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The Hybrid Takeover

Catherine Wong
Domo

When I think about the word "hybrid," the first thing that pops into my mind is hybrid cloud models, which are de-facto in many organizations. But what I've come to see over this past year is that hybrid isn't just about the technology, hybrid models are taking over our daily lives.

I witnessed first-hand how it took years of learning and adjusting to determine how to do hybrid cloud right, in order to optimize a company's technology investments. And in the end, we as an industry have realized that there's no one-size-fits-all when it comes to cloud vs on-prem with your technology strategy. The need for flexibility and accessibility moved organizations towards the hybrid model: a way to get the best of both worlds while meeting the unique needs of the company and its business.

Today's new normal is vacillating across a spectrum of hybrid models, too. The 2020 pandemic forced all of us to shift our work and home lives, and quickly. For example, at work, teams are trying to manage a hybrid of remote workers and in-office workers, while schools and parents are trying to manage remote and in-person learning.

Unlike major tech shifts which have sometimes taken years to be realized, this new model has been an overnight jolt to our systems. And it's been anything but smooth. In fact, for most of us, it's broken. Very broken.

You hear about kids who are unable to join their classes online due to lack of access to WiFi or laptops, or teachers who are running on fumes trying to design education plans that cover both in-person or online learning simultaneously. In the workforce, you're seeing droves of parents — mostly women — leaving their jobs to manage their families through this pandemic.

We don't have a decade to figure out the new hybrid world, but I have faith that together we'll work it out because we've seen the silver lining of finding the best of both worlds through hybrid models.

Capitalizing on the best of both remote and in-person work/school will be determined by tailoring the benefits to your company's or family's needs. Organizations and individuals are on a fast path to finding their hybrid: At work, how are we re-envisioning our schedules, the level of flexibility, and even the physical design of our workspaces? Each team and perhaps type of role will have different needs but will optimize to find their best mode of balance. At school, how can curriculum for various subjects be optimized to take advantage of the best of remote learning practices and experiences?

One thing is certain: Flexibility and adaptability are key, as both benefits and requirements to developing the hybrid model that enables you to thrive. Those attitudes will future-proof our new normal now, and in the event other crises come to jolt the system, we'll be ready with a best of both worlds hybrid solution.

Catherine Wong is Chief Product Officer at Domo

Hot Topics

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 ...

The Hybrid Takeover

Catherine Wong
Domo

When I think about the word "hybrid," the first thing that pops into my mind is hybrid cloud models, which are de-facto in many organizations. But what I've come to see over this past year is that hybrid isn't just about the technology, hybrid models are taking over our daily lives.

I witnessed first-hand how it took years of learning and adjusting to determine how to do hybrid cloud right, in order to optimize a company's technology investments. And in the end, we as an industry have realized that there's no one-size-fits-all when it comes to cloud vs on-prem with your technology strategy. The need for flexibility and accessibility moved organizations towards the hybrid model: a way to get the best of both worlds while meeting the unique needs of the company and its business.

Today's new normal is vacillating across a spectrum of hybrid models, too. The 2020 pandemic forced all of us to shift our work and home lives, and quickly. For example, at work, teams are trying to manage a hybrid of remote workers and in-office workers, while schools and parents are trying to manage remote and in-person learning.

Unlike major tech shifts which have sometimes taken years to be realized, this new model has been an overnight jolt to our systems. And it's been anything but smooth. In fact, for most of us, it's broken. Very broken.

You hear about kids who are unable to join their classes online due to lack of access to WiFi or laptops, or teachers who are running on fumes trying to design education plans that cover both in-person or online learning simultaneously. In the workforce, you're seeing droves of parents — mostly women — leaving their jobs to manage their families through this pandemic.

We don't have a decade to figure out the new hybrid world, but I have faith that together we'll work it out because we've seen the silver lining of finding the best of both worlds through hybrid models.

Capitalizing on the best of both remote and in-person work/school will be determined by tailoring the benefits to your company's or family's needs. Organizations and individuals are on a fast path to finding their hybrid: At work, how are we re-envisioning our schedules, the level of flexibility, and even the physical design of our workspaces? Each team and perhaps type of role will have different needs but will optimize to find their best mode of balance. At school, how can curriculum for various subjects be optimized to take advantage of the best of remote learning practices and experiences?

One thing is certain: Flexibility and adaptability are key, as both benefits and requirements to developing the hybrid model that enables you to thrive. Those attitudes will future-proof our new normal now, and in the event other crises come to jolt the system, we'll be ready with a best of both worlds hybrid solution.

Catherine Wong is Chief Product Officer at Domo

Hot Topics

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 ...