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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

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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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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 ...