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Protecting Your Business-Critical Data in the Data-Driven Economy

Scotty Calkins
Datadobi

To borrow a phrase from the British mathematician and entrepreneur, Clive Humby, "data is the new oil." It's what our economy runs on. Organizations use data to fuel their operations, make smart business decisions, improve customer relationships, and much more. Because so much value can be extracted from data its influence is generally positive, but it can also be detrimental to a business experiencing a serious disruption such as a cyberattack, insider threat, or storage platform-specific hack or bug.

If your organization was a victim of one of these scenarios and suddenly lacked access to data repositories, would operations screech to a halt?

Your data is basically a carbon copy of your company and if you don't have access to your business-critical data, you can't do business. If you are unfortunate enough to be attacked, you need a plan in place to restore your data quickly and to any target if you don't want to suffer costly downtime. During major disruptions, certain copies of your data may be unavailable for quite some time. But you can get your business-critical apps up and running quickly and keep an uninterrupted flow of business if you add an extra layer of protection for business-critical data — what's called a "golden copy."


Identifying Business-Critical Data

Organizations with petabytes of data may initially find it daunting to identify the datasets that are critical to keeping the business. Business-critical data will look different for every organization; generally, it's any data absolutely needed to continue running your business.

A widely used fitness tracker and watchmaker experienced a massive outage late last year, leaving users disconnected from their applications for days after suffering a ransomware attack. No one was able to access their user history from the application, leaving the organization unable to continue operations. The application data would be considered business-critical data in this scenario; it's exactly the type of data that needs to be protected and accessible.

Air Gapping and Traditional Disaster Recovery

Once an organization identifies its business-critical data, it can add an additional air-gap solution for an extra layer to their traditional business continuity plan. An air-gap solution — storing your data in a bunker site (whether on-premises or in the cloud) that is isolated from your network — provides an extra layer of security against both insider and external threats.

An air-gap solution puts a barrier between the golden copy of business-critical data and employees who may unintentionally (or in rare cases intentionally) do harm to it. Instead, control is shifted to a limited set of cyber-protection administrators, often under the auspices of the legal department or risk management department. In order to open the bunker site, it will require a specific set of protocols to be in place. Organizations can set the number of steps in the pipeline. The harder data is to access, the harder it is to attack it, and the more protected it is. Moving control of the golden copy to very strict procedures prevents any malicious insiders from attacking data.

An air-gap solution not only safely isolates a golden copy of your most important data, it also gives you an all-important copy of your company as well. The air gap gives organizations the security of knowing they are protected from attackers whether from within or outside of the company. It gives organizations the ability and flexibility to get back up and running as fast as possible.

In a data-driven economy, attacks on data have the ability to cripple business. Adding a golden copy of business-critical data gives organizations peace of mind, and the ability to stay afloat in the midst of a crisis.

Scotty Calkins is Senior Systems Engineer at Datadobi

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Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

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Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Protecting Your Business-Critical Data in the Data-Driven Economy

Scotty Calkins
Datadobi

To borrow a phrase from the British mathematician and entrepreneur, Clive Humby, "data is the new oil." It's what our economy runs on. Organizations use data to fuel their operations, make smart business decisions, improve customer relationships, and much more. Because so much value can be extracted from data its influence is generally positive, but it can also be detrimental to a business experiencing a serious disruption such as a cyberattack, insider threat, or storage platform-specific hack or bug.

If your organization was a victim of one of these scenarios and suddenly lacked access to data repositories, would operations screech to a halt?

Your data is basically a carbon copy of your company and if you don't have access to your business-critical data, you can't do business. If you are unfortunate enough to be attacked, you need a plan in place to restore your data quickly and to any target if you don't want to suffer costly downtime. During major disruptions, certain copies of your data may be unavailable for quite some time. But you can get your business-critical apps up and running quickly and keep an uninterrupted flow of business if you add an extra layer of protection for business-critical data — what's called a "golden copy."


Identifying Business-Critical Data

Organizations with petabytes of data may initially find it daunting to identify the datasets that are critical to keeping the business. Business-critical data will look different for every organization; generally, it's any data absolutely needed to continue running your business.

A widely used fitness tracker and watchmaker experienced a massive outage late last year, leaving users disconnected from their applications for days after suffering a ransomware attack. No one was able to access their user history from the application, leaving the organization unable to continue operations. The application data would be considered business-critical data in this scenario; it's exactly the type of data that needs to be protected and accessible.

Air Gapping and Traditional Disaster Recovery

Once an organization identifies its business-critical data, it can add an additional air-gap solution for an extra layer to their traditional business continuity plan. An air-gap solution — storing your data in a bunker site (whether on-premises or in the cloud) that is isolated from your network — provides an extra layer of security against both insider and external threats.

An air-gap solution puts a barrier between the golden copy of business-critical data and employees who may unintentionally (or in rare cases intentionally) do harm to it. Instead, control is shifted to a limited set of cyber-protection administrators, often under the auspices of the legal department or risk management department. In order to open the bunker site, it will require a specific set of protocols to be in place. Organizations can set the number of steps in the pipeline. The harder data is to access, the harder it is to attack it, and the more protected it is. Moving control of the golden copy to very strict procedures prevents any malicious insiders from attacking data.

An air-gap solution not only safely isolates a golden copy of your most important data, it also gives you an all-important copy of your company as well. The air gap gives organizations the security of knowing they are protected from attackers whether from within or outside of the company. It gives organizations the ability and flexibility to get back up and running as fast as possible.

In a data-driven economy, attacks on data have the ability to cripple business. Adding a golden copy of business-critical data gives organizations peace of mind, and the ability to stay afloat in the midst of a crisis.

Scotty Calkins is Senior Systems Engineer at Datadobi

Hot Topics

The Latest

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...