30 Ways APM Should Evolve - Part 2

APMdigest asked the top minds in the industry what they feel is the most important way Application Performance Management (APM) tools must evolve. The recommendations on this list provide a rare look into the long-term future of APM technology. Part 2 covers the evolution of the relationship between APM and analytics.

Start with 30 Ways APM Should Evolve - Part 1

6. INTEGRATION WITH ANALYTICS

The future evolution of APM solutions will depend on well chosen integrations — both to inform APM data sets, as well as to enrich to other environments, such as those associated with advanced IT analytics for performance and capacity optimization, IT service management for change management, governance and workflow, and with business analytic capabilities to optimize business and IT service outcomes. APM's real value comes from not trying to be the center of the new world order, but from becoming a central player in enabling advanced service delivery and optimization in the digital age.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

More than ever, APM tools need to go beyond the application level. In today's world of multi-tier, multi-layer, multi-component distributed systems, performance is determined by so many factors that good tools need to capture a cohesive view of all of them, but at the same time prevent the user from drowning in information overflow. That requires smart and intelligent tools that can identify what matters, not just dumb, data-gathering engines with fancy looking (but otherwise useless) UIs.
Sven Dummer
Senior Director of Product Marketing, Loggly

7. FOCUS ON CHANGE

Traditionally, APM tools are focused on early detection of symptoms of performance and availability issues. The goal is to detect them before they develop into an incident. Such approach is limited as it requires some indicators or patterns of abnormal system behavior. This means that on one hand an issue starts developing while on another, early indicators are frequently very difficult to link to the actual root cause of the issue. It is widely known in the industry that the majority of performance and availability issues are caused by changes. Focusing on analysis of actual changes as a true root cause in addition to early indicators, the APM tools will significantly improve their ability to prevent issues and minimize manual investigation linking symptoms to the root cause triggering them.
Sasha Gilenson
CEO, Evolven

8. CORRELATE LOGS AND METRICS

Application Performance Management (APM) has been around a long time, but the digital transformation that's happening today across industries and organizations of all sizes, is really becoming the key driver for evolution in this space. Traditional APM tools for monitoring provide limited analytics, create siloed views and are inadequate for effectively managing today's modern multi-tier and distributed micro-services based applications. Having real time access to the complete picture dramatically helps businesses of all sizes continuously build, run and secure modern applications. As such, the modern-day APM solution has evolved to require a more comprehensive approach that includes a unified approach for log and metric data — tying together the two most critical sources/KPIs when tracking application performance. With the right technology, correlating log and metrics data is instant, contextual and comprehensive, opening up a rich universe of opportunity that spans the full application lifecycle — from code through the entire CI/CD process/tools to end-user behaviors.
Ramin Sayar
CEO, Sumo Logic

9. INTEGRATED PERFORMANCE AND CAPACITY MANAGEMENT

In the long term, Application Performance Management (APM) tools need to continue their evolution towards becoming integrated performance and capacity management platforms, using advanced analytics to detect performance issues, attribute cause to either problem or demand load, and facilitate repair or infrastructure modifications, respectively. Toward this goal, shorter-term advances should leverage machine learning-based technology to automate the incident detection and attribution functions. Longer term, the adoption of prescriptive analytics combined with Infrastructure as Code (IaC) promises to enable smart, cost-efficient, infrastructure provisioning to accommodate varying or increasing demand.
Mike Paquette
VP, Products, Prelert

10. DATA FROM MULTIPLE SOURCES

APM tools must adapt to the proliferation of monitoring products and general complexity in the average enterprise. Those that can aggregate data from ANY source via a Common Alert Format (whilst stripping out the "noise", de-duplicating, enriching, normalizing) and present this data coherently back the business for more effective correlation of technical issues to business impact shall prevail!
Grant Glading
Sales & Marketing Director, Interlink Software

Read 30 Ways APM Should Evolve - Part 3, covering the expanding scope of APM tools.

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30 Ways APM Should Evolve - Part 2

APMdigest asked the top minds in the industry what they feel is the most important way Application Performance Management (APM) tools must evolve. The recommendations on this list provide a rare look into the long-term future of APM technology. Part 2 covers the evolution of the relationship between APM and analytics.

Start with 30 Ways APM Should Evolve - Part 1

6. INTEGRATION WITH ANALYTICS

The future evolution of APM solutions will depend on well chosen integrations — both to inform APM data sets, as well as to enrich to other environments, such as those associated with advanced IT analytics for performance and capacity optimization, IT service management for change management, governance and workflow, and with business analytic capabilities to optimize business and IT service outcomes. APM's real value comes from not trying to be the center of the new world order, but from becoming a central player in enabling advanced service delivery and optimization in the digital age.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

More than ever, APM tools need to go beyond the application level. In today's world of multi-tier, multi-layer, multi-component distributed systems, performance is determined by so many factors that good tools need to capture a cohesive view of all of them, but at the same time prevent the user from drowning in information overflow. That requires smart and intelligent tools that can identify what matters, not just dumb, data-gathering engines with fancy looking (but otherwise useless) UIs.
Sven Dummer
Senior Director of Product Marketing, Loggly

7. FOCUS ON CHANGE

Traditionally, APM tools are focused on early detection of symptoms of performance and availability issues. The goal is to detect them before they develop into an incident. Such approach is limited as it requires some indicators or patterns of abnormal system behavior. This means that on one hand an issue starts developing while on another, early indicators are frequently very difficult to link to the actual root cause of the issue. It is widely known in the industry that the majority of performance and availability issues are caused by changes. Focusing on analysis of actual changes as a true root cause in addition to early indicators, the APM tools will significantly improve their ability to prevent issues and minimize manual investigation linking symptoms to the root cause triggering them.
Sasha Gilenson
CEO, Evolven

8. CORRELATE LOGS AND METRICS

Application Performance Management (APM) has been around a long time, but the digital transformation that's happening today across industries and organizations of all sizes, is really becoming the key driver for evolution in this space. Traditional APM tools for monitoring provide limited analytics, create siloed views and are inadequate for effectively managing today's modern multi-tier and distributed micro-services based applications. Having real time access to the complete picture dramatically helps businesses of all sizes continuously build, run and secure modern applications. As such, the modern-day APM solution has evolved to require a more comprehensive approach that includes a unified approach for log and metric data — tying together the two most critical sources/KPIs when tracking application performance. With the right technology, correlating log and metrics data is instant, contextual and comprehensive, opening up a rich universe of opportunity that spans the full application lifecycle — from code through the entire CI/CD process/tools to end-user behaviors.
Ramin Sayar
CEO, Sumo Logic

9. INTEGRATED PERFORMANCE AND CAPACITY MANAGEMENT

In the long term, Application Performance Management (APM) tools need to continue their evolution towards becoming integrated performance and capacity management platforms, using advanced analytics to detect performance issues, attribute cause to either problem or demand load, and facilitate repair or infrastructure modifications, respectively. Toward this goal, shorter-term advances should leverage machine learning-based technology to automate the incident detection and attribution functions. Longer term, the adoption of prescriptive analytics combined with Infrastructure as Code (IaC) promises to enable smart, cost-efficient, infrastructure provisioning to accommodate varying or increasing demand.
Mike Paquette
VP, Products, Prelert

10. DATA FROM MULTIPLE SOURCES

APM tools must adapt to the proliferation of monitoring products and general complexity in the average enterprise. Those that can aggregate data from ANY source via a Common Alert Format (whilst stripping out the "noise", de-duplicating, enriching, normalizing) and present this data coherently back the business for more effective correlation of technical issues to business impact shall prevail!
Grant Glading
Sales & Marketing Director, Interlink Software

Read 30 Ways APM Should Evolve - Part 3, covering the expanding scope of APM tools.

Related Links

Hot Topics

The Latest

Redis Monitoring 101: Key Metrics You Need to Watch

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Beyond Traditional Autoscaling: The Future of Kubernetes in AI Infrastructure

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI Drives Surge in Data Budgets

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned Architecture Causes Service Disruptions, High Operational Costs and Security Challenges

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

How GenAI Can Save Time for the NetOps Team

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

Will AI Solve the Growing Data Divide?

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

Top Concerns for Tech Decision Makers

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

Gartner: Top Trends Shaping the Future of Cloud

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

The Great SaaS Hangover (and the Cure Nobody Is Talking About)

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

OpenShift Monitoring: 5 Things You Need to Keep an Eye on

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...