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The Hybrid Landscape: Planning The Path To Cloud Migration Success

Jeff Veis
Actian

Data can be hard — knowing where to get it, where to store it, and most importantly, how to use it, are all questions enterprises need to answer. For most companies, this is an ongoing process in which multiple factors and challenges have arisen.

In the Actian Datacast 2020: Hybrid Data Trends Snapshot, we shed light on the challenges of cloud migration and how organizations are leveraging data. Surveying over 300 Chief Information Officers (CIOs) — the IT Decision Makers (ITDMs), and Chief Data Officers (CDOs) — the Data Decision Makers (DDMs), a few key takeaways emerged:

■ Hybrid landscapes are unavoidable

■ There are unexpected complications with cloud migrations

■ Many lessons have been, and continue to be, learned

■ DDMs have very real concerns around data migration

■ ITDMs, DDMs and their teams are challenged in working together

Embracing the Hybrid Landscape

For many enterprises, hybrid environments are unavoidable. Due to the large amount of data these companies already have in existing systems, as well as the various compliance requirements they are mandated to abide by, a cloud-only strategy is not always a viable option. In fact, 85% of enterprises surveyed stated that they have data both on-premise and in the cloud.

Perhaps surprisingly, less than 10% IT departments surveyed have more than 5% of their data in the cloud. This is primarily due to security concerns, cost predictability, regulatory and compliance issues, legacy applications, and budget allocated to the maintenance of existing data warehouses.

For these businesses, cloud migration is an ongoing conversation and requires executives to weigh multiple factors — from existing investments, to skill sets, to service delivery practices — before making the move.

Unexpected Complications with Cloud Migration

The original hype around the cloud included ease of migration, flexibility of use, and dramatic cost savings. Unfortunately, the reality of the cloud has not always lived up to the expectations, particularly around data security, real-time reporting and predictable cost savings.

Regardless of the key driver for moving to the cloud, 70% of ITDMs experienced challenges during the move, while 59% experienced many more complications than they originally anticipated. In fact, less than 20% of ITDMs stated they had a seamless cloud migration experience. Comparing these statistics, companies have a higher probability of hitting roadblocks than having a perfectly smooth experience, so something to consider and be prepared for.

While in many cases digital transformation has been equated to cloud migration, the requirements and needs of one business often do not match those of a different company. The digital transformation journey of each company is unique, and the chosen cloud strategy should reflect an organization's specific needs.

Lessons Learned from Cloud Migration

In looking back at past migration efforts, more organizations are realizing just how unique and varied transformation is, even across their own business units. In fact, 63% of ITDMs stated they would handle the migration process differently the second time around. For instance, 37% said they underestimated the project complexity, 29% said they did too much at once, and 27% said that they did not sufficiently understand the tools.

To combat these would-be challenges in the future, ITDMs need complete visibility into potential complexities before beginning the journey, and must better understand the importance of adequate preparation, workload selection, education and support.

Ultimately, a cloud migration journey should benefit the company — whether from a cost saving, compliance, security, or other business requirements. If that means taking a bit more time at the beginning to choose the proper cloud strategy that works best, then build that into your roadmap from the start.

The Top Concerns of Data Decision Makers

The desire for real-time data analytics was most often cited as the driver for cloud migration, according to the survey. However, even with so much data at our fingertips, many organizations are still not using that data to its fullest potential.

Nearly 6 in 10 DDMs said they are spending more time, rather than less, on traditional reporting. And while traditional reporting can still drive business impact it was reported that only slightly over half of the data available is actually being used to drive impactful business decisions. This means nearly half of an organization's date is being "left on the table."

IT and Data Teams - Working Together

Due to the proliferation of data that enterprises experience, its effective use comes from IT and data teams working together. Here the survey reveals a "tale of two cities" with IT teams being more likely to say they work well with the data team, while data teams are more likely to say that the two teams have material differences. At the core of this difference of perception is a core misunderstanding of each other's needs and constraints. For instance, DDMs state security and timely data as the top two challenges they experience when working with the IT team — they don't understand why it's so hard to have ready and secure access to the data they need.

As the needs and requirements of each of these teams continues to change, particularly within the IT organization, the skills needed will also change. Over time, this shift is likely to result in closing the gap of understanding between providers and consumers of data.

Data is both our greatest commodity and greatest challenge. Understanding what a specific organization needs in terms of cloud strategy, and the best course of action for implementing that strategy, is the pathway to success. Given the impacts from the pandemic, and other factors, agility is paramount to today's data driven enterprise, but so is cost containment, system complexity and capability. Take the time to plot out the strategy that is most beneficial to the company — a hybrid roadmap is likely what will get you to the next level.

Jeff Veis is CMO at Actian

Hot Topics

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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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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 Landscape: Planning The Path To Cloud Migration Success

Jeff Veis
Actian

Data can be hard — knowing where to get it, where to store it, and most importantly, how to use it, are all questions enterprises need to answer. For most companies, this is an ongoing process in which multiple factors and challenges have arisen.

In the Actian Datacast 2020: Hybrid Data Trends Snapshot, we shed light on the challenges of cloud migration and how organizations are leveraging data. Surveying over 300 Chief Information Officers (CIOs) — the IT Decision Makers (ITDMs), and Chief Data Officers (CDOs) — the Data Decision Makers (DDMs), a few key takeaways emerged:

■ Hybrid landscapes are unavoidable

■ There are unexpected complications with cloud migrations

■ Many lessons have been, and continue to be, learned

■ DDMs have very real concerns around data migration

■ ITDMs, DDMs and their teams are challenged in working together

Embracing the Hybrid Landscape

For many enterprises, hybrid environments are unavoidable. Due to the large amount of data these companies already have in existing systems, as well as the various compliance requirements they are mandated to abide by, a cloud-only strategy is not always a viable option. In fact, 85% of enterprises surveyed stated that they have data both on-premise and in the cloud.

Perhaps surprisingly, less than 10% IT departments surveyed have more than 5% of their data in the cloud. This is primarily due to security concerns, cost predictability, regulatory and compliance issues, legacy applications, and budget allocated to the maintenance of existing data warehouses.

For these businesses, cloud migration is an ongoing conversation and requires executives to weigh multiple factors — from existing investments, to skill sets, to service delivery practices — before making the move.

Unexpected Complications with Cloud Migration

The original hype around the cloud included ease of migration, flexibility of use, and dramatic cost savings. Unfortunately, the reality of the cloud has not always lived up to the expectations, particularly around data security, real-time reporting and predictable cost savings.

Regardless of the key driver for moving to the cloud, 70% of ITDMs experienced challenges during the move, while 59% experienced many more complications than they originally anticipated. In fact, less than 20% of ITDMs stated they had a seamless cloud migration experience. Comparing these statistics, companies have a higher probability of hitting roadblocks than having a perfectly smooth experience, so something to consider and be prepared for.

While in many cases digital transformation has been equated to cloud migration, the requirements and needs of one business often do not match those of a different company. The digital transformation journey of each company is unique, and the chosen cloud strategy should reflect an organization's specific needs.

Lessons Learned from Cloud Migration

In looking back at past migration efforts, more organizations are realizing just how unique and varied transformation is, even across their own business units. In fact, 63% of ITDMs stated they would handle the migration process differently the second time around. For instance, 37% said they underestimated the project complexity, 29% said they did too much at once, and 27% said that they did not sufficiently understand the tools.

To combat these would-be challenges in the future, ITDMs need complete visibility into potential complexities before beginning the journey, and must better understand the importance of adequate preparation, workload selection, education and support.

Ultimately, a cloud migration journey should benefit the company — whether from a cost saving, compliance, security, or other business requirements. If that means taking a bit more time at the beginning to choose the proper cloud strategy that works best, then build that into your roadmap from the start.

The Top Concerns of Data Decision Makers

The desire for real-time data analytics was most often cited as the driver for cloud migration, according to the survey. However, even with so much data at our fingertips, many organizations are still not using that data to its fullest potential.

Nearly 6 in 10 DDMs said they are spending more time, rather than less, on traditional reporting. And while traditional reporting can still drive business impact it was reported that only slightly over half of the data available is actually being used to drive impactful business decisions. This means nearly half of an organization's date is being "left on the table."

IT and Data Teams - Working Together

Due to the proliferation of data that enterprises experience, its effective use comes from IT and data teams working together. Here the survey reveals a "tale of two cities" with IT teams being more likely to say they work well with the data team, while data teams are more likely to say that the two teams have material differences. At the core of this difference of perception is a core misunderstanding of each other's needs and constraints. For instance, DDMs state security and timely data as the top two challenges they experience when working with the IT team — they don't understand why it's so hard to have ready and secure access to the data they need.

As the needs and requirements of each of these teams continues to change, particularly within the IT organization, the skills needed will also change. Over time, this shift is likely to result in closing the gap of understanding between providers and consumers of data.

Data is both our greatest commodity and greatest challenge. Understanding what a specific organization needs in terms of cloud strategy, and the best course of action for implementing that strategy, is the pathway to success. Given the impacts from the pandemic, and other factors, agility is paramount to today's data driven enterprise, but so is cost containment, system complexity and capability. Take the time to plot out the strategy that is most beneficial to the company — a hybrid roadmap is likely what will get you to the next level.

Jeff Veis is CMO at Actian

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 ...