Top 5 Data Infrastructure Trends to Watch in 2026

Carlo Finotti
DataStrike

IT organizations are preparing for 2026 with increased expectations around modernization, cloud maturity, and data readiness. At the same time, many teams continue to operate with limited staffing and are trying to maintain complex environments with small internal groups. These conditions are creating a distinct set of priorities for the year ahead.

The DataStrike 2026 Data Infrastructure Survey Report, based on responses from nearly 280 IT leaders across industries, points to five trends that are shaping data infrastructure planning for 2026.

1. Rising Budgets Are Not Resolving Operational Gaps

According to the survey, 74% of IT leaders expect their budgets to increase in 2026. This indicates a strong organizational interest in improving infrastructure and addressing long-standing needs. However, the survey also shows that greater funding does not necessarily translate into expanded internal capacity.

More than half of respondents report that they still lack the internal resources needed to address issues promptly or support initiatives that require sustained technical focus. Database administration is a clear example. Only about one third of organizations employ dedicated database administrators (DBAs), and many of those teams consist of only one or two people responsible for managing a range of platforms, including Oracle, SQL Server, PostgreSQL and cloud-native environments. Because the average DBA salary exceeds $100,000, building larger internal teams is not always cost-effective.

As a result, many organizations are entering 2026 with more financial support but without the personnel required to fully leverage it. This imbalance is shaping technology choices, modernization timelines and the degree to which teams must rely on outside support.

2. Legacy System Modernization Is a Central Priority for 2026

The survey identifies modernization of legacy systems as the top challenge for 2026, with 46% of IT leaders selecting it as their primary concern. This indicates a shift from last year's focus on tool sprawl and adoption patterns. Modernization is now viewed as a prerequisite for supporting current demands and future growth.

Legacy systems often anchor critical workflows, but their limitations can affect performance, scalability and integration with cloud-native or distributed architectures. The survey results suggest that organizations are preparing to address these constraints directly. Modernization efforts may involve platform updates, restructuring of data environments, or reconfiguration of underlying infrastructure to support more flexible and efficient operations.

The elevated focus on modernization also reflects broader pressures to support data-driven initiatives and to improve reliability as workloads grow larger and more varied.

3. Technical Debt Has Become a Significant Operational Burden

A total of 33% of respondents identify technical debt as a primary challenge for 2026. This indicates a growing awareness of the impact accumulated constraints have on day-to-day operations and long-term planning.

Technical debt in data infrastructure can involve outdated configurations, aging database versions, integration points that no longer align with current workflows or architectural decisions that limit scalability. The presence of technical debt affects issues ranging from performance and availability to the ability to adopt new platforms or support emerging workloads.

According to the survey, more IT leaders recognize that unresolved technical debt can slow modernization, complicate cloud operations, and limit the effectiveness of new initiatives. As a result, addressing technical debt has shifted from a background task to a more deliberate component of planning for 2026.

4. Data Strategy Development Is Now a High-Priority Infrastructure Task

The survey shows that 61% of IT leaders rank development of a data strategy as their top priority for the coming year. This signals a broader shift in how organizations view the role of data planning within infrastructure management.

A data strategy encompasses decisions about data models, governance, lifecycle management, workload placement, integration patterns, and the use of cloud or open-source platforms. The report notes increasing adoption of open-source databases such as PostgreSQL as organizations work to reduce dependency on proprietary systems and manage costs more effectively.

As AI-related workloads grow in prominence, many organizations are reassessing how prepared their data environments are to support them. The emphasis on data strategy reflects the need for more coherent and better-aligned foundations before implementing large-scale changes or advanced analytics initiatives.

5. MSP Adoption Is Rising as Skill Requirements Expand

One of the most notable findings is the continued rise in reliance on managed service providers. The survey reports that 60% of organizations now use MSPs for data infrastructure support. This represents more than double the rate reported in DataStrike's 2025 survey.

This trend indicates a systematic shift in how organizations are filling skill gaps and managing increasingly diverse environments. MSPs are being used to offset staffing limitations, extend coverage across more platforms, or maintain systems that require specialized expertise. For many organizations, external support is becoming an integral component of their operating model as internal teams remain small. This trend is likely to grow as technologies are changing at a faster rate than what occurred over the last 5 - 10 years.

The report shows that organizations are planning for a year defined by modernization requirements, greater attention to data strategy, and increased dependence on external expertise. While budgets are growing, staffing limitations continue to shape what internal teams can realistically support. As a result, the most significant work ahead involves balancing investment with structural constraints and ensuring that data environments are prepared for evolving demands.

Carlo Finotti is SVP of Delivery at DataStrike

Related Links

Hot Topics

The Latest

The Corporate Network Is Fading - Here's What Replaces It

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 ...

IT Leaders Must Establish a Network Data Architecture Practice

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 ...

Data Center Outages Are Decreasing but More Costly

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 ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

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 ...

TLS Certificate Expiration Is Becoming an Observability Problem

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 ...

Enterprise Cloud Observability: Choosing the Right Starting Point

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 ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Why VM and Container Monitoring Is Not Enough for Cloud-Hosted Autonomous Agents

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Beyond Monitoring: Why IT Operations Must Evolve into Decision Operations

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...

Top 5 Data Infrastructure Trends to Watch in 2026

Carlo Finotti
DataStrike

IT organizations are preparing for 2026 with increased expectations around modernization, cloud maturity, and data readiness. At the same time, many teams continue to operate with limited staffing and are trying to maintain complex environments with small internal groups. These conditions are creating a distinct set of priorities for the year ahead.

The DataStrike 2026 Data Infrastructure Survey Report, based on responses from nearly 280 IT leaders across industries, points to five trends that are shaping data infrastructure planning for 2026.

1. Rising Budgets Are Not Resolving Operational Gaps

According to the survey, 74% of IT leaders expect their budgets to increase in 2026. This indicates a strong organizational interest in improving infrastructure and addressing long-standing needs. However, the survey also shows that greater funding does not necessarily translate into expanded internal capacity.

More than half of respondents report that they still lack the internal resources needed to address issues promptly or support initiatives that require sustained technical focus. Database administration is a clear example. Only about one third of organizations employ dedicated database administrators (DBAs), and many of those teams consist of only one or two people responsible for managing a range of platforms, including Oracle, SQL Server, PostgreSQL and cloud-native environments. Because the average DBA salary exceeds $100,000, building larger internal teams is not always cost-effective.

As a result, many organizations are entering 2026 with more financial support but without the personnel required to fully leverage it. This imbalance is shaping technology choices, modernization timelines and the degree to which teams must rely on outside support.

2. Legacy System Modernization Is a Central Priority for 2026

The survey identifies modernization of legacy systems as the top challenge for 2026, with 46% of IT leaders selecting it as their primary concern. This indicates a shift from last year's focus on tool sprawl and adoption patterns. Modernization is now viewed as a prerequisite for supporting current demands and future growth.

Legacy systems often anchor critical workflows, but their limitations can affect performance, scalability and integration with cloud-native or distributed architectures. The survey results suggest that organizations are preparing to address these constraints directly. Modernization efforts may involve platform updates, restructuring of data environments, or reconfiguration of underlying infrastructure to support more flexible and efficient operations.

The elevated focus on modernization also reflects broader pressures to support data-driven initiatives and to improve reliability as workloads grow larger and more varied.

3. Technical Debt Has Become a Significant Operational Burden

A total of 33% of respondents identify technical debt as a primary challenge for 2026. This indicates a growing awareness of the impact accumulated constraints have on day-to-day operations and long-term planning.

Technical debt in data infrastructure can involve outdated configurations, aging database versions, integration points that no longer align with current workflows or architectural decisions that limit scalability. The presence of technical debt affects issues ranging from performance and availability to the ability to adopt new platforms or support emerging workloads.

According to the survey, more IT leaders recognize that unresolved technical debt can slow modernization, complicate cloud operations, and limit the effectiveness of new initiatives. As a result, addressing technical debt has shifted from a background task to a more deliberate component of planning for 2026.

4. Data Strategy Development Is Now a High-Priority Infrastructure Task

The survey shows that 61% of IT leaders rank development of a data strategy as their top priority for the coming year. This signals a broader shift in how organizations view the role of data planning within infrastructure management.

A data strategy encompasses decisions about data models, governance, lifecycle management, workload placement, integration patterns, and the use of cloud or open-source platforms. The report notes increasing adoption of open-source databases such as PostgreSQL as organizations work to reduce dependency on proprietary systems and manage costs more effectively.

As AI-related workloads grow in prominence, many organizations are reassessing how prepared their data environments are to support them. The emphasis on data strategy reflects the need for more coherent and better-aligned foundations before implementing large-scale changes or advanced analytics initiatives.

5. MSP Adoption Is Rising as Skill Requirements Expand

One of the most notable findings is the continued rise in reliance on managed service providers. The survey reports that 60% of organizations now use MSPs for data infrastructure support. This represents more than double the rate reported in DataStrike's 2025 survey.

This trend indicates a systematic shift in how organizations are filling skill gaps and managing increasingly diverse environments. MSPs are being used to offset staffing limitations, extend coverage across more platforms, or maintain systems that require specialized expertise. For many organizations, external support is becoming an integral component of their operating model as internal teams remain small. This trend is likely to grow as technologies are changing at a faster rate than what occurred over the last 5 - 10 years.

The report shows that organizations are planning for a year defined by modernization requirements, greater attention to data strategy, and increased dependence on external expertise. While budgets are growing, staffing limitations continue to shape what internal teams can realistically support. As a result, the most significant work ahead involves balancing investment with structural constraints and ensuring that data environments are prepared for evolving demands.

Carlo Finotti is SVP of Delivery at DataStrike

Related Links

Hot Topics

The Latest

The Corporate Network Is Fading - Here's What Replaces It

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 ...

IT Leaders Must Establish a Network Data Architecture Practice

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 ...

Data Center Outages Are Decreasing but More Costly

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 ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

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 ...

TLS Certificate Expiration Is Becoming an Observability Problem

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 ...

Enterprise Cloud Observability: Choosing the Right Starting Point

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 ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Why VM and Container Monitoring Is Not Enough for Cloud-Hosted Autonomous Agents

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Beyond Monitoring: Why IT Operations Must Evolve into Decision Operations

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...