Skip to main content

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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...