teams match each workload with the provider that does it best. Need scalable storage and compute? Run it in AWS. Prefer Google Cloud’s federation and AI services? Use BigQuery. Want seamless integration with Microsoft tools? Deploy in Azure Synapse.
IDC reports that 79% of enterprises run analytics across multiple public clouds, most often mixing AWS, Azure, and Google Cloud. For analytics leaders, that means faster innovation, fewer bottlenecks, and the freedom to adopt new capabilities without waiting for a single vendor to catch up.
When data is scattered across cloud platforms, analytics slows down. Teams spend more time locating, moving, and reconciling data than extracting value from it. Multi-cloud analytics changes that by allowing you to connect and query data where it already resides.
When combined with architectures and services like virtual warehouses and lakehouses, multi-cloud enables unified access across cloud boundaries. That means analysts can work with a complete, real-time view of the business, even if the data lives in different formats or providers.
Analytics demand can be unpredictable. One week it’s steady; the next, a campaign launch or regulatory deadline floods your infrastructure. Scaling to meet those surges is critical, but doing it within a single cloud often leads to delays, degraded performance, or ballooning costs.
According to Forrester, 93% of single-cloud enterprises experienced service disruptions or cost spikes. Multi-cloud gives teams the flexibility to scale across providers as needed. If capacity runs thin in one cloud, workloads can shift to another with better availability or pricing.
This kind of agility ensures analytics stays responsive. Your organization can use this advantage to scale intelligently, keep performance high, and contain costs even when demand spikes unexpectedly.
From IoT sensors to mobile apps, most enterprise data is now generated outside traditional data centers. To turn that data into timely insight, it needs to be processed near its source, not sent across regions to a central cloud.
Multi-cloud allows teams to run analytics in cloud regions closest to where data is generated. This approach reduces latency and cuts bandwidth costs. It also supports real-time use cases such as fraud detection and inventory tracking.
Forrester reports that leading firms aim to make streaming data query-ready in under five minutes. Multi-cloud provides the flexibility to meet these expectations and build analytics systems that keep up with where and how data is actually generated.
Multi-cloud helps analytics teams stay compliant without sacrificing reach or resilience. Personally identifiable data can stay in-region to meet residency laws, while GPU-heavy workloads run abroad where capacity is available.
Policy controls move with the data, and cross-region failover ensures that analytics pipelines keep running even if a provider goes down. The result is an environment that balances performance with governance, designed for a world where both are equally important.
PwC found that 27% of cloud spend is wasted, and 84% of executives say cost control is their top concern. Multi-cloud enables smarter resource allocation, letting teams place analytics workloads where they’re most efficient in terms of both cost and performance.
Multi-cloud analytics is driving impact in industries where scale, complexity, and regulation collide. From factory floors to boardrooms, organizations are using multi-cloud analytics to unlock new forms of operational intelligence while meeting the demands of compliance and continuity.
Manufacturers like Toyota and BMW stream real-time sensor data into cloud-based AI models to catch failures before they happen. Analyzing power fluctuations or conveyor speed in real time empowered Toyota’s Indiana plant to cut downtime by 50% and reduce maintenance costs by 25%.
The architecture is intentional: telemetry is pushed to the nearest cloud region and processed using the platform with the strongest AI capabilities. Alerts are then sent back on-premise, enabling teams to take action before production is disrupted.
Predicting supply chain delays requires visibility across customs data, freight systems, and inventory signals, which are often stored across different clouds and formats. Multi-cloud analytics enables this by combining federated access with scalable compute.
IBM reports that companies using machine learning (ML) in federated cloud models can proactively adjust sourcing and production to avoid disruptions. Sensitive supplier data stays in-region, while high-volume analytics burst to other providers as needed, feeding real-time insights into planning tools across the business.
Tracking carbon and energy metrics for corporate ESG reporting demands data from IoT sensors, procurement platforms, and ERP systems. Multi-cloud analytics enables the analysis of these inputs in context. Multi-cloud analytics lets sustainability teams detect anomalies like emissions spikes and turn them into action and accountability.
Governance can be a challenge when data is siloed and workloads spread across disparate systems and environments. Multi-cloud analytics can deliver a unified and consistent view of data, workloads, and events across multiple cloud environments. It can help analyze and assess real-time log data from disparate sources as well as access controls, data encryption, and other security measures across environments, enabling security and compliance teams to proactively identify and address potential issues.
Multi-cloud analytics offers immense potential, but only if it’s implemented with clear intent and disciplined execution. These best practices offer a practical roadmap, grounded in analyst guidance, to help you make deliberate choices about architecture, tooling, and governance.
Every technical decision should trace back to a business outcome. Before choosing platforms or designing pipelines, define what you’re trying to achieve and which metrics will measure progress.
Choose interoperable, open platforms
Portability matters in multi-cloud. Teams should prioritize engines, file formats, and APIs that work consistently across environments to avoid being boxed in.
Gartner ranks governance and risk mitigation among the five foundational steps before expanding any cloud program. Security, compliance, and privacy controls should not be retrofitted after scale. They must be built early and into the pipeline.
Without unified visibility, multi-cloud quickly becomes unmanageable. Organizations should consolidate performance, spend, and usage metrics in one place, rather than spreading them across separate dashboards.
Multi-cloud analytics is a necessity for organizations dealing with complex, distributed data and trying to maintain a competitive edge. Adopting a platform such as emma cloud management platform gives analytics teams the ability to work with the best tools available, run workloads close to where data is generated, and maintain control over compliance and cost.
Shifting away from single-cloud constraints will empower you to unlock richer datasets, build smarter models, and deliver insights without interruption. If you are just beginning, anchor every decision in business outcomes. Select open tools that can adapt to your data, integrate governance from day one, and provide teams with a single pane of glass for cost and performance. Start with one high-impact workload, prove the value, then scale the pattern.
Ready to act? Try emma now and learn how to unify, automate, and optimize every cloud in one user-friendly platform.
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