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Hybrid Cloud Management Platforms Help Control Data and Costs

More enterprises are implementing hybrid cloud management platforms as they diversify their IT environments to overcome the limits of relying solely on public clouds, according to a new research report published by Information Services Group (ISG).

The 2024 ISG Provider Lens™ global Private/Hybrid Cloud — Data Center Solutions report finds that organizations want the flexibility, scalability and agility of cloud computing while addressing their unique operational, regulatory and security challenges. In many cases, intelligently planned hybrid cloud platforms help them control expenses, data residency and compliance.

"Companies that are worried about the economy want to get more out of their IT investments," said Anay Nawathe, ISG cloud delivery lead. "With strong management, private and hybrid cloud infrastructures can maximize operational efficiency and financial resilience."

Along with these benefits, hybrid clouds bring more complexity, especially with the need for resource coordination across platforms and smooth data flow between on-premises and cloud infrastructure, ISG says. This requires specialized tools and skills, so enterprises are implementing hybrid cloud management platforms that let them get the most out of each cloud environment and minimize performance bottlenecks.

Organizations are also under pressure to make IT infrastructure more resilient, increasing the demand for backup and disaster recovery platforms, the report says. These create copies of critical data and systems so operations can quickly resume after a cyberattack or natural disaster. Scalable, secure and cost-effective resiliency solutions are becoming as crucial as primary on-premises and public cloud infrastructure.

AI and ML play growing roles in both cloud management and resilience platforms, ISG says. Companies are embracing AI and ML cloud management tools that use data from various sources to predict downtime and initiate self-healing tools, enhancing reliability. Such technologies are also being used to automate backup and recovery platforms, some of which use algorithms to identify and respond to anomalies or threats in real time.

"Faster response and recovery to a disruption minimizes any loss of revenue and productivity, while at the same time improving customer satisfaction," said Jan Erik Aase, partner and global leader, ISG Provider Lens Research. "Vendors are helping enterprises achieve these gains through AI and automation."

Companies are also tightening control over data in both cloud management and resilience platforms using privacy-enhancing features, the report says. These include access controls and encryption key management that allow them to define and enforce granular access policies.

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Hybrid Cloud Management Platforms Help Control Data and Costs

More enterprises are implementing hybrid cloud management platforms as they diversify their IT environments to overcome the limits of relying solely on public clouds, according to a new research report published by Information Services Group (ISG).

The 2024 ISG Provider Lens™ global Private/Hybrid Cloud — Data Center Solutions report finds that organizations want the flexibility, scalability and agility of cloud computing while addressing their unique operational, regulatory and security challenges. In many cases, intelligently planned hybrid cloud platforms help them control expenses, data residency and compliance.

"Companies that are worried about the economy want to get more out of their IT investments," said Anay Nawathe, ISG cloud delivery lead. "With strong management, private and hybrid cloud infrastructures can maximize operational efficiency and financial resilience."

Along with these benefits, hybrid clouds bring more complexity, especially with the need for resource coordination across platforms and smooth data flow between on-premises and cloud infrastructure, ISG says. This requires specialized tools and skills, so enterprises are implementing hybrid cloud management platforms that let them get the most out of each cloud environment and minimize performance bottlenecks.

Organizations are also under pressure to make IT infrastructure more resilient, increasing the demand for backup and disaster recovery platforms, the report says. These create copies of critical data and systems so operations can quickly resume after a cyberattack or natural disaster. Scalable, secure and cost-effective resiliency solutions are becoming as crucial as primary on-premises and public cloud infrastructure.

AI and ML play growing roles in both cloud management and resilience platforms, ISG says. Companies are embracing AI and ML cloud management tools that use data from various sources to predict downtime and initiate self-healing tools, enhancing reliability. Such technologies are also being used to automate backup and recovery platforms, some of which use algorithms to identify and respond to anomalies or threats in real time.

"Faster response and recovery to a disruption minimizes any loss of revenue and productivity, while at the same time improving customer satisfaction," said Jan Erik Aase, partner and global leader, ISG Provider Lens Research. "Vendors are helping enterprises achieve these gains through AI and automation."

Companies are also tightening control over data in both cloud management and resilience platforms using privacy-enhancing features, the report says. These include access controls and encryption key management that allow them to define and enforce granular access policies.

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80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...