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How Next-Gen Data Management Can Help You Navigate the Hybrid Multicloud World

Chris Wiborg
Cohesity

Once upon a time data lived in the data center. Now data lives everywhere.

You have data in the data center, data at edge locations used by remote offices, data on mobile devices, and data in the cloud. And when a business has data in the cloud, it usually doesn't mean just one cloud.

Chances are good that you have data in SaaS applications like Microsoft 365, Salesforce, and other applications, clouds, and systems. Organizations are increasingly adopting hybrid multicloud strategies. So, some of your data might live on AWS, Google Cloud Platform, and/or Microsoft Azure.

All this signals the need for a new approach to data management, a next-gen solution — one that gives the power of choice to an organization to design a data management strategy that best meets its unique business needs. At the same time, the entire process of data management must be simplified. The era of multiple legacy point solutions to handle a company's data needs cannot meet the needs of a modern enterprise that must manage, protect, and derive business value from its data to compete and succeed.

Understand That Distributed Data Creates New Mobility and Security Implications

When your data lives in many different places, there are several implications.

You need to address data logistics — or how to get data from one place to another. In some cases you are moving data to the cloud. But sometimes you may want to repatriate data, which involves moving data back from the cloud.

Additionally, you have to rethink your approach to security. When all of your data lived in your data center, you could protect it with a hard perimeter around that data center. But because data is now everywhere, your security model must change and adopt zero trust principles.

Now you need to manage data everywhere in a way that is efficient and effective. Your data management approach should start with protecting and backing up your data. This will help you to recover if you have an outage or you are attacked by ransomware, which is growing at an alarming rate.

Take Responsibility Rather Than Assuming That Data in the Cloud Is Safe

You may think that when you put data in the cloud it is automatically protected. But just because your Microsoft 365 implementation is in the cloud, it doesn't mean Microsoft can bring back your data if things go wrong. Microsoft 365 retains customer content for 30 days at most.

Microsoft, Google, and AWS may offer guarantees related to their cloud services' uptime and availability. But you are responsible for making sure your data is secure and accessible for compliance, legal, and other purposes. This is known as the cloud's shared responsibility model. Under this model, you are responsible for your data — even if an employee mistakenly or intentionally deletes that data or you fall victim to ransomware or another type of cyberattack.

But not everybody operating in today's hybrid multicloud world understands that because SaaS and IaaS are relatively new models, and many IT operations teams and other talent responsible for resiliency aren't fully aware of the limitations and risks cloud poses when it comes to your data.

Avoid Creating More Silos By Taking a Centralized Approach

Your database provider may tell you that its database provides native online backup. But that is a siloed approach that adds complexity from a broader operations perspective rather than enabling modernization and simplification.

The best way to avoid silos is to implement a centralized data management solution that protects and lets you manage your data — in the cloud and on premises — using a single administrative interface.

Be Aware That As-A-Service Disaster Recovery Is An Effective Option

You may choose to back up all of your cloud, software-as-a-service, and on-premises data using a self-managed backup solution. But now data management companies also offer additional resiliency via disaster recovery-as-a-service (DRaaS) solutions. This means you now have the flexibility to choose between managing everything on your own or letting your DRaaS provider focus on managing the infrastructure, while you focus on the policies that will govern your data — where the value resides.

Whether you choose to manage your own infrastructure, consume as-a-service options, or adopt a flexible hybrid approach — as more and more organizations are choosing — make sure that your data management solution addresses all of your needs, wherever your data resides.

By consolidating "one off" solutions and adopting a next-gen data management platform approach you can simplify complexity and lower the costs involved with managing your data. At the same time, this approach will allow you to follow an operational strategy that is best for your business while helping you to avoid data mobility problems, and letting you recover faster when disaster strikes.

Now you can more easily protect your data. More importantly, you can protect your business.

Chris Wiborg is VP of Product Marketing at Cohesity

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

How Next-Gen Data Management Can Help You Navigate the Hybrid Multicloud World

Chris Wiborg
Cohesity

Once upon a time data lived in the data center. Now data lives everywhere.

You have data in the data center, data at edge locations used by remote offices, data on mobile devices, and data in the cloud. And when a business has data in the cloud, it usually doesn't mean just one cloud.

Chances are good that you have data in SaaS applications like Microsoft 365, Salesforce, and other applications, clouds, and systems. Organizations are increasingly adopting hybrid multicloud strategies. So, some of your data might live on AWS, Google Cloud Platform, and/or Microsoft Azure.

All this signals the need for a new approach to data management, a next-gen solution — one that gives the power of choice to an organization to design a data management strategy that best meets its unique business needs. At the same time, the entire process of data management must be simplified. The era of multiple legacy point solutions to handle a company's data needs cannot meet the needs of a modern enterprise that must manage, protect, and derive business value from its data to compete and succeed.

Understand That Distributed Data Creates New Mobility and Security Implications

When your data lives in many different places, there are several implications.

You need to address data logistics — or how to get data from one place to another. In some cases you are moving data to the cloud. But sometimes you may want to repatriate data, which involves moving data back from the cloud.

Additionally, you have to rethink your approach to security. When all of your data lived in your data center, you could protect it with a hard perimeter around that data center. But because data is now everywhere, your security model must change and adopt zero trust principles.

Now you need to manage data everywhere in a way that is efficient and effective. Your data management approach should start with protecting and backing up your data. This will help you to recover if you have an outage or you are attacked by ransomware, which is growing at an alarming rate.

Take Responsibility Rather Than Assuming That Data in the Cloud Is Safe

You may think that when you put data in the cloud it is automatically protected. But just because your Microsoft 365 implementation is in the cloud, it doesn't mean Microsoft can bring back your data if things go wrong. Microsoft 365 retains customer content for 30 days at most.

Microsoft, Google, and AWS may offer guarantees related to their cloud services' uptime and availability. But you are responsible for making sure your data is secure and accessible for compliance, legal, and other purposes. This is known as the cloud's shared responsibility model. Under this model, you are responsible for your data — even if an employee mistakenly or intentionally deletes that data or you fall victim to ransomware or another type of cyberattack.

But not everybody operating in today's hybrid multicloud world understands that because SaaS and IaaS are relatively new models, and many IT operations teams and other talent responsible for resiliency aren't fully aware of the limitations and risks cloud poses when it comes to your data.

Avoid Creating More Silos By Taking a Centralized Approach

Your database provider may tell you that its database provides native online backup. But that is a siloed approach that adds complexity from a broader operations perspective rather than enabling modernization and simplification.

The best way to avoid silos is to implement a centralized data management solution that protects and lets you manage your data — in the cloud and on premises — using a single administrative interface.

Be Aware That As-A-Service Disaster Recovery Is An Effective Option

You may choose to back up all of your cloud, software-as-a-service, and on-premises data using a self-managed backup solution. But now data management companies also offer additional resiliency via disaster recovery-as-a-service (DRaaS) solutions. This means you now have the flexibility to choose between managing everything on your own or letting your DRaaS provider focus on managing the infrastructure, while you focus on the policies that will govern your data — where the value resides.

Whether you choose to manage your own infrastructure, consume as-a-service options, or adopt a flexible hybrid approach — as more and more organizations are choosing — make sure that your data management solution addresses all of your needs, wherever your data resides.

By consolidating "one off" solutions and adopting a next-gen data management platform approach you can simplify complexity and lower the costs involved with managing your data. At the same time, this approach will allow you to follow an operational strategy that is best for your business while helping you to avoid data mobility problems, and letting you recover faster when disaster strikes.

Now you can more easily protect your data. More importantly, you can protect your business.

Chris Wiborg is VP of Product Marketing at Cohesity

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...