2018 Application Performance Management Predictions - Part 6

Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2018. Part 6 covers ITOA and data.

Start with 2018 Application Performance Management Predictions - Part 1

Start with 2018 Application Performance Management Predictions - Part 2

Start with 2018 Application Performance Management Predictions - Part 3

Start with 2018 Application Performance Management Predictions - Part 4

Start with 2018 Application Performance Management Predictions - Part 5

DATA GROWTH

The amount of data facing ITOps practitioners is only going to grow in the coming year and teams will be faced with the increased challenge of finding the signal in the noise — and fast — to resolve incidents. As a result, it will be necessary for ITOps to reexamine previous assumptions around automation and responsibility.
Eric Sigler
Head of DevOps, PagerDuty

SMART DATA

By utilizing smart data, which distills the essence of the traffic flows that traverse the service delivery infrastructure in a distributed fashion, close to the source, and compresses it into metadata, businesses can ensure they only store the information that holds real value. This information can then be used to gain meaningful and actionable insights, helping organizations to gain a competitive edge while driving efficiencies by enabling data to be rapidly compressed, and substantially reducing the volume of data stored by an order of magnitude or more. Smart data is already used to power a range of service, operations and business analytics across different industries including automotive, manufacturing and healthcare, and we expect its usage to increase dramatically in 2018. With the proliferation of IoT sensors, mobile devices and digital services creating an abundance of data used by the various applications and services that rely on hybrid cloud infrastructure, having the ability to convert smart data into meaningful and actionable IT and business insights, will help corporations to thrive in 2018 and beyond.
Michael Segal
Area VP, Strategy, NetScout

APM CONVERGED DATA STORES

Today's APM tools and monitoring, in general, have discreet silos of data for time-series, transactions, and logs. In 2018 We will begin to see the first converged data stores, which will unlock the ability to answer questions significantly more easily than today's tools.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

Read Jonah Kowall's Blog: Looking Back at 2017 APM Predictions - Did They Come True?

APPLICATION-CENTRIC APPROACH TO BIG DATA

In the past, people were focused on learning the various big data technologies. It took time for users to understand, differentiate, and ultimately deploy them. There was a lot of debate and plenty of hype. Now that organizations have cut through the noise and figured all that out, they're concerned about actually putting their data to use. The enterprise doesn't really care about the technology being used. It's not important which distribution or database or analytics they're using, what matters is the result. The enterprise has realized this and we can expect to see an increased adoption of an application-centric approach to big data in the coming year.
Kunal Agarwal
CEO, Unravel Data

NEW PERFORMANCE METRICS

We'll see new patterns of backend performance problems stemming from the broader adoption of containers and microservices architecture. Our performance palette will be expanded to include new measurements like micro pause delays, herd effects, cold start time etc.
Peco Karayanev
Sr. Product Manager, Riverbed

DBA TAKES ON IT OPS ROLE

The New Job Description: DBAs Take on New Responsibilities. One of the most significant changes we will see in 2018 will be toward a more collaborative relationship between IT infrastructure managers and database administrators (DBAs). As more applications are run in the cloud, senior DBA managers will be able to take more of a central role in troubleshooting problems and improving efficiency. DBAs will be looking beyond just the application and database to find and fix issues. Both IT and application teams will need tools that look deeply into the cloud infrastructure to identify causes of performance and availability issues and provide accurate recommendations for addressing them.
Jerry Melnick
President & CEO, SIOS Technology

DISTRIBUTED ANALYSIS

2018 will see a de-emphasis on centralized cloud-based management and traditional data-lakes, and a shift towards distributed analysis. This will be driven not only by explosive growth in IoT, but also by the support of edge computing by major cloud vendors
Richard Whitehead
Evangelist-in-Chief, Moogsoft

RESTFUL API DRIVES ITOA

The advent of efficient RESTful APIs on many services and applications coupled with the maturation of time-series databases such as OpenTSDB and InfluxDB will drive IT operations analytics to use more quantitative approaches, and lead to advances in root cause analysis. This is due to the high storage efficiency of the time series databases, and the speed with which the optimize-on-write approaches they use can accept data. It is now increasingly practical to track large quantitative data volumes. RESTful API endpoints from applications and cloud services are rich in metrics, and the same types of APIs are efficient at accepting such metrics in data streams. With these large volumes of contemporaneous, high-cardinality time series data sources, operations analysis will become possible at a higher scale than previously possible. Cross-correlation will yield forensic insight into failures. In contrast, predictive time series analysis based on auto-regressive/moving average models, while mathematically practical, will fail to lead to any significantly valuable results on operations data, with rare exceptions.
Thomas Stocking
Director of Sales Engineering, GroundWork

IOT INFLECTION POINT

IoT apps need to get out of the hype cycle and deal with real world pain points. Their devices are still a hassle to set up and they solve too few real world cases. Alexa has shown promise, but IoT platforms as a whole are too fragmented for developers to invest in learning them. Hardware manufacturers will agree on a common set of protocols and open up APIs for devices to work seamlessly.
Abhinav Asthana
CEO, Postman

In my opinion, the biggest occurrence in 2017 was that IoT reached peak hype, giving way to new hype-cycles for machine learning (actually, an offshoot of IoT) and for Artificial Intelligence (a familiar topic area, and one that requires IoT data as fuel for its intelligence). We saw a combination of several companies making very large investments in IoT, while others are scaling back or reorganizing their IoT teams. This combination of investment push and pull means that we're at an inflection point. For IoT, this means we're now at a point where projects have to deliver results. IoT vendors invested ahead of demand, with all sorts of claims of IoT one-stop shopping. With more capacity in the industry than there is demand, I expect we will see players drop off or shift focus.
James Kirkland
Chief Architect, IoT, Red Hat

SELF TEACHING

We've now passed the point where we know that the human brain can no longer cope with the complexity of modern applications. Meanwhile, businesses have never relied on digital services more than they do today. Today, every company is a digital company and every critical IT issue has become a business issue. Therefore, APM solutions must evolve from just being early performance-issue detection tools to providing much more insights into the other phases of the resolution process. This includes not only root cause diagnostic and identification capabilities, but also self-teaching capabilities that leverage big data and AI-based algorithms and require very limited initial configuration to deliver actionable insights and recommended remediation actions. This will allow DevOps teams to make the best possible decisions to resolve performance issues.
Vincent Geffray
Senior Director of Product Marketing, IT Response Automation, Everbridge

Read 2018 Network Performance Management Predictions, the final installment.

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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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

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2018 Application Performance Management Predictions - Part 6

Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2018. Part 6 covers ITOA and data.

Start with 2018 Application Performance Management Predictions - Part 1

Start with 2018 Application Performance Management Predictions - Part 2

Start with 2018 Application Performance Management Predictions - Part 3

Start with 2018 Application Performance Management Predictions - Part 4

Start with 2018 Application Performance Management Predictions - Part 5

DATA GROWTH

The amount of data facing ITOps practitioners is only going to grow in the coming year and teams will be faced with the increased challenge of finding the signal in the noise — and fast — to resolve incidents. As a result, it will be necessary for ITOps to reexamine previous assumptions around automation and responsibility.
Eric Sigler
Head of DevOps, PagerDuty

SMART DATA

By utilizing smart data, which distills the essence of the traffic flows that traverse the service delivery infrastructure in a distributed fashion, close to the source, and compresses it into metadata, businesses can ensure they only store the information that holds real value. This information can then be used to gain meaningful and actionable insights, helping organizations to gain a competitive edge while driving efficiencies by enabling data to be rapidly compressed, and substantially reducing the volume of data stored by an order of magnitude or more. Smart data is already used to power a range of service, operations and business analytics across different industries including automotive, manufacturing and healthcare, and we expect its usage to increase dramatically in 2018. With the proliferation of IoT sensors, mobile devices and digital services creating an abundance of data used by the various applications and services that rely on hybrid cloud infrastructure, having the ability to convert smart data into meaningful and actionable IT and business insights, will help corporations to thrive in 2018 and beyond.
Michael Segal
Area VP, Strategy, NetScout

APM CONVERGED DATA STORES

Today's APM tools and monitoring, in general, have discreet silos of data for time-series, transactions, and logs. In 2018 We will begin to see the first converged data stores, which will unlock the ability to answer questions significantly more easily than today's tools.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

Read Jonah Kowall's Blog: Looking Back at 2017 APM Predictions - Did They Come True?

APPLICATION-CENTRIC APPROACH TO BIG DATA

In the past, people were focused on learning the various big data technologies. It took time for users to understand, differentiate, and ultimately deploy them. There was a lot of debate and plenty of hype. Now that organizations have cut through the noise and figured all that out, they're concerned about actually putting their data to use. The enterprise doesn't really care about the technology being used. It's not important which distribution or database or analytics they're using, what matters is the result. The enterprise has realized this and we can expect to see an increased adoption of an application-centric approach to big data in the coming year.
Kunal Agarwal
CEO, Unravel Data

NEW PERFORMANCE METRICS

We'll see new patterns of backend performance problems stemming from the broader adoption of containers and microservices architecture. Our performance palette will be expanded to include new measurements like micro pause delays, herd effects, cold start time etc.
Peco Karayanev
Sr. Product Manager, Riverbed

DBA TAKES ON IT OPS ROLE

The New Job Description: DBAs Take on New Responsibilities. One of the most significant changes we will see in 2018 will be toward a more collaborative relationship between IT infrastructure managers and database administrators (DBAs). As more applications are run in the cloud, senior DBA managers will be able to take more of a central role in troubleshooting problems and improving efficiency. DBAs will be looking beyond just the application and database to find and fix issues. Both IT and application teams will need tools that look deeply into the cloud infrastructure to identify causes of performance and availability issues and provide accurate recommendations for addressing them.
Jerry Melnick
President & CEO, SIOS Technology

DISTRIBUTED ANALYSIS

2018 will see a de-emphasis on centralized cloud-based management and traditional data-lakes, and a shift towards distributed analysis. This will be driven not only by explosive growth in IoT, but also by the support of edge computing by major cloud vendors
Richard Whitehead
Evangelist-in-Chief, Moogsoft

RESTFUL API DRIVES ITOA

The advent of efficient RESTful APIs on many services and applications coupled with the maturation of time-series databases such as OpenTSDB and InfluxDB will drive IT operations analytics to use more quantitative approaches, and lead to advances in root cause analysis. This is due to the high storage efficiency of the time series databases, and the speed with which the optimize-on-write approaches they use can accept data. It is now increasingly practical to track large quantitative data volumes. RESTful API endpoints from applications and cloud services are rich in metrics, and the same types of APIs are efficient at accepting such metrics in data streams. With these large volumes of contemporaneous, high-cardinality time series data sources, operations analysis will become possible at a higher scale than previously possible. Cross-correlation will yield forensic insight into failures. In contrast, predictive time series analysis based on auto-regressive/moving average models, while mathematically practical, will fail to lead to any significantly valuable results on operations data, with rare exceptions.
Thomas Stocking
Director of Sales Engineering, GroundWork

IOT INFLECTION POINT

IoT apps need to get out of the hype cycle and deal with real world pain points. Their devices are still a hassle to set up and they solve too few real world cases. Alexa has shown promise, but IoT platforms as a whole are too fragmented for developers to invest in learning them. Hardware manufacturers will agree on a common set of protocols and open up APIs for devices to work seamlessly.
Abhinav Asthana
CEO, Postman

In my opinion, the biggest occurrence in 2017 was that IoT reached peak hype, giving way to new hype-cycles for machine learning (actually, an offshoot of IoT) and for Artificial Intelligence (a familiar topic area, and one that requires IoT data as fuel for its intelligence). We saw a combination of several companies making very large investments in IoT, while others are scaling back or reorganizing their IoT teams. This combination of investment push and pull means that we're at an inflection point. For IoT, this means we're now at a point where projects have to deliver results. IoT vendors invested ahead of demand, with all sorts of claims of IoT one-stop shopping. With more capacity in the industry than there is demand, I expect we will see players drop off or shift focus.
James Kirkland
Chief Architect, IoT, Red Hat

SELF TEACHING

We've now passed the point where we know that the human brain can no longer cope with the complexity of modern applications. Meanwhile, businesses have never relied on digital services more than they do today. Today, every company is a digital company and every critical IT issue has become a business issue. Therefore, APM solutions must evolve from just being early performance-issue detection tools to providing much more insights into the other phases of the resolution process. This includes not only root cause diagnostic and identification capabilities, but also self-teaching capabilities that leverage big data and AI-based algorithms and require very limited initial configuration to deliver actionable insights and recommended remediation actions. This will allow DevOps teams to make the best possible decisions to resolve performance issues.
Vincent Geffray
Senior Director of Product Marketing, IT Response Automation, Everbridge

Read 2018 Network Performance Management Predictions, the final installment.

Related Links

Hot Topics

The Latest

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...

Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Customers Demand Frictionless Experiences - This Is How Retailers Can Provide Them

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

IT's Motivation Crisis Is Really a Process Crisis

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...