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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...