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IT Pros Turn to Database Management for App Efficiency

Vicky Harp

Ensuring application availability is much more than simple up/down monitoring, as today's users have come to expect real-time responses, regardless of the device or network. While application front ends and back ends vary widely by application type, what almost all have in common is their dependency on transactional databases. To truly understand application performance, IT professionals must analyze database workloads in application context.


A recent Unisphere Research survey revealed that leading enterprises are turning to database lifecycle management (DLM) to address the complexity of modern databases. DLM involves coordinated processes, tools and people to optimize all aspects of the data lifecycle including data architecture and modeling, database design, performance monitoring, administration, security, storage and archiving.

Nearly 90 percent of survey respondents agree that the complexity of their database environment has increased over the past five years, while more than 45 percent say these environments have grown "significantly" or "extremely" more complex during this time. Getting a handle on these environments is critical to ensuring enterprise application availability and efficiency, not to mention a company's ability to conduct business and generate revenue.

Survey results indicate that nearly 80 percent of organizations now have some sort of DLM initiative underway, but most are in the early stages and 51 percent are part of existing application lifecycle management (ALM) efforts. While it's encouraging that companies understand the importance of databases to their ALM efforts, they may not be giving DLM the focus it deserves.

Close to 90 percent of companies using DLM solutions are already seeing a range of tangible business benefits as a result of their DLM efforts, including increased data systems uptime, making data more highly available to end users, increased confidence in the data, and more rapid and frequent delivery of applications.

Cloud computing represents a new way of managing data environments that may relieve enterprise data shops of some administrative burdens and provide speed and flexibility advantages for the business. For mission-critical applications, the movement to the cloud presents some sobering news. Only 19 percent of data managers surveyed indicate they intend to move a significant portion of their enterprise data to public cloud, while 26 percent intend to move to more secure private or hybrid cloud environments. These figures indicate that enterprise applications will not be able to fully leverage the benefits of data in the cloud for some time.

Aligning applications with data seems to be a key issue for many IT pros. When asked what their most pressing challenges were in managing database environments, "determining related application issues" was top of mind for 30 percent of respondents. According to one, DLM addresses this issue by enhancing the "ability to remain in step with the application."

Enterprise applications are only as effective as the databases that provide their foundation, and these survey findings make it clear that IT pros fully understand this reality. As a result, many companies are embracing DLM initiatives to ensure their mission-critical applications are deployed quickly, run with optimal efficiency and provide maximum business value.

Vicky Harp is a Corporate Strategist at IDERA.

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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 ...

IT Pros Turn to Database Management for App Efficiency

Vicky Harp

Ensuring application availability is much more than simple up/down monitoring, as today's users have come to expect real-time responses, regardless of the device or network. While application front ends and back ends vary widely by application type, what almost all have in common is their dependency on transactional databases. To truly understand application performance, IT professionals must analyze database workloads in application context.


A recent Unisphere Research survey revealed that leading enterprises are turning to database lifecycle management (DLM) to address the complexity of modern databases. DLM involves coordinated processes, tools and people to optimize all aspects of the data lifecycle including data architecture and modeling, database design, performance monitoring, administration, security, storage and archiving.

Nearly 90 percent of survey respondents agree that the complexity of their database environment has increased over the past five years, while more than 45 percent say these environments have grown "significantly" or "extremely" more complex during this time. Getting a handle on these environments is critical to ensuring enterprise application availability and efficiency, not to mention a company's ability to conduct business and generate revenue.

Survey results indicate that nearly 80 percent of organizations now have some sort of DLM initiative underway, but most are in the early stages and 51 percent are part of existing application lifecycle management (ALM) efforts. While it's encouraging that companies understand the importance of databases to their ALM efforts, they may not be giving DLM the focus it deserves.

Close to 90 percent of companies using DLM solutions are already seeing a range of tangible business benefits as a result of their DLM efforts, including increased data systems uptime, making data more highly available to end users, increased confidence in the data, and more rapid and frequent delivery of applications.

Cloud computing represents a new way of managing data environments that may relieve enterprise data shops of some administrative burdens and provide speed and flexibility advantages for the business. For mission-critical applications, the movement to the cloud presents some sobering news. Only 19 percent of data managers surveyed indicate they intend to move a significant portion of their enterprise data to public cloud, while 26 percent intend to move to more secure private or hybrid cloud environments. These figures indicate that enterprise applications will not be able to fully leverage the benefits of data in the cloud for some time.

Aligning applications with data seems to be a key issue for many IT pros. When asked what their most pressing challenges were in managing database environments, "determining related application issues" was top of mind for 30 percent of respondents. According to one, DLM addresses this issue by enhancing the "ability to remain in step with the application."

Enterprise applications are only as effective as the databases that provide their foundation, and these survey findings make it clear that IT pros fully understand this reality. As a result, many companies are embracing DLM initiatives to ensure their mission-critical applications are deployed quickly, run with optimal efficiency and provide maximum business value.

Vicky Harp is a Corporate Strategist at IDERA.

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 ...