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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IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

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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

Immutable by Design: Reinventing Business Continuity and Disaster Recovery

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

Embracing Cost-Effective Observability Through an OpenTelemetry Approach

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Why Employees Hate Security (And What Businesses Can Do About It)

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

Transforming Network Remediation with a Closed-Loop Approach

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

IT Leaders Are Leveraging AI Agents to Unlock Autonomous Transformation in 2025

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

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In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

5 Steps to Enhancing Network Observability for Your NOC

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Broadcom

Top Cloud Trends for 2025: Wrangling Cloud Spend, Expanding FinOps Teams, and Adopting GenAI

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Data Migration Strategies for Optimizing Cloud Costs

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