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The Essential Tools to Support Digital Transformation - Part 3

APMdigest asked experts from across the IT industry — from analysts and consultants to users and the top vendors — for their opinions on the essential tools to support digital transformation. Part 3 covers analytics, AI and machine learning.

Start with The Essential Tools to Support Digital Transformation - Part 1

Start with The Essential Tools to Support Digital Transformation - Part 2

Advanced IT Analytics (AIA)

While there are many critical areas of technology innovation currently evolving in IT, the most powerful and transformative are advanced analytic capabilities, often integrated with insights into service (application/infrastructure) interdependencies. What EMA calls advanced IT analytics (AIA), and many in the industry call "AIOps," is an arena of fast-paced innovation in many dimensions, with diverse options for investment, and benefits ranging from improved IT-to-business alignment, improved business performance, dramatic values in toolset consolidation and unifying IT, as well as core strengths in dramatic reductions in mean-time-to-repair, as just some examples. Whether AIA can properly be called a tool or not, it typically helps to assimilate many different toolsets into a new, cross-domain layer designed for proactive rather than reactive IT management and planning.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Artificial intelligence (AI)

Artificial intelligence (AI) is becoming a mission-critical tool to support digital transformation. New development platforms like cloud and microservices enable enterprises to reach new market opportunities faster. On the flip side, more than three-quarters of CIOs around the world believe these new applications are so complex that IT is becoming almost unmanageable. As many small teams work together, getting consistent end-to-end visibility is more challenging, but also more important. Many companies try to solve this problem by growing their operations team, leading to a higher time investment and eventually increased costs. When looking at the problem more closely, it becomes obvious that most time is spent in analyzing data in the context of the impact on the business. This is where artificial intelligence (AI) can help. AI-based systems can find the root cause of problems in milliseconds no matter how complex a system, ultimately resolving application problems before customers are impacted. The next step is to use AI-based virtual assistants, which understand natural language and can provide actionable answers to complex digital performance questions in real-time. And, by simplifying conversations that can be held over voice or chat, AI help expand the use of operational data beyond IT experts.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

MACHINE LEARNING

The most important tool to support digital transformation is a modern, scalable, and fast data analytics platform with machine learning built-in. Unencumbered by legacy databases, digital economy companies disrupt traditional industries with agile approaches and modern, open analytical platforms to derive insight from heavy volumes of data right now — and not later after they have missed their opportunity. Traditional industry players are equally data driven, moving as quickly as they can to modernize their data warehouses and analytical stores and avoid disruption and minimize customer churn. Start-ups with fresh rounds of funding and 100-year old banks each understand a modern data analytical platform with machine learning built-in is imperative to digital transformation.
Jeff Healey
Senior Director of Vertica Product Marketing, Micro Focus

KPI ADVANCEMENT TOOLS

A company's ability to differentiate and win now rests largely on how expeditiously they can respond to changing business needs by rolling out high-performing, innovative online products and services. Businesses need highly productive development teams that excel across three areas: quality, velocity and efficiency. Development leaders need KPI advancement tools leveraging empirical data to guide smart decisions that drive improvements in all of these areas. Many organizations continue to rely heavily on mainframe processing. Therefore, digital transformation requires tools that go beyond just integrating the mainframe more fully into developer environments, to actually amplifying developer productivity on the platform.
Sam Knutson
VP of Product Management, Compuware

WORKSPACE ANALYTICS

The most important tool that an organization needs to drive digital transformation is access to workspace analytics. To improve the performance of its end users, an organization must have visibility into how issues experienced at the endpoint are impacting productivity. Analytics can link client-side end-user facing data regarding VDI sessions with the usage of guest resources within infrastructure environments. This collected data can then be tied back into the VDI session, presenting IT with telemetry they can then use to monitor, analyze and optimize endpoint performance within their end-user computing environments.
Simon Clephan
VP of Business Development and Strategic Alliances, IGEL

monitoring integration as a service (MIaaS)

Digital transformation has the tendency to create monitoring blind spots. You've got one foot in the cloud, one foot on prem. You're wading into DevOps and real-time analytics. You need something that's going to bring it all together for you. That's why I recommend a monitoring integration as a service (MIaaS) platform.
Moria Fredrickson
Director of Marketing, Blue Medora

Read The Essential Tools to Support Digital Transformation - Part 4, covering communication and collaboration.

The Latest

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

Image
IBM

 

The Essential Tools to Support Digital Transformation - Part 3

APMdigest asked experts from across the IT industry — from analysts and consultants to users and the top vendors — for their opinions on the essential tools to support digital transformation. Part 3 covers analytics, AI and machine learning.

Start with The Essential Tools to Support Digital Transformation - Part 1

Start with The Essential Tools to Support Digital Transformation - Part 2

Advanced IT Analytics (AIA)

While there are many critical areas of technology innovation currently evolving in IT, the most powerful and transformative are advanced analytic capabilities, often integrated with insights into service (application/infrastructure) interdependencies. What EMA calls advanced IT analytics (AIA), and many in the industry call "AIOps," is an arena of fast-paced innovation in many dimensions, with diverse options for investment, and benefits ranging from improved IT-to-business alignment, improved business performance, dramatic values in toolset consolidation and unifying IT, as well as core strengths in dramatic reductions in mean-time-to-repair, as just some examples. Whether AIA can properly be called a tool or not, it typically helps to assimilate many different toolsets into a new, cross-domain layer designed for proactive rather than reactive IT management and planning.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Artificial intelligence (AI)

Artificial intelligence (AI) is becoming a mission-critical tool to support digital transformation. New development platforms like cloud and microservices enable enterprises to reach new market opportunities faster. On the flip side, more than three-quarters of CIOs around the world believe these new applications are so complex that IT is becoming almost unmanageable. As many small teams work together, getting consistent end-to-end visibility is more challenging, but also more important. Many companies try to solve this problem by growing their operations team, leading to a higher time investment and eventually increased costs. When looking at the problem more closely, it becomes obvious that most time is spent in analyzing data in the context of the impact on the business. This is where artificial intelligence (AI) can help. AI-based systems can find the root cause of problems in milliseconds no matter how complex a system, ultimately resolving application problems before customers are impacted. The next step is to use AI-based virtual assistants, which understand natural language and can provide actionable answers to complex digital performance questions in real-time. And, by simplifying conversations that can be held over voice or chat, AI help expand the use of operational data beyond IT experts.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

MACHINE LEARNING

The most important tool to support digital transformation is a modern, scalable, and fast data analytics platform with machine learning built-in. Unencumbered by legacy databases, digital economy companies disrupt traditional industries with agile approaches and modern, open analytical platforms to derive insight from heavy volumes of data right now — and not later after they have missed their opportunity. Traditional industry players are equally data driven, moving as quickly as they can to modernize their data warehouses and analytical stores and avoid disruption and minimize customer churn. Start-ups with fresh rounds of funding and 100-year old banks each understand a modern data analytical platform with machine learning built-in is imperative to digital transformation.
Jeff Healey
Senior Director of Vertica Product Marketing, Micro Focus

KPI ADVANCEMENT TOOLS

A company's ability to differentiate and win now rests largely on how expeditiously they can respond to changing business needs by rolling out high-performing, innovative online products and services. Businesses need highly productive development teams that excel across three areas: quality, velocity and efficiency. Development leaders need KPI advancement tools leveraging empirical data to guide smart decisions that drive improvements in all of these areas. Many organizations continue to rely heavily on mainframe processing. Therefore, digital transformation requires tools that go beyond just integrating the mainframe more fully into developer environments, to actually amplifying developer productivity on the platform.
Sam Knutson
VP of Product Management, Compuware

WORKSPACE ANALYTICS

The most important tool that an organization needs to drive digital transformation is access to workspace analytics. To improve the performance of its end users, an organization must have visibility into how issues experienced at the endpoint are impacting productivity. Analytics can link client-side end-user facing data regarding VDI sessions with the usage of guest resources within infrastructure environments. This collected data can then be tied back into the VDI session, presenting IT with telemetry they can then use to monitor, analyze and optimize endpoint performance within their end-user computing environments.
Simon Clephan
VP of Business Development and Strategic Alliances, IGEL

monitoring integration as a service (MIaaS)

Digital transformation has the tendency to create monitoring blind spots. You've got one foot in the cloud, one foot on prem. You're wading into DevOps and real-time analytics. You need something that's going to bring it all together for you. That's why I recommend a monitoring integration as a service (MIaaS) platform.
Moria Fredrickson
Director of Marketing, Blue Medora

Read The Essential Tools to Support Digital Transformation - Part 4, covering communication and collaboration.

The Latest

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

Image
IBM